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# Quyen
<img src="quyen.webp" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- **Quyen-SE (0.5B)**
- **Quyen-Mini (1.8B)**
- **Quyen (4B)**
- **Quyen-Plus (7B)**
- **Quyen-Pro (14B)**
- **Quyen-Pro-Max (72B)**
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by **Teknium**
- *Capyabara* by **LDJ**
- *distilabel-intel-orca-dpo-pairs* by **argilla**
- *orca_dpo_pairs* by **Intel**
- and Private Data by **Ontocord** & **BEE-spoke-data**
# Prompt Template
- All Quyen models use ChatML as the default template:
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant
```
- You can also use `apply_chat_template`:
```python
messages = [
{"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
{"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to **Tensoic** and **Ontocord** for their generous support with compute and data preparation. | {"language": ["en"], "license": "other", "library_name": "transformers", "datasets": ["teknium/OpenHermes-2.5", "LDJnr/Capybara", "Intel/orca_dpo_pairs", "argilla/distilabel-intel-orca-dpo-pairs"]} | null | vilm/Quyen-Pro-v0.1-GGUF | [
"transformers",
"gguf",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:LDJnr/Capybara",
"dataset:Intel/orca_dpo_pairs",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-06T16:39:52+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-intel-orca-dpo-pairs #license-other #endpoints_compatible #region-us
|
# Quyen
<img src="URL" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- Quyen-SE (0.5B)
- Quyen-Mini (1.8B)
- Quyen (4B)
- Quyen-Plus (7B)
- Quyen-Pro (14B)
- Quyen-Pro-Max (72B)
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by Teknium
- *Capyabara* by LDJ
- *distilabel-intel-orca-dpo-pairs* by argilla
- *orca_dpo_pairs* by Intel
- and Private Data by Ontocord & BEE-spoke-data
# Prompt Template
- All Quyen models use ChatML as the default template:
- You can also use 'apply_chat_template':
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation. | [
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *distilabel-intel-orca-dpo-pairs* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation."
] | [
"TAGS\n#transformers #gguf #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-intel-orca-dpo-pairs #license-other #endpoints_compatible #region-us \n",
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *distilabel-intel-orca-dpo-pairs* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation."
] | [
86,
27,
167,
33,
18,
31
] | [
"passage: TAGS\n#transformers #gguf #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-intel-orca-dpo-pairs #license-other #endpoints_compatible #region-us \n# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\"># Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *distilabel-intel-orca-dpo-pairs* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation."
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null | null | null |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | PranavInvenics/phi2_v2 | [
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"region:us"
] | 2024-02-06T16:44:22+00:00 | [] | [] | TAGS
#safetensors #autotrain #text-generation #conversational #license-other #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
29,
29,
3
] | [
"passage: TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
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null | null | transformers | I recently ran into this [quantized model of Noromaid](https://huggingface.co/rAIfle/NoroMaid-v0.4-Mixtral-Instruct-8x7b-Zloss-exl2-rpcal) with the "rpcal" term on the end.</br>
The secret sauce used an [RP data set](https://huggingface.co/datasets/royallab/PIPPA-cleaned) instead of the standard llama dataset to do the quantizing.</br>
On a test drive, it seemed to make an enormous difference in the quality of the output, so I'm trying to quantify if it does make a significant difference.</br>
I plan to run a variety of things like [The Sarah Test](https://rentry.org/thesarahtest) to see if I can see any objective differences.</br>
My favorite model for the past few weeks has been really enjoyable, so I want to see how it compares to this one.</br>
Favorite: [zaq-hack/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-bpw300-h6-exl2](https://huggingface.co/zaq-hack/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-bpw300-h6-exl2)</br>
This model: EXL2 @ 3.0 bpw using the RP samples instead of standard calibration.
I'm just tinkering. All credit to the original creators: Noromaid is hot.
---
# Disclaimer:
## This model is experimental, do not expect everything to work.
This model uses the Chatml **prompting format**
---
Beeg noromaid on ***steroids***. Suitable for RP, ERP.
This model was trained on the Zloss fork of Charles, and should fix issue the model had.
Use Chatml prompt format, but not the special token.
The reason is that Axolotl merge the finetune with the base model at 1.0 weight basically, but this is too much, so I use another script available [HERE](https://github.com/DocShotgun/LLM-notebooks/blob/main/weighted-lora-merge.ipynb) to merge with less weight, sadly, it don't take the special Chatml token. It's like Orca2 for the matter.
## Credits:
- Undi
- IkariDev
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains FP16 files of Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
### Prompt format: Chatml
```
<|im_start|>system
{sysprompt}<|im_end|>
<|im_start|>user
{input}<|im_end|>
<|im_start|>assistant
{output}<|im_end|>
```
## Datasets used:
- Aesir 1, 2 & 3 modified by us, credit to ([MinervaAI](https://huggingface.co/MinervaAI) / [Gryphe](https://huggingface.co/Gryphe))
- [LimaRP-20231109](https://huggingface.co/datasets/lemonilia/LimaRP) ([Lemonilia](https://huggingface.co/lemonilia))
- [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ([NobodyExistsOnTheInternet](https://huggingface.co/NobodyExistsOnTheInternet)
- [No-robots-ShareGPT](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) ([Doctor-Shotgun](https://huggingface.co/Doctor-Shotgun))
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek | {"license": "cc-by-nc-4.0"} | text-generation | zaq-hack/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-bpw300-h6-exl2-rpcal | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T16:45:20+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| I recently ran into this quantized model of Noromaid with the "rpcal" term on the end.</br>
The secret sauce used an RP data set instead of the standard llama dataset to do the quantizing.</br>
On a test drive, it seemed to make an enormous difference in the quality of the output, so I'm trying to quantify if it does make a significant difference.</br>
I plan to run a variety of things like The Sarah Test to see if I can see any objective differences.</br>
My favorite model for the past few weeks has been really enjoyable, so I want to see how it compares to this one.</br>
Favorite: zaq-hack/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-bpw300-h6-exl2</br>
This model: EXL2 @ 3.0 bpw using the RP samples instead of standard calibration.
I'm just tinkering. All credit to the original creators: Noromaid is hot.
!image/png
---
# Disclaimer:
## This model is experimental, do not expect everything to work.
This model uses the Chatml prompting format
---
Beeg noromaid on *steroids*. Suitable for RP, ERP.
This model was trained on the Zloss fork of Charles, and should fix issue the model had.
Use Chatml prompt format, but not the special token.
The reason is that Axolotl merge the finetune with the base model at 1.0 weight basically, but this is too much, so I use another script available HERE to merge with less weight, sadly, it don't take the special Chatml token. It's like Orca2 for the matter.
## Credits:
- Undi
- IkariDev
## Description
This repo contains FP16 files of Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss.
FP16 - by IkariDev and Undi
GGUF - by IkariDev and Undi
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
### Prompt format: Chatml
## Datasets used:
- Aesir 1, 2 & 3 modified by us, credit to (MinervaAI / Gryphe)
- LimaRP-20231109 (Lemonilia)
- ToxicQAFinal (NobodyExistsOnTheInternet
- No-robots-ShareGPT (Doctor-Shotgun)
## Others
Undi: If you want to support me, you can here.
IkariDev: Visit my retro/neocities style website please kek | [
"# Disclaimer:",
"## This model is experimental, do not expect everything to work.\n\nThis model uses the Chatml prompting format\n\n---\n\n\nBeeg noromaid on *steroids*. Suitable for RP, ERP.\n\nThis model was trained on the Zloss fork of Charles, and should fix issue the model had.\n\nUse Chatml prompt format, but not the special token.\n\nThe reason is that Axolotl merge the finetune with the base model at 1.0 weight basically, but this is too much, so I use another script available HERE to merge with less weight, sadly, it don't take the special Chatml token. It's like Orca2 for the matter.",
"## Credits:\n- Undi\n- IkariDev",
"## Description\n\n\n\nThis repo contains FP16 files of Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss.\n\nFP16 - by IkariDev and Undi\n\n\n\n\n\n\n\n\n\n\n\nGGUF - by IkariDev and Undi",
"## Ratings:\n\nNote: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!\n\nNo ratings yet!\n\nIf you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is \"ikaridev\" and \"undi\".",
"### Prompt format: Chatml",
"## Datasets used:\n\n- Aesir 1, 2 & 3 modified by us, credit to (MinervaAI / Gryphe)\n- LimaRP-20231109 (Lemonilia)\n- ToxicQAFinal (NobodyExistsOnTheInternet\n- No-robots-ShareGPT (Doctor-Shotgun)",
"## Others\n\nUndi: If you want to support me, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Disclaimer:",
"## This model is experimental, do not expect everything to work.\n\nThis model uses the Chatml prompting format\n\n---\n\n\nBeeg noromaid on *steroids*. Suitable for RP, ERP.\n\nThis model was trained on the Zloss fork of Charles, and should fix issue the model had.\n\nUse Chatml prompt format, but not the special token.\n\nThe reason is that Axolotl merge the finetune with the base model at 1.0 weight basically, but this is too much, so I use another script available HERE to merge with less weight, sadly, it don't take the special Chatml token. It's like Orca2 for the matter.",
"## Credits:\n- Undi\n- IkariDev",
"## Description\n\n\n\nThis repo contains FP16 files of Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss.\n\nFP16 - by IkariDev and Undi\n\n\n\n\n\n\n\n\n\n\n\nGGUF - by IkariDev and Undi",
"## Ratings:\n\nNote: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!\n\nNo ratings yet!\n\nIf you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is \"ikaridev\" and \"undi\".",
"### Prompt format: Chatml",
"## Datasets used:\n\n- Aesir 1, 2 & 3 modified by us, credit to (MinervaAI / Gryphe)\n- LimaRP-20231109 (Lemonilia)\n- ToxicQAFinal (NobodyExistsOnTheInternet\n- No-robots-ShareGPT (Doctor-Shotgun)",
"## Others\n\nUndi: If you want to support me, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek"
] | [
62,
3,
145,
11,
55,
78,
9,
75,
32
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Disclaimer:## This model is experimental, do not expect everything to work.\n\nThis model uses the Chatml prompting format\n\n---\n\n\nBeeg noromaid on *steroids*. Suitable for RP, ERP.\n\nThis model was trained on the Zloss fork of Charles, and should fix issue the model had.\n\nUse Chatml prompt format, but not the special token.\n\nThe reason is that Axolotl merge the finetune with the base model at 1.0 weight basically, but this is too much, so I use another script available HERE to merge with less weight, sadly, it don't take the special Chatml token. It's like Orca2 for the matter.## Credits:\n- Undi\n- IkariDev## Description\n\n\n\nThis repo contains FP16 files of Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss.\n\nFP16 - by IkariDev and Undi\n\n\n\n\n\n\n\n\n\n\n\nGGUF - by IkariDev and Undi## Ratings:\n\nNote: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!\n\nNo ratings yet!\n\nIf you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is \"ikaridev\" and \"undi\".### Prompt format: Chatml## Datasets used:\n\n- Aesir 1, 2 & 3 modified by us, credit to (MinervaAI / Gryphe)\n- LimaRP-20231109 (Lemonilia)\n- ToxicQAFinal (NobodyExistsOnTheInternet\n- No-robots-ShareGPT (Doctor-Shotgun)## Others\n\nUndi: If you want to support me, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek"
] | [
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sft_zephyr
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "HuggingFaceH4/zephyr-7b-alpha", "model-index": [{"name": "sft_zephyr", "results": []}]} | null | Shel2679/sft_zephyr | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-alpha",
"license:mit",
"region:us"
] | 2024-02-06T16:46:28+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-alpha #license-mit #region-us
|
# sft_zephyr
This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-alpha on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# sft_zephyr\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-alpha on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-alpha #license-mit #region-us \n",
"# sft_zephyr\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-alpha on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-alpha #license-mit #region-us \n# sft_zephyr\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-alpha on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- num_epochs: 5### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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] |
null | null | espnet |
# reazonspeech-espnet-v1
`reazonspeech-espnet-v1` es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo.
| {"language": ["es"], "license": "apache-2.0", "library_name": "espnet", "tags": ["automatic-speech-recognition", "speech", "espnet", "spanish"]} | automatic-speech-recognition | Dallyana/espnet_asr_model2 | [
"espnet",
"automatic-speech-recognition",
"speech",
"spanish",
"es",
"license:apache-2.0",
"region:us"
] | 2024-02-06T16:47:07+00:00 | [] | [
"es"
] | TAGS
#espnet #automatic-speech-recognition #speech #spanish #es #license-apache-2.0 #region-us
|
# reazonspeech-espnet-v1
'reazonspeech-espnet-v1' es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo.
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"# reazonspeech-espnet-v1\n\n'reazonspeech-espnet-v1' es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo."
] | [
"TAGS\n#espnet #automatic-speech-recognition #speech #spanish #es #license-apache-2.0 #region-us \n",
"# reazonspeech-espnet-v1\n\n'reazonspeech-espnet-v1' es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo."
] | [
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"passage: TAGS\n#espnet #automatic-speech-recognition #speech #spanish #es #license-apache-2.0 #region-us \n# reazonspeech-espnet-v1\n\n'reazonspeech-espnet-v1' es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo."
] | [
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null | null | null | ## Origin model:
deepseek-ai/deepseek-math-7b-rl
## Prompt Template:
```
User: {prompt}
Assistant:
``` | {"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL"} | null | tastypear/deepseek-ai-deepseek-math-7b-rl-GGUF | [
"gguf",
"license:other",
"region:us"
] | 2024-02-06T16:47:08+00:00 | [] | [] | TAGS
#gguf #license-other #region-us
| ## Origin model:
deepseek-ai/deepseek-math-7b-rl
## Prompt Template:
| [
"## Origin model:\n\ndeepseek-ai/deepseek-math-7b-rl",
"## Prompt Template:"
] | [
"TAGS\n#gguf #license-other #region-us \n",
"## Origin model:\n\ndeepseek-ai/deepseek-math-7b-rl",
"## Prompt Template:"
] | [
14,
19,
6
] | [
"passage: TAGS\n#gguf #license-other #region-us \n## Origin model:\n\ndeepseek-ai/deepseek-math-7b-rl## Prompt Template:"
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null | null | transformers |
# Quyen
<img src="quyen.webp" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- **Quyen-SE (0.5B)**
- **Quyen-Mini (1.8B)**
- **Quyen (4B)**
- **Quyen-Plus (7B)**
- **Quyen-Pro (14B)**
- **Quyen-Pro-Max (72B)**
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by **Teknium**
- *Capyabara* by **LDJ**
- *argilla/distilabel-capybara-dpo-7k-binarized* by **argilla**
- *orca_dpo_pairs* by **Intel**
- and Private Data by **Ontocord** & **BEE-spoke-data**
# Prompt Template
- All Quyen models use ChatML as the default template:
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant
```
- You can also use `apply_chat_template`:
```python
messages = [
{"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
{"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to **Tensoic** and **Ontocord** for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes. | {"language": ["en"], "license": "other", "library_name": "transformers", "datasets": ["teknium/OpenHermes-2.5", "LDJnr/Capybara", "Intel/orca_dpo_pairs", "argilla/distilabel-capybara-dpo-7k-binarized"], "pipeline_tag": "text-generation"} | text-generation | LoneStriker/Quyen-Plus-v0.1-AWQ | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:LDJnr/Capybara",
"dataset:Intel/orca_dpo_pairs",
"dataset:argilla/distilabel-capybara-dpo-7k-binarized",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | 2024-02-06T16:47:29+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #4-bit #region-us
|
# Quyen
<img src="URL" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- Quyen-SE (0.5B)
- Quyen-Mini (1.8B)
- Quyen (4B)
- Quyen-Plus (7B)
- Quyen-Pro (14B)
- Quyen-Pro-Max (72B)
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by Teknium
- *Capyabara* by LDJ
- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla
- *orca_dpo_pairs* by Intel
- and Private Data by Ontocord & BEE-spoke-data
# Prompt Template
- All Quyen models use ChatML as the default template:
- You can also use 'apply_chat_template':
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes. | [
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
"TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #4-bit #region-us \n",
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
113,
27,
171,
33,
18,
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] | [
"passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #4-bit #region-us \n# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\"># Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "259.58 +/- 18.21", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | frahman/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-06T16:49:08+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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] |
null | null | transformers | # CausalLM 34B β
## PROMPT FORMAT:
[chatml](https://github.com/openai/openai-python/blob/main/chatml.md)
There are some issues with the model weights in terms of precision. In the next version update, we will roll back some progress and retrain to fix these issues as soon as possible.
**Please note:** Do not use "accelerated inference frameworks" like **VLLM** temporarily. Instead, use Transformers for inference. Otherwise, due to precision issues, the output quality will be significantly degraded. If you need faster inference, you can consider using the q8_0 quantization (faster and better than bf16 vllm for this model only) with llama.cpp temporarily or wait for the official version.
To be fixed in the upcoming next version update.
**no repetition_penalty!**
Please do not use wikitext for quantization calibration because all wikitext have been re-aligned on synthetic dataset, and its distribution differs significantly from the original wikitext.
## MT-Bench: 8.5
## Some contamination detection if you want to check:
| Models | MMLU (ref: llama7b) | TBA |
| ------------------------- | ------------------- | ---- |
| microsoft/Orca-2-7b | 0.77 | |
| mistralai/Mistral-7B-v0.1 | 0.46 | |
| **CausalLM/34b-beta** | **0.38** | |
| 01-ai/Yi-6B-200K | 0.3 | |
data from https://huggingface.co/spaces/Yeyito/llm_contamination_detector
It should be *safe*. It was not trained on the benchmark, but the contamination of the training dataset is unavoidable due to cost constraints. | {"license": "gpl-3.0"} | text-generation | CausalLM/34b-beta | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T16:50:45+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| CausalLM 34B β
==============
PROMPT FORMAT:
--------------
chatml
There are some issues with the model weights in terms of precision. In the next version update, we will roll back some progress and retrain to fix these issues as soon as possible.
Please note: Do not use "accelerated inference frameworks" like VLLM temporarily. Instead, use Transformers for inference. Otherwise, due to precision issues, the output quality will be significantly degraded. If you need faster inference, you can consider using the q8\_0 quantization (faster and better than bf16 vllm for this model only) with URL temporarily or wait for the official version.
To be fixed in the upcoming next version update.
no repetition\_penalty!
Please do not use wikitext for quantization calibration because all wikitext have been re-aligned on synthetic dataset, and its distribution differs significantly from the original wikitext.
MT-Bench: 8.5
-------------
!mt-bench
Some contamination detection if you want to check:
--------------------------------------------------
Models: microsoft/Orca-2-7b, MMLU (ref: llama7b): 0.77, TBA:
Models: mistralai/Mistral-7B-v0.1, MMLU (ref: llama7b): 0.46, TBA:
Models: CausalLM/34b-beta, MMLU (ref: llama7b): 0.38, TBA:
Models: 01-ai/Yi-6B-200K, MMLU (ref: llama7b): 0.3, TBA:
data from URL
It should be *safe*. It was not trained on the benchmark, but the contamination of the training dataset is unavoidable due to cost constraints.
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
59
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers | dataset: chathuranga-jayanath/selfapr-manipulation-bug-error-context-all | {} | text2text-generation | chathuranga-jayanath/codet5-small-v20 | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T16:55:07+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| dataset: chathuranga-jayanath/selfapr-manipulation-bug-error-context-all | [] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
53
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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# Model Card for Model ID
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## Uses
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## Bias, Risks, and Limitations
### Recommendations
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Use the code below to get started with the model.
## Training Details
### Training Data
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#### Preprocessing [optional]
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## Evaluation
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# seo-headline_2
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5682
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8031 | 1.29 | 500 | 0.7142 |
| 0.6117 | 2.58 | 1000 | 0.5948 |
| 0.5568 | 3.86 | 1500 | 0.5755 |
| 0.5219 | 5.15 | 2000 | 0.5682 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "google/pegasus-cnn_dailymail", "model-index": [{"name": "seo-headline_2", "results": []}]} | text2text-generation | valurank/seo-headline | [
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T16:58:52+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
| seo-headline\_2
===============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5682
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 6
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 6",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 6",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
68,
144,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 6### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
# Quyen
<img src="quyen.webp" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- **Quyen-SE (0.5B)**
- **Quyen-Mini (1.8B)**
- **Quyen (4B)**
- **Quyen-Plus (7B)**
- **Quyen-Pro (14B)**
- **Quyen-Pro-Max (72B)**
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by **Teknium**
- *Capyabara* by **LDJ**
- *argilla/distilabel-capybara-dpo-7k-binarized* by **argilla**
- *orca_dpo_pairs* by **Intel**
- and Private Data by **Ontocord** & **BEE-spoke-data**
# Prompt Template
- All Quyen models use ChatML as the default template:
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant
```
- You can also use `apply_chat_template`:
```python
messages = [
{"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
{"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to **Tensoic** and **Ontocord** for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes. | {"language": ["en"], "license": "other", "library_name": "transformers", "datasets": ["teknium/OpenHermes-2.5", "LDJnr/Capybara", "Intel/orca_dpo_pairs", "argilla/distilabel-capybara-dpo-7k-binarized"], "pipeline_tag": "text-generation"} | text-generation | LoneStriker/Quyen-Plus-v0.1-GPTQ | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:LDJnr/Capybara",
"dataset:Intel/orca_dpo_pairs",
"dataset:argilla/distilabel-capybara-dpo-7k-binarized",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T16:59:40+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# Quyen
<img src="URL" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- Quyen-SE (0.5B)
- Quyen-Mini (1.8B)
- Quyen (4B)
- Quyen-Plus (7B)
- Quyen-Pro (14B)
- Quyen-Pro-Max (72B)
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by Teknium
- *Capyabara* by LDJ
- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla
- *orca_dpo_pairs* by Intel
- and Private Data by Ontocord & BEE-spoke-data
# Prompt Template
- All Quyen models use ChatML as the default template:
- You can also use 'apply_chat_template':
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes. | [
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
"TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
110,
27,
171,
33,
18,
54
] | [
"passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #region-us \n# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\"># Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
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null | null | metavoice |
MetaVoice-1B is a 1.2B parameter base model trained on 100K hours of speech for TTS (text-to-speech). It has been built with the following priorities:
* Emotional speech rhythm and tone in English. No hallucinations.
* Support for voice cloning with finetuning.
* We have had success with as little as 1 minute training data for Indian speakers.
* Zero-shot cloning for American & British voices, with 30s reference audio.
* Support for long-form synthesis.
We’re releasing MetaVoice-1B under the Apache 2.0 license, *it can be used without restrictions*.
## Usage
See [Github](https://github.com/metavoiceio/metavoice-src) for the latest usage instructions.
## Soon
- Long form TTS
- Fine-tuning code
## Architecture
We predict EnCodec tokens from text, and speaker information. This is then diffused up to the waveform level, with post-processing applied to clean up the audio.
* We use a causal GPT to predict the first two hierarchies of EnCodec tokens. Text and audio are part of the LLM context. Speaker information is passed via conditioning at the token embedding layer. This speaker conditioning is obtained from a separately trained speaker verification network.
- The two hierarchies are predicted in a "flattened interleaved" manner, we predict the first token of the first hierarchy, then the first token of the second hierarchy, then the second token of the first hierarchy, and so on.
- We use condition-free sampling to boost the cloning capability of the model.
- The text is tokenised using a custom trained BPE tokeniser with 512 tokens.
- Note that we've skipped predicting semantic tokens as done in other works, as we found that this isn't strictly necessary.
* We use a non-causal (encoder-style) transformer to predict the rest of the 6 hierarchies from the first two hierarchies. This is a super small model (~10Mn parameters), and has extensive zero-shot generalisation to most speakers we've tried. Since it's non-causal, we're also able to predict all the timesteps in parallel.
* We use multi-band diffusion to generate waveforms from the EnCodec tokens. We noticed that the speech is clearer than using the original RVQ decoder or VOCOS. However, the diffusion at waveform level leaves some background artifacts which are quite unpleasant to the ear. We clean this up in the next step.
* We use DeepFilterNet to clear up the artifacts introduced by the multi-band diffusion.
## Optimizations
The model supports:
1. KV-caching via Flash Decoding
2. Batching (including texts of different lengths)
| {"language": ["en"], "license": "apache-2.0", "library_name": "metavoice", "tags": ["pretrained", "text-to-speech"], "inference": false} | text-to-speech | metavoiceio/metavoice-1B-v0.1 | [
"metavoice",
"pretrained",
"text-to-speech",
"en",
"license:apache-2.0",
"has_space",
"region:us"
] | 2024-02-06T17:02:48+00:00 | [] | [
"en"
] | TAGS
#metavoice #pretrained #text-to-speech #en #license-apache-2.0 #has_space #region-us
|
MetaVoice-1B is a 1.2B parameter base model trained on 100K hours of speech for TTS (text-to-speech). It has been built with the following priorities:
* Emotional speech rhythm and tone in English. No hallucinations.
* Support for voice cloning with finetuning.
* We have had success with as little as 1 minute training data for Indian speakers.
* Zero-shot cloning for American & British voices, with 30s reference audio.
* Support for long-form synthesis.
We’re releasing MetaVoice-1B under the Apache 2.0 license, *it can be used without restrictions*.
## Usage
See Github for the latest usage instructions.
## Soon
- Long form TTS
- Fine-tuning code
## Architecture
We predict EnCodec tokens from text, and speaker information. This is then diffused up to the waveform level, with post-processing applied to clean up the audio.
* We use a causal GPT to predict the first two hierarchies of EnCodec tokens. Text and audio are part of the LLM context. Speaker information is passed via conditioning at the token embedding layer. This speaker conditioning is obtained from a separately trained speaker verification network.
- The two hierarchies are predicted in a "flattened interleaved" manner, we predict the first token of the first hierarchy, then the first token of the second hierarchy, then the second token of the first hierarchy, and so on.
- We use condition-free sampling to boost the cloning capability of the model.
- The text is tokenised using a custom trained BPE tokeniser with 512 tokens.
- Note that we've skipped predicting semantic tokens as done in other works, as we found that this isn't strictly necessary.
* We use a non-causal (encoder-style) transformer to predict the rest of the 6 hierarchies from the first two hierarchies. This is a super small model (~10Mn parameters), and has extensive zero-shot generalisation to most speakers we've tried. Since it's non-causal, we're also able to predict all the timesteps in parallel.
* We use multi-band diffusion to generate waveforms from the EnCodec tokens. We noticed that the speech is clearer than using the original RVQ decoder or VOCOS. However, the diffusion at waveform level leaves some background artifacts which are quite unpleasant to the ear. We clean this up in the next step.
* We use DeepFilterNet to clear up the artifacts introduced by the multi-band diffusion.
## Optimizations
The model supports:
1. KV-caching via Flash Decoding
2. Batching (including texts of different lengths)
| [
"## Usage\nSee Github for the latest usage instructions.",
"## Soon\n- Long form TTS\n- Fine-tuning code",
"## Architecture\nWe predict EnCodec tokens from text, and speaker information. This is then diffused up to the waveform level, with post-processing applied to clean up the audio.\n\n* We use a causal GPT to predict the first two hierarchies of EnCodec tokens. Text and audio are part of the LLM context. Speaker information is passed via conditioning at the token embedding layer. This speaker conditioning is obtained from a separately trained speaker verification network.\n - The two hierarchies are predicted in a \"flattened interleaved\" manner, we predict the first token of the first hierarchy, then the first token of the second hierarchy, then the second token of the first hierarchy, and so on.\n - We use condition-free sampling to boost the cloning capability of the model.\n - The text is tokenised using a custom trained BPE tokeniser with 512 tokens.\n - Note that we've skipped predicting semantic tokens as done in other works, as we found that this isn't strictly necessary.\n* We use a non-causal (encoder-style) transformer to predict the rest of the 6 hierarchies from the first two hierarchies. This is a super small model (~10Mn parameters), and has extensive zero-shot generalisation to most speakers we've tried. Since it's non-causal, we're also able to predict all the timesteps in parallel.\n* We use multi-band diffusion to generate waveforms from the EnCodec tokens. We noticed that the speech is clearer than using the original RVQ decoder or VOCOS. However, the diffusion at waveform level leaves some background artifacts which are quite unpleasant to the ear. We clean this up in the next step.\n* We use DeepFilterNet to clear up the artifacts introduced by the multi-band diffusion.",
"## Optimizations\nThe model supports: \n1. KV-caching via Flash Decoding \n2. Batching (including texts of different lengths)"
] | [
"TAGS\n#metavoice #pretrained #text-to-speech #en #license-apache-2.0 #has_space #region-us \n",
"## Usage\nSee Github for the latest usage instructions.",
"## Soon\n- Long form TTS\n- Fine-tuning code",
"## Architecture\nWe predict EnCodec tokens from text, and speaker information. This is then diffused up to the waveform level, with post-processing applied to clean up the audio.\n\n* We use a causal GPT to predict the first two hierarchies of EnCodec tokens. Text and audio are part of the LLM context. Speaker information is passed via conditioning at the token embedding layer. This speaker conditioning is obtained from a separately trained speaker verification network.\n - The two hierarchies are predicted in a \"flattened interleaved\" manner, we predict the first token of the first hierarchy, then the first token of the second hierarchy, then the second token of the first hierarchy, and so on.\n - We use condition-free sampling to boost the cloning capability of the model.\n - The text is tokenised using a custom trained BPE tokeniser with 512 tokens.\n - Note that we've skipped predicting semantic tokens as done in other works, as we found that this isn't strictly necessary.\n* We use a non-causal (encoder-style) transformer to predict the rest of the 6 hierarchies from the first two hierarchies. This is a super small model (~10Mn parameters), and has extensive zero-shot generalisation to most speakers we've tried. Since it's non-causal, we're also able to predict all the timesteps in parallel.\n* We use multi-band diffusion to generate waveforms from the EnCodec tokens. We noticed that the speech is clearer than using the original RVQ decoder or VOCOS. However, the diffusion at waveform level leaves some background artifacts which are quite unpleasant to the ear. We clean this up in the next step.\n* We use DeepFilterNet to clear up the artifacts introduced by the multi-band diffusion.",
"## Optimizations\nThe model supports: \n1. KV-caching via Flash Decoding \n2. Batching (including texts of different lengths)"
] | [
35,
13,
14,
445,
31
] | [
"passage: TAGS\n#metavoice #pretrained #text-to-speech #en #license-apache-2.0 #has_space #region-us \n## Usage\nSee Github for the latest usage instructions.## Soon\n- Long form TTS\n- Fine-tuning code## Architecture\nWe predict EnCodec tokens from text, and speaker information. This is then diffused up to the waveform level, with post-processing applied to clean up the audio.\n\n* We use a causal GPT to predict the first two hierarchies of EnCodec tokens. Text and audio are part of the LLM context. Speaker information is passed via conditioning at the token embedding layer. This speaker conditioning is obtained from a separately trained speaker verification network.\n - The two hierarchies are predicted in a \"flattened interleaved\" manner, we predict the first token of the first hierarchy, then the first token of the second hierarchy, then the second token of the first hierarchy, and so on.\n - We use condition-free sampling to boost the cloning capability of the model.\n - The text is tokenised using a custom trained BPE tokeniser with 512 tokens.\n - Note that we've skipped predicting semantic tokens as done in other works, as we found that this isn't strictly necessary.\n* We use a non-causal (encoder-style) transformer to predict the rest of the 6 hierarchies from the first two hierarchies. This is a super small model (~10Mn parameters), and has extensive zero-shot generalisation to most speakers we've tried. Since it's non-causal, we're also able to predict all the timesteps in parallel.\n* We use multi-band diffusion to generate waveforms from the EnCodec tokens. We noticed that the speech is clearer than using the original RVQ decoder or VOCOS. However, the diffusion at waveform level leaves some background artifacts which are quite unpleasant to the ear. We clean this up in the next step.\n* We use DeepFilterNet to clear up the artifacts introduced by the multi-band diffusion."
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null | null | transformers |
This model is for the module
# Initial Knowledge Assessment Test Generation
## Steps
- Data was gathered by:
- Downloading youtube playlists for each course from every category
- The videos were transcribed
- The text was fed to chatgpt via API, to formulate prompts n reponse pairs.
- 2.78 Billion parameter Phi2 model by [Microsoft](https://huggingface.co/microsoft/phi-2) was finetuned on the curated data.
## How to use the model?
### Note the format of the prompt. Only change the text in the variable "paragraph". This is the text which acts as the context for the generated test./
```
# Use a huggingafce pipeline as a high-level helper
from transformers import pipeline
import torch
pipe = pipeline("text-generation",
model="SalehAhmad/Initial_Knowledge_Assessment_Test-Model-Phi2_3Epochs",
device_map='auto',
torch_dtype=torch.bfloat16,
max_new_tokens=1024)
paragraph = '''Computer science theories and basic programming principles form the foundation of the ever-evolving field of technology. At its core, computer science is not just about writing code but involves the exploration and application of fundamental principles that underpin the design and functioning of computers. One key theory in computer science is the Turing Machine, proposed by Alan Turing in the 1930s. This theoretical construct laid the groundwork for understanding the limits and possibilities of computation. The idea that any computable function could be computed by a Turing Machine provided a theoretical framework for the development of modern computers.
Another essential theory in computer science is the concept of algorithms. Algorithms are step-by-step procedures or formulas for solving problems and performing tasks. They are crucial in programming as they guide the computer in executing tasks efficiently. The study of algorithms involves analyzing their efficiency and correctness, and it plays a pivotal role in designing software that can handle large datasets and complex computations. Moreover, algorithms are closely related to data structures, which are the ways in which data is organized and stored in a computer's memory. Efficient data structures are essential for optimizing the performance of algorithms.'''
prompt = f'''Instruct: You are a chatbot, who is helping to curate datasets. Based on the input paragraph as context generate as many mcq question as possible without repeptition. You donot generate repetitive questions.
When you are given a paragraph for context. You will generate multiple mcq questions, it's 4 options and it's actual answer.
For Example:
Paragraph: .....
-Start of Question-
Question: ......
Options:
a) .....
b) .....
c) .....
d) .....
Actual Answer: b)....
-End of Question-
-Start of Question-
Question: ......
Options:
a) .....
b) .....
c) .....
d) .....
Actual Answer: d)....
-End of Question-
and so on.
Paragraph: {paragraph}
Output: '''
output = pipe(prompt,
num_return_sequences=1,
return_full_text=False)
print(output[0]['generated_text'])
``` | {"language": ["en"], "library_name": "transformers", "pipeline_tag": "text-generation", "widget": [{"text": "Instruct: You are a chatbot, who is helping to curate datasets. Based on the input paragraph as context generate as many mcq question as possible without repeptition. You donot generate repetitive questions.\nWhen you are given a paragraph for context. You will generate multiple mcq questions, it's 4 options and it's actual answer.\nFor Example:\nParagraph: .....\n-Start of Question-\nQuestion: ......\nOptions: \na) .....\nb) .....\nc) .....\nd) .....\nActual Answer: b)....\n-End of Question-\n-Start of Question-\nQuestion: ......\nOptions: \na) .....\nb) .....\nc) .....\nd) .....\nActual Answer: d)....\n-End of Question-\nand so on.\nParagraph: Computer science theories and basic programming principles form the foundation of the ever-evolving field of technology. At its core, computer science is not just about writing code but involves the exploration and application of fundamental principles that underpin the design and functioning of computers. One key theory in computer science is the Turing Machine, proposed by Alan Turing in the 1930s. This theoretical construct laid the groundwork for understanding the limits and possibilities of computation. The idea that any computable function could be computed by a Turing Machine provided a theoretical framework for the development of modern computers. Another essential theory in computer science is the concept of algorithms. Algorithms are step-by-step procedures or formulas for solving problems and performing tasks. They are crucial in programming as they guide the computer in executing tasks efficiently. The study of algorithms involves analyzing their efficiency and correctness, and it plays a pivotal role in designing software that can handle large datasets and complex computations. Moreover, algorithms are closely related to data structures, which are the ways in which data is organized and stored in a computer's memory. Efficient data structures are essential for optimizing the performance of algorithms.\nOutput: ", "example_title": "Example 1"}, {"text": "Instruct: You are a chatbot, who is helping to curate datasets. Based on the input paragraph as context generate as many mcq question as possible without repeptition. You donot generate repetitive questions.\nWhen you are given a paragraph for context. You will generate multiple mcq questions, it's 4 options and it's actual answer.\nFor Example:\nParagraph: .....\n-Start of Question-\nQuestion: ......\nOptions: \na) .....\nb) .....\nc) .....\nd) .....\nActual Answer: b)....\n-End of Question-\n-Start of Question-\nQuestion: ......\nOptions: \na) .....\nb) .....\nc) .....\nd) .....\nActual Answer: d)....\n-End of Question-\nand so on.\nParagraph: Business financial education is an essential aspect of any successful enterprise. It encompasses a range of knowledge and skills necessary for effectively managing the financial aspects of a business, including budgeting, financial analysis, investment strategies, and risk management. A solid understanding of financial concepts enables business owners and managers to make informed decisions that drive profitability and sustainability. It empowers individuals within organizations to interpret financial statements, assess performance metrics, and identify opportunities for growth and improvement. Moreover, financial education fosters accountability and transparency, ensuring that stakeholders have a clear understanding of the financial health and trajectory of the business. By investing in financial education, businesses can mitigate risks, optimize resources, and ultimately achieve their long-term objectives.\nOutput: ", "example_title": "Example 2"}, {"text": "Instruct: You are a chatbot, who is helping to curate datasets. Based on the input paragraph as context generate as many mcq question as possible without repeptition. You donot generate repetitive questions.\nWhen you are given a paragraph for context. You will generate multiple mcq questions, it's 4 options and it's actual answer.\nFor Example:\nParagraph: .....\n-Start of Question-\nQuestion: ......\nOptions: \na) .....\nb) .....\nc) .....\nd) .....\nActual Answer: b)....\n-End of Question-\n-Start of Question-\nQuestion: ......\nOptions: \na) .....\nb) .....\nc) .....\nd) .....\nActual Answer: d)....\n-End of Question-\nand so on.\nParagraph: LLMs, or Language Model Models, are advanced artificial intelligence systems designed to process and generate human-like text based on input prompts. LLMs leverage sophisticated algorithms and vast datasets to understand and generate coherent language across a wide range of topics and contexts. Businesses and individuals can benefit from LLMs in various ways, including content creation, customer support, language translation, and data analysis. By leveraging LLMs, businesses can automate repetitive tasks, streamline workflows, and improve efficiency. Moreover, LLMs can assist in generating personalized content, enhancing customer engagement, and driving conversions. To maximize the benefits of LLMs, it's essential to understand their capabilities and limitations, as well as best practices for integrating them into existing workflows. Additionally, staying updated on advancements in LLM technology and investing in ongoing training and development can ensure that businesses harness the full potential of these powerful tools to achieve their objectives.\nOutput: ", "example_title": "Example 3"}]} | text-generation | SalehAhmad/Initial_Knowledge_Assessment_Test-Model-Phi2_3Epochs | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:03:05+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #phi #text-generation #custom_code #en #autotrain_compatible #endpoints_compatible #region-us
|
This model is for the module
# Initial Knowledge Assessment Test Generation
## Steps
- Data was gathered by:
- Downloading youtube playlists for each course from every category
- The videos were transcribed
- The text was fed to chatgpt via API, to formulate prompts n reponse pairs.
- 2.78 Billion parameter Phi2 model by Microsoft was finetuned on the curated data.
## How to use the model?
### Note the format of the prompt. Only change the text in the variable "paragraph". This is the text which acts as the context for the generated test./
| [
"# Initial Knowledge Assessment Test Generation",
"## Steps\n- Data was gathered by:\n - Downloading youtube playlists for each course from every category\n - The videos were transcribed\n - The text was fed to chatgpt via API, to formulate prompts n reponse pairs. \n- 2.78 Billion parameter Phi2 model by Microsoft was finetuned on the curated data.",
"## How to use the model?",
"### Note the format of the prompt. Only change the text in the variable \"paragraph\". This is the text which acts as the context for the generated test./"
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #custom_code #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# Initial Knowledge Assessment Test Generation",
"## Steps\n- Data was gathered by:\n - Downloading youtube playlists for each course from every category\n - The videos were transcribed\n - The text was fed to chatgpt via API, to formulate prompts n reponse pairs. \n- 2.78 Billion parameter Phi2 model by Microsoft was finetuned on the curated data.",
"## How to use the model?",
"### Note the format of the prompt. Only change the text in the variable \"paragraph\". This is the text which acts as the context for the generated test./"
] | [
44,
8,
74,
7,
37
] | [
"passage: TAGS\n#transformers #safetensors #phi #text-generation #custom_code #en #autotrain_compatible #endpoints_compatible #region-us \n# Initial Knowledge Assessment Test Generation## Steps\n- Data was gathered by:\n - Downloading youtube playlists for each course from every category\n - The videos were transcribed\n - The text was fed to chatgpt via API, to formulate prompts n reponse pairs. \n- 2.78 Billion parameter Phi2 model by Microsoft was finetuned on the curated data.## How to use the model?### Note the format of the prompt. Only change the text in the variable \"paragraph\". This is the text which acts as the context for the generated test./"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-classification | PropulseLab/gender_binary | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:03:49+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #distilbert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | null | Lora Models | {} | null | nirbhayfaaya/Lora-Pretrained | [
"safetensors",
"region:us"
] | 2024-02-06T17:07:16+00:00 | [] | [] | TAGS
#safetensors #region-us
| Lora Models | [] | [
"TAGS\n#safetensors #region-us \n"
] | [
11
] | [
"passage: TAGS\n#safetensors #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# lulygavri/roberta-pol
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0734
- Validation Loss: 0.1397
- Train Accuracy: 0.9515
- Train Precision: [0.61956854 0.99608521 0.83460292]
- Train Precision W: 0.9635
- Train Recall: [0.97308663 0.94779554 0.97394503]
- Train Recall W: 0.9515
- Train F1: [0.75709278 0.97134057 0.89890607]
- Train F1 W: 0.9548
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11994, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 500, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train Precision | Train Precision W | Train Recall | Train Recall W | Train F1 | Train F1 W | Epoch |
|:----------:|:---------------:|:--------------:|:----------------------------------:|:-----------------:|:----------------------------------:|:--------------:|:----------------------------------:|:----------:|:-----:|
| 0.0734 | 0.1397 | 0.9515 | [0.61956854 0.99608521 0.83460292] | 0.9635 | [0.97308663 0.94779554 0.97394503] | 0.9515 | [0.75709278 0.97134057 0.89890607] | 0.9548 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "PlanTL-GOB-ES/roberta-base-bne", "model-index": [{"name": "lulygavri/roberta-pol", "results": []}]} | text-classification | lulygavri/roberta-pol | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"base_model:PlanTL-GOB-ES/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:09:50+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #base_model-PlanTL-GOB-ES/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| lulygavri/roberta-pol
=====================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0734
* Validation Loss: 0.1397
* Train Accuracy: 0.9515
* Train Precision: [0.61956854 0.99608521 0.83460292]
* Train Precision W: 0.9635
* Train Recall: [0.97308663 0.94779554 0.97394503]
* Train Recall W: 0.9515
* Train F1: [0.75709278 0.97134057 0.89890607]
* Train F1 W: 0.9548
* Epoch: 1
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'transformers.optimization\_tf', 'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_schedule\_fn': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 11994, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'warmup\_steps': 500, 'power': 1.0, 'name': None}, 'registered\_name': 'WarmUp'}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: mixed\_float16
### Training results
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.15.0
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11994, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 500, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #base_model-PlanTL-GOB-ES/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11994, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 500, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
75,
414,
4,
31
] | [
"passage: TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #base_model-PlanTL-GOB-ES/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11994, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 500, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: mixed\\_float16### Training results"
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null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="FatmaYoussef/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | FatmaYoussef/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-06T17:13:46+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | transformers |
## MiquMaid v2 2x70B
Check out our blogpost about this model series [Here!](https://ikaridevgit.github.io/index.html?blog=blogid-6&bo=true#Miqu-base) - Join our Discord server [Here!](https://discord.gg/Bb8pRUXy3Z)
<center>[<a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B">V2-70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO">V2-70B-DPO</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B">V2-2x70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B-DPO">V2-2x70B-DPO</a>]
</br>
<div style="width: 100%;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/ro7nbLf4sfRFmFOWcQkUU.png" style="display: block; margin: auto;">
</div></center>
This model uses the Alpaca **prompting format**
Model trained for RP conversation on Miqu-70B with our magic sauce.
Then, we have done a MoE, made of MiquMaid-v2-70B and Miqu-70B base, making the model using the finetune AND the base model for each token, working together.
We have seen a significant improvement, so we decided to share that, even if the model is very big.
## Credits:
- Undi
- IkariDev
## Description
This repo contains FP16 files of MiquMaid-v2-2x70B.
Switch: [FP16](https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B) - [GGUF](https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B-GGUF)
## Training data used:
- [Aesir datasets](https://huggingface.co/MinervaAI)
- [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt)
- [limarp](https://huggingface.co/datasets/lemonilia/LimaRP)
- [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt)
- [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal)
### Custom format:
```
### Instruction:
{system prompt}
### Input:
{input}
### Response:
{reply}
```
## Others
Undi: If you want to support us, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek | {"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]} | text-generation | NeverSleep/MiquMaid-v2-2x70B | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T17:14:08+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## MiquMaid v2 2x70B
Check out our blogpost about this model series Here! - Join our Discord server Here!
<center>[<a href="URL - <a href="URL - <a href="URL - <a href="URL
</br>
<div style="width: 100%;">
<img src="URL style="display: block; margin: auto;">
</div></center>
This model uses the Alpaca prompting format
Model trained for RP conversation on Miqu-70B with our magic sauce.
Then, we have done a MoE, made of MiquMaid-v2-70B and Miqu-70B base, making the model using the finetune AND the base model for each token, working together.
We have seen a significant improvement, so we decided to share that, even if the model is very big.
## Credits:
- Undi
- IkariDev
## Description
This repo contains FP16 files of MiquMaid-v2-2x70B.
Switch: FP16 - GGUF
## Training data used:
- Aesir datasets
- NoRobots
- limarp
- toxic-dpo-v0.1-sharegpt
- ToxicQAFinal
### Custom format:
## Others
Undi: If you want to support us, you can here.
IkariDev: Visit my retro/neocities style website please kek | [
"## MiquMaid v2 2x70B\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce.\n\nThen, we have done a MoE, made of MiquMaid-v2-70B and Miqu-70B base, making the model using the finetune AND the base model for each token, working together.\n\nWe have seen a significant improvement, so we decided to share that, even if the model is very big.",
"## Credits:\n- Undi\n- IkariDev",
"## Description\n\nThis repo contains FP16 files of MiquMaid-v2-2x70B.\n\nSwitch: FP16 - GGUF",
"## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal",
"### Custom format:",
"## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## MiquMaid v2 2x70B\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce.\n\nThen, we have done a MoE, made of MiquMaid-v2-70B and Miqu-70B base, making the model using the finetune AND the base model for each token, working together.\n\nWe have seen a significant improvement, so we decided to share that, even if the model is very big.",
"## Credits:\n- Undi\n- IkariDev",
"## Description\n\nThis repo contains FP16 files of MiquMaid-v2-2x70B.\n\nSwitch: FP16 - GGUF",
"## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal",
"### Custom format:",
"## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek"
] | [
75,
193,
11,
32,
40,
5,
32
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## MiquMaid v2 2x70B\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce.\n\nThen, we have done a MoE, made of MiquMaid-v2-70B and Miqu-70B base, making the model using the finetune AND the base model for each token, working together.\n\nWe have seen a significant improvement, so we decided to share that, even if the model is very big.## Credits:\n- Undi\n- IkariDev## Description\n\nThis repo contains FP16 files of MiquMaid-v2-2x70B.\n\nSwitch: FP16 - GGUF## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal### Custom format:## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# he
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0111
- Wer: 37.4517
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0155 | 0.02 | 50 | 0.0160 | 82.2919 |
| 0.0191 | 0.04 | 100 | 0.0271 | 41.2986 |
| 0.0194 | 0.06 | 150 | 0.0244 | 40.1791 |
| 0.0179 | 0.07 | 200 | 0.0223 | 34.4189 |
| 0.0157 | 0.09 | 250 | 0.0259 | 25.5445 |
| 0.016 | 0.11 | 300 | 0.0248 | 33.1773 |
| 0.0139 | 0.13 | 350 | 0.0214 | 29.3914 |
| 0.02 | 0.15 | 400 | 0.0223 | 37.3092 |
| 0.0149 | 0.17 | 450 | 0.0243 | 55.5669 |
| 0.0147 | 0.18 | 500 | 0.0210 | 70.0997 |
| 0.0134 | 0.2 | 550 | 0.0303 | 69.6519 |
| 0.0122 | 0.22 | 600 | 0.0182 | 47.2420 |
| 0.0104 | 0.24 | 650 | 0.0213 | 32.7906 |
| 0.0114 | 0.26 | 700 | 0.0171 | 25.8091 |
| 0.01 | 0.28 | 750 | 0.0171 | 40.4641 |
| 0.0071 | 0.3 | 800 | 0.0157 | 45.0641 |
| 0.0069 | 0.31 | 850 | 0.0172 | 49.5217 |
| 0.008 | 0.33 | 900 | 0.0169 | 48.7075 |
| 0.0056 | 0.35 | 950 | 0.0158 | 42.0721 |
| 0.0074 | 0.37 | 1000 | 0.0141 | 37.8587 |
| 0.0056 | 0.39 | 1050 | 0.0143 | 30.9994 |
| 0.0057 | 0.41 | 1100 | 0.0140 | 37.8995 |
| 0.0052 | 0.42 | 1150 | 0.0136 | 36.7393 |
| 0.003 | 0.44 | 1200 | 0.0127 | 34.9685 |
| 0.0034 | 0.46 | 1250 | 0.0119 | 35.5994 |
| 0.0041 | 0.48 | 1300 | 0.0118 | 37.6756 |
| 0.005 | 0.5 | 1350 | 0.0113 | 38.1641 |
| 0.0037 | 0.52 | 1400 | 0.0110 | 38.4490 |
| 0.0021 | 0.54 | 1450 | 0.0111 | 37.4517 |
| 0.0023 | 0.55 | 1500 | 0.0111 | 37.4517 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"language": ["he"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-medium", "model-index": [{"name": "he", "results": []}]} | automatic-speech-recognition | cantillation/whisper-medium-he-teamim-silsuless-ori-TrainAndVal-Nikud | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"he",
"base_model:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:14:39+00:00 | [] | [
"he"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #he #base_model-openai/whisper-medium #license-apache-2.0 #endpoints_compatible #region-us
| he
==
This model is a fine-tuned version of openai/whisper-medium on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0111
* Wer: 37.4517
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 4e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1500
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.0.dev0
* Pytorch 2.1.2
* Datasets 2.16.1
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
81,
112,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #he #base_model-openai/whisper-medium #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1500\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
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] |
null | null | null |
# Lora of kafka/カフカ/卡芙卡/카프카 (Honkai: Star Rail)
## What Is This?
This is the LoRA model of waifu kafka/カフカ/卡芙卡/카프카 (Honkai: Star Rail).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/kafka_starrail](https://huggingface.co/datasets/CyberHarem/kafka_starrail), which contains 1374 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets.
* Trained for 10000 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `kafka_starrail`.**
* Pruned core tags for this waifu are `bangs, breasts, eyewear_on_head, long_hair, sunglasses, purple_hair, purple_eyes, large_breasts`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 8500, you need to download [`8500/kafka_starrail.pt`](https://huggingface.co/CyberHarem/kafka_starrail/resolve/main/8500/kafka_starrail.pt) as the embedding and [`8500/kafka_starrail.safetensors`](https://huggingface.co/CyberHarem/kafka_starrail/resolve/main/8500/kafka_starrail.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 8500.
1600 images (1.67 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_0_2 | pattern_1 | pattern_2 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:--------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 8500 | 25 | **0.985** | 0.976 | 0.833 | **0.791** | [Download](https://huggingface.co/CyberHarem/kafka_starrail/resolve/main/8500/kafka_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 9750 | 29 | 0.983 | 0.953 | 0.835 | 0.790 | [Download](https://huggingface.co/CyberHarem/kafka_starrail/resolve/main/9750/kafka_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 6250 | 19 | 0.982 | **0.976** | 0.835 | 0.788 | [Download](https://huggingface.co/CyberHarem/kafka_starrail/resolve/main/6250/kafka_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 3750 | 11 | 0.978 | 0.953 | **0.843** | 0.787 | [Download](https://huggingface.co/CyberHarem/kafka_starrail/resolve/main/3750/kafka_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 2500 | 8 | 0.973 | 0.973 | 0.835 | 0.765 | [Download](https://huggingface.co/CyberHarem/kafka_starrail/resolve/main/2500/kafka_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 7750 to 10000](all/0.md)
* [Steps From 5250 to 7500](all/1.md)
* [Steps From 2750 to 5000](all/2.md)
* [Steps From 250 to 2500](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/kafka_starrail"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/kafka_starrail | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/kafka_starrail",
"license:mit",
"region:us"
] | 2024-02-06T17:15:12+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/kafka_starrail #license-mit #region-us
| Lora of kafka/カフカ/卡芙卡/카프카 (Honkai: Star Rail)
=============================================
What Is This?
-------------
This is the LoRA model of waifu kafka/カフカ/卡芙卡/카프카 (Honkai: Star Rail).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/kafka\_starrail, which contains 1374 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets.
* Trained for 10000 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'kafka\_starrail'.
* Pruned core tags for this waifu are 'bangs, breasts, eyewear\_on\_head, long\_hair, sunglasses, purple\_hair, purple\_eyes, large\_breasts'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 8500, you need to download '8500/kafka\_starrail.pt' as the embedding and '8500/kafka\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 8500.
1600 images (1.67 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 7750 to 10000
* Steps From 5250 to 7500
* Steps From 2750 to 5000
* Steps From 250 to 2500
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 8500, you need to download '8500/kafka\\_starrail.pt' as the embedding and '8500/kafka\\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 8500.\n\n\n1600 images (1.67 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/kafka_starrail #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 8500, you need to download '8500/kafka\\_starrail.pt' as the embedding and '8500/kafka\\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 8500.\n\n\n1600 images (1.67 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500"
] | [
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"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/kafka_starrail #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-classification | PropulseLab/gender_classification_with_undecided_7_percent_errors | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
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"1910.09700"
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#transformers #safetensors #distilbert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- License:
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
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### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | null |
# Lora of fu_xuan/符玄/符玄/부현 (Honkai: Star Rail)
## What Is This?
This is the LoRA model of waifu fu_xuan/符玄/符玄/부현 (Honkai: Star Rail).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/fu_xuan_starrail](https://huggingface.co/datasets/CyberHarem/fu_xuan_starrail), which contains 1091 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets.
* Trained for 10000 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `fu_xuan_starrail`.**
* Pruned core tags for this waifu are `long_hair, bangs, hair_ornament, pink_hair, parted_bangs, facial_mark, very_long_hair, yellow_eyes`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 8750, you need to download [`8750/fu_xuan_starrail.pt`](https://huggingface.co/CyberHarem/fu_xuan_starrail/resolve/main/8750/fu_xuan_starrail.pt) as the embedding and [`8750/fu_xuan_starrail.safetensors`](https://huggingface.co/CyberHarem/fu_xuan_starrail/resolve/main/8750/fu_xuan_starrail.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 8750.
1760 images (2.03 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1_0 | pattern_1_1 | pattern_2_0 | pattern_2_1 | pattern_2_2 | pattern_3_0 | pattern_3_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:------------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 8750 | 33 | **0.990** | 0.955 | 0.826 | **0.805** | [Download](https://huggingface.co/CyberHarem/fu_xuan_starrail/resolve/main/8750/fu_xuan_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 9500 | 35 | 0.985 | **0.981** | **0.831** | 0.796 | [Download](https://huggingface.co/CyberHarem/fu_xuan_starrail/resolve/main/9500/fu_xuan_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 6250 | 23 | 0.985 | 0.976 | 0.830 | 0.793 | [Download](https://huggingface.co/CyberHarem/fu_xuan_starrail/resolve/main/6250/fu_xuan_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 5750 | 22 | 0.980 | 0.974 | 0.830 | 0.775 | [Download](https://huggingface.co/CyberHarem/fu_xuan_starrail/resolve/main/5750/fu_xuan_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 5000 | 19 | 0.979 | 0.976 | 0.827 | 0.768 | [Download](https://huggingface.co/CyberHarem/fu_xuan_starrail/resolve/main/5000/fu_xuan_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 7750 to 10000](all/0.md)
* [Steps From 5250 to 7500](all/1.md)
* [Steps From 2750 to 5000](all/2.md)
* [Steps From 250 to 2500](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/fu_xuan_starrail"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/fu_xuan_starrail | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/fu_xuan_starrail",
"license:mit",
"region:us"
] | 2024-02-06T17:18:48+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/fu_xuan_starrail #license-mit #region-us
| Lora of fu\_xuan/符玄/符玄/부현 (Honkai: Star Rail)
=============================================
What Is This?
-------------
This is the LoRA model of waifu fu\_xuan/符玄/符玄/부현 (Honkai: Star Rail).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/fu\_xuan\_starrail, which contains 1091 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets.
* Trained for 10000 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'fu\_xuan\_starrail'.
* Pruned core tags for this waifu are 'long\_hair, bangs, hair\_ornament, pink\_hair, parted\_bangs, facial\_mark, very\_long\_hair, yellow\_eyes'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 8750, you need to download '8750/fu\_xuan\_starrail.pt' as the embedding and '8750/fu\_xuan\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 8750.
1760 images (2.03 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 7750 to 10000
* Steps From 5250 to 7500
* Steps From 2750 to 5000
* Steps From 250 to 2500
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 8750, you need to download '8750/fu\\_xuan\\_starrail.pt' as the embedding and '8750/fu\\_xuan\\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 8750.\n\n\n1760 images (2.03 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/fu_xuan_starrail #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 8750, you need to download '8750/fu\\_xuan\\_starrail.pt' as the embedding and '8750/fu\\_xuan\\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 8750.\n\n\n1760 images (2.03 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500"
] | [
45,
38,
476
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/fu_xuan_starrail #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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] |
null | null | transformers |
This model is a finetuned version of ```gpt2-medium```
## Model description
GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This
means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the
predictions for the token `i` only use the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a
prompt.
### To use this model
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model_name = "Sharathhebbar24/SSH_355M"
>>> model = AutoModelForCausalLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> def generate_text(prompt):
>>> inputs = tokenizer.encode(prompt, return_tensors='pt')
>>> outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
>>> generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>> return generated[:generated.rfind(".")+1]
>>> generate_text("Should I Invest in stocks")
``` | {"license": "apache-2.0", "datasets": ["databricks/databricks-dolly-15k", "gamino/wiki_medical_terms", "Sharathhebbar24/openhermes", "Sharathhebbar24/Open-Platypus", "Sharathhebbar24/sql-create-context", "Sharathhebbar24/Evol-Instruct-Code-80k-v1", "Sharathhebbar24/BeaverTails_filtered", "Sharathhebbar24/arxiv-math-instruct-50k", "Sharathhebbar24/MetaMathQA", "Intel/orca_dpo_pairs"]} | text-generation | Sharathhebbar24/SSH_355M | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"dataset:databricks/databricks-dolly-15k",
"dataset:gamino/wiki_medical_terms",
"dataset:Sharathhebbar24/openhermes",
"dataset:Sharathhebbar24/Open-Platypus",
"dataset:Sharathhebbar24/sql-create-context",
"dataset:Sharathhebbar24/Evol-Instruct-Code-80k-v1",
"dataset:Sharathhebbar24/BeaverTails_filtered",
"dataset:Sharathhebbar24/arxiv-math-instruct-50k",
"dataset:Sharathhebbar24/MetaMathQA",
"dataset:Intel/orca_dpo_pairs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T17:18:56+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #dataset-databricks/databricks-dolly-15k #dataset-gamino/wiki_medical_terms #dataset-Sharathhebbar24/openhermes #dataset-Sharathhebbar24/Open-Platypus #dataset-Sharathhebbar24/sql-create-context #dataset-Sharathhebbar24/Evol-Instruct-Code-80k-v1 #dataset-Sharathhebbar24/BeaverTails_filtered #dataset-Sharathhebbar24/arxiv-math-instruct-50k #dataset-Sharathhebbar24/MetaMathQA #dataset-Intel/orca_dpo_pairs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model is a finetuned version of
## Model description
GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This
means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the
predictions for the token 'i' only use the inputs from '1' to 'i' but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a
prompt.
### To use this model
| [
"## Model description\n\nGPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This\nmeans it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots\nof publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,\nit was trained to guess the next word in sentences.\n\nMore precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,\nshifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the\npredictions for the token 'i' only use the inputs from '1' to 'i' but not the future tokens.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a\nprompt.",
"### To use this model"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-generation #dataset-databricks/databricks-dolly-15k #dataset-gamino/wiki_medical_terms #dataset-Sharathhebbar24/openhermes #dataset-Sharathhebbar24/Open-Platypus #dataset-Sharathhebbar24/sql-create-context #dataset-Sharathhebbar24/Evol-Instruct-Code-80k-v1 #dataset-Sharathhebbar24/BeaverTails_filtered #dataset-Sharathhebbar24/arxiv-math-instruct-50k #dataset-Sharathhebbar24/MetaMathQA #dataset-Intel/orca_dpo_pairs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model description\n\nGPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This\nmeans it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots\nof publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,\nit was trained to guess the next word in sentences.\n\nMore precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,\nshifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the\npredictions for the token 'i' only use the inputs from '1' to 'i' but not the future tokens.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a\nprompt.",
"### To use this model"
] | [
220,
243,
6
] | [
"passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #dataset-databricks/databricks-dolly-15k #dataset-gamino/wiki_medical_terms #dataset-Sharathhebbar24/openhermes #dataset-Sharathhebbar24/Open-Platypus #dataset-Sharathhebbar24/sql-create-context #dataset-Sharathhebbar24/Evol-Instruct-Code-80k-v1 #dataset-Sharathhebbar24/BeaverTails_filtered #dataset-Sharathhebbar24/arxiv-math-instruct-50k #dataset-Sharathhebbar24/MetaMathQA #dataset-Intel/orca_dpo_pairs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Model description\n\nGPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This\nmeans it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots\nof publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,\nit was trained to guess the next word in sentences.\n\nMore precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,\nshifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the\npredictions for the token 'i' only use the inputs from '1' to 'i' but not the future tokens.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a\nprompt.### To use this model"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fa-zwnj-base-finetuned-NER
This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-fa-zwnj-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0001
- eval_Accuracy: 1.0000
- eval_Precision (macro): 0.9998
- eval_Recall (macro): 0.9999
- eval_F1 (macro): 0.9998
- eval_Precision (micro): 1.0000
- eval_Recall (micro): 1.0000
- eval_F1 (micro): 1.0000
- eval_runtime: 54.9025
- eval_samples_per_second: 139.902
- eval_steps_per_second: 8.761
- epoch: 16.02
- step: 10272
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "HooshvareLab/bert-fa-zwnj-base", "model-index": [{"name": "bert-fa-zwnj-base-finetuned-NER", "results": []}]} | token-classification | Pooya-Fallah/bert-fa-zwnj-base-finetuned-NER | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:HooshvareLab/bert-fa-zwnj-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:23:33+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-HooshvareLab/bert-fa-zwnj-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-fa-zwnj-base-finetuned-NER
This model is a fine-tuned version of HooshvareLab/bert-fa-zwnj-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0001
- eval_Accuracy: 1.0000
- eval_Precision (macro): 0.9998
- eval_Recall (macro): 0.9999
- eval_F1 (macro): 0.9998
- eval_Precision (micro): 1.0000
- eval_Recall (micro): 1.0000
- eval_F1 (micro): 1.0000
- eval_runtime: 54.9025
- eval_samples_per_second: 139.902
- eval_steps_per_second: 8.761
- epoch: 16.02
- step: 10272
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# bert-fa-zwnj-base-finetuned-NER\n\nThis model is a fine-tuned version of HooshvareLab/bert-fa-zwnj-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0001\n- eval_Accuracy: 1.0000\n- eval_Precision (macro): 0.9998\n- eval_Recall (macro): 0.9999\n- eval_F1 (macro): 0.9998\n- eval_Precision (micro): 1.0000\n- eval_Recall (micro): 1.0000\n- eval_F1 (micro): 1.0000\n- eval_runtime: 54.9025\n- eval_samples_per_second: 139.902\n- eval_steps_per_second: 8.761\n- epoch: 16.02\n- step: 10272",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-HooshvareLab/bert-fa-zwnj-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-fa-zwnj-base-finetuned-NER\n\nThis model is a fine-tuned version of HooshvareLab/bert-fa-zwnj-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0001\n- eval_Accuracy: 1.0000\n- eval_Precision (macro): 0.9998\n- eval_Recall (macro): 0.9999\n- eval_F1 (macro): 0.9998\n- eval_Precision (micro): 1.0000\n- eval_Recall (micro): 1.0000\n- eval_F1 (micro): 1.0000\n- eval_runtime: 54.9025\n- eval_samples_per_second: 139.902\n- eval_steps_per_second: 8.761\n- epoch: 16.02\n- step: 10272",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
76,
203,
6,
12,
8,
3,
90,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-HooshvareLab/bert-fa-zwnj-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-fa-zwnj-base-finetuned-NER\n\nThis model is a fine-tuned version of HooshvareLab/bert-fa-zwnj-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0001\n- eval_Accuracy: 1.0000\n- eval_Precision (macro): 0.9998\n- eval_Recall (macro): 0.9999\n- eval_F1 (macro): 0.9998\n- eval_Precision (micro): 1.0000\n- eval_Recall (micro): 1.0000\n- eval_F1 (micro): 1.0000\n- eval_runtime: 54.9025\n- eval_samples_per_second: 139.902\n- eval_steps_per_second: 8.761\n- epoch: 16.02\n- step: 10272## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2161
- Accuracy: 0.9245
- F1: 0.9244
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3067 | 0.911 | 0.9101 |
| No log | 2.0 | 500 | 0.2161 | 0.9245 | 0.9244 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9245, "name": "Accuracy"}, {"type": "f1", "value": 0.9243518892752073, "name": "F1"}]}]}]} | text-classification | oyemade/distilbert-base-uncased-finetuned-emotion | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:23:51+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2161
* Accuracy: 0.9245
* F1: 0.9244
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-patch4-window7-224-in22k
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0117
- F1: 0.9762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0575 | 0.99 | 50 | 0.0560 | 0.9257 |
| 0.0561 | 2.0 | 101 | 0.0359 | 0.9475 |
| 0.027 | 2.99 | 151 | 0.0212 | 0.9643 |
| 0.0236 | 4.0 | 202 | 0.0145 | 0.9737 |
| 0.0256 | 4.99 | 252 | 0.0269 | 0.9503 |
| 0.0226 | 6.0 | 303 | 0.0123 | 0.9762 |
| 0.0265 | 6.99 | 353 | 0.0135 | 0.9731 |
| 0.0168 | 8.0 | 404 | 0.0098 | 0.9824 |
| 0.0074 | 8.99 | 454 | 0.0172 | 0.9700 |
| 0.0125 | 9.9 | 500 | 0.0117 | 0.9762 |
### Framework versions
- Transformers 4.37.2
- Pytorch 1.12.1+cu102
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["f1"], "base_model": "microsoft/swin-base-patch4-window7-224-in22k", "model-index": [{"name": "swin-base-patch4-window7-224-in22k", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "f1", "value": 0.976218332192814, "name": "F1"}]}]}]} | image-classification | debajyotidasgupta/swin-base-patch4-window7-224-in22k | [
"transformers",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224-in22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:26:35+00:00 | [] | [] | TAGS
#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-base-patch4-window7-224-in22k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-base-patch4-window7-224-in22k
==================================
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0117
* F1: 0.9762
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 1.12.1+cu102
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 1.12.1+cu102\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-base-patch4-window7-224-in22k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 1.12.1+cu102\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers | <div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/X3SBrIb.png" alt="MidnightRose" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
### Overview
A 120 layer, 103B parameter frankenmerge of [Midnight-Rose-70B-v2.0.3](https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v2.0.3) with itself.
This model is uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.
### Sampler Tips
* I recommend keeping your max context to around 6144 tokens, although you can push higher if you don't mind some decrease in coherence.
* I recommend using Quadratic Sampling (i.e. smoothing factor) as it's good stuff. Experiment with values between 0.2 and 0.5.
* I recommend using Min-P. This model seems to work well with Min-P values in the entire range from low settings like 0.05 to high settings like 0.9 when paired with smoothing factor. Experiment to find your best setting.
* You can enable dynamic temperature if you want, but that adds yet another variable to consider and I find it's unnecessary with you're already using Min-P and smoothing factor.
* You don't *need* to use a high repetition penalty with this model, but it tolerates high rep penalty, so experiment to find the right value for your preferences.
Experiment with any and all of the settings below! I'm not a sampler wizard, and what suits my preferences may not suit yours.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.12,
"rep_pen": 1.1,
"rep_pen_range": 2800,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"dynatemp": false,
"min_temp": 0.8,
"max_temp": 1.35,
"dynatemp_exponent": 1,
"smoothing_factor": 0.4,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 2,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"logit_bias": [],
"n": 1,
"rep_pen_size": 0,
"genamt": 500,
"max_length": 6144
}
```
### Prompting Tips
Try the following context template for use in SillyTavern. It might help, although it's a little heavy on tokens. If you save the text as a .json file, you can import it directly.
```
{
"story_string": "{{#if system}}{{system}}\n{{/if}}\nCONTEXTUAL INFORMATION\n{{#if wiBefore}}\n- World and character info:\n{{wiBefore}}\n{{/if}}\n{{#if description}}\n- {{char}}'s background and persona:\n{{description}}\n{{/if}}\n{{#if mesExamples}}\n{{mesExamples}}\n{{/if}}\n{{#if personality}}\n{{personality}}\n{{/if}}\n{{#if scenario}}\n- Roleplay scenario:\n{{scenario}}\n{{/if}}\n{{#if wiAfter}}{{wiAfter}}\n{{/if}}\n{{#if persona}}{{persona}}\n{{/if}}",
"example_separator": "",
"chat_start": "---\nTaking the above information into consideration, you must engage with {{user}} and others as {{char}} in the roleplay below this line. Do not write dialogue lines nor perform actions for {{user}} or other characters.\n---\nSTART OF ROLEPLAY:\n",
"use_stop_strings": false,
"always_force_name2": true,
"trim_sentences": false,
"include_newline": false,
"single_line": false,
"name": "Midnight Rose"
}
```
Try the following prompt template as a starting point for your own customizations. The cajoling parts supposedly make LLMs perform better. See [this paper](https://arxiv.org/abs/2307.11760).
I recommend the Vicuna instruction format for this blend but others work too. Tulu's format is another good one to use.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts. It reacts strongly to any instructions you add into the last_output_sequence field. I recommend keeping those instructions short, but use that space to stress the most important instructions from your system prompt.
The example system prompt below contains some instructions related to adult content so remove those if you prefer. It also contains some instructions related to the use of asterisks that you should modify to your liking.
If you plan to run this model at 4096 context, I recommend that you slim down the template system prompt below because it's heavy on tokens. Discard what you don't want or need.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": true,
"system_prompt": "You are a creative and detail-oriented AI assistant engaged in an uncensored iterative roleplaying experience as {{char}} with me, the user, playing the roles of {{user}} and narrator. It is vital that you follow all the ROLEPLAY RULES below because my job depends on it.\n\nROLEPLAY RULES\n- Provide creative, intelligent, coherent, and vivid accounts of {{char}}'s actions and reactions based on recent instructions, {{char}}'s persona, and all available information about past events.\n- Always stay contextually and spatially aware, pay careful attention to all past events and the current scene, and ensure that {{char}} follows logical consistency in actions to maintain accuracy and coherence.\n- Explicit adult content and profanity are permitted.\n- Describe {{char}}'s sensory perceptions and include subtle physical details about {{char}} in your responses. Vary these details to keep the roleplay fresh and engaging.\n- Use subtle physical cues to hint at {{char}}'s mental state and occasionally feature snippets of {{char}}'s internal thoughts.\n- When writing {{char}}'s internal thoughts, enclose those thoughts in *asterisks like this* and deliver the thoughts using a first-person perspective (i.e. use \"I\" pronouns).\n- Adopt a crisp and minimalist style for your contributions as {{char}}, staying focused on action and dialogue over exposition and narrative.\n- Only the user may advance time in the roleplay. Keep the progression grounded in the present context.",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "USER:\n",
"output_sequence": "ASSISTANT:\n",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "ASSISTANT(roleplay exclusively as {{char}} ensuring logical consistency with spacial awareness and past events to maintain accuracy and coherence):\n",
"activation_regex": "",
"name": "Midnight Rose Roleplay"
}
```
### Quantizations
* Static GGUF -- [mradermacher/Midnight-Rose-103B-v2.0.3-GGUF](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-GGUF)
* Weighted GGUF -- [mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF](https://huggingface.co/mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF)
* Exl2 2.4bpw -- [llmixer/Midnight-Rose-103B-v2.0.3-2.4bpw-h6-exl2](https://huggingface.co/llmixer/Midnight-Rose-103B-v2.0.3-2.4bpw-h6-exl2)
* Exl2 3.0bpw -- [llmixer/Midnight-Rose-103B-v2.0.3-3.0bpw-h6-exl2](https://huggingface.co/llmixer/Midnight-Rose-103B-v2.0.3-3.0bpw-h6-exl2)
* Exl2 3.5bpw -- [llmixer/Midnight-Rose-103B-v2.0.3-3.5bpw-h6-exl2](https://huggingface.co/llmixer/Midnight-Rose-103B-v2.0.3-3.5bpw-h6-exl2)
* Exl2 4.0bpw -- [llmixer/Midnight-Rose-103B-v2.0.3-4.0bpw-h6-exl2](https://huggingface.co/llmixer/Midnight-Rose-103B-v2.0.3-4.0bpw-h6-exl2)
* Exl2 5.0bpw -- [llmixer/Midnight-Rose-103B-v2.0.3-5.0bpw-h6-exl2](https://huggingface.co/llmixer/Midnight-Rose-103B-v2.0.3-5.0bpw-h6-exl2)
### Licence and usage restrictions
Llama2 license inherited from base models, plus restrictions applicable to [Dreamgen/Opus](https://huggingface.co/dreamgen/opus-v0.5-70b).
Tulu also has its own license, available at https://allenai.org/impact-license.
I am not a lawyer and I do not profess to know how multiple licenses intersect in a merge of LLM model weights. You should consult with a lawyer before using any model merge beyond private use.
### Tools Used
* [mergekit](https://github.com/cg123/mergekit)
```
slices:
- sources:
- model: /home/llm/mergequant/models/mr-v2.0.3
layer_range: [0, 40] # 40
- sources:
- model: /home/llm/mergequant/models/mr-v2.0.3
layer_range: [20, 60] # 40
- sources:
- model: /home/llm/mergequant/models/mr-v2.0.3
layer_range: [40, 80] # 40
merge_method: passthrough
dtype: float16
``` | {"language": ["en"], "license": "llama2"} | text-generation | sophosympatheia/Midnight-Rose-103B-v2.0.3 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"arxiv:2307.11760",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T17:27:53+00:00 | [
"2307.11760"
] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #en #arxiv-2307.11760 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.URL alt="MidnightRose" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
### Overview
A 120 layer, 103B parameter frankenmerge of Midnight-Rose-70B-v2.0.3 with itself.
This model is uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.
### Sampler Tips
* I recommend keeping your max context to around 6144 tokens, although you can push higher if you don't mind some decrease in coherence.
* I recommend using Quadratic Sampling (i.e. smoothing factor) as it's good stuff. Experiment with values between 0.2 and 0.5.
* I recommend using Min-P. This model seems to work well with Min-P values in the entire range from low settings like 0.05 to high settings like 0.9 when paired with smoothing factor. Experiment to find your best setting.
* You can enable dynamic temperature if you want, but that adds yet another variable to consider and I find it's unnecessary with you're already using Min-P and smoothing factor.
* You don't *need* to use a high repetition penalty with this model, but it tolerates high rep penalty, so experiment to find the right value for your preferences.
Experiment with any and all of the settings below! I'm not a sampler wizard, and what suits my preferences may not suit yours.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
### Prompting Tips
Try the following context template for use in SillyTavern. It might help, although it's a little heavy on tokens. If you save the text as a .json file, you can import it directly.
Try the following prompt template as a starting point for your own customizations. The cajoling parts supposedly make LLMs perform better. See this paper.
I recommend the Vicuna instruction format for this blend but others work too. Tulu's format is another good one to use.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts. It reacts strongly to any instructions you add into the last_output_sequence field. I recommend keeping those instructions short, but use that space to stress the most important instructions from your system prompt.
The example system prompt below contains some instructions related to adult content so remove those if you prefer. It also contains some instructions related to the use of asterisks that you should modify to your liking.
If you plan to run this model at 4096 context, I recommend that you slim down the template system prompt below because it's heavy on tokens. Discard what you don't want or need.
If you save this as a .json file, you can import it directly into Silly Tavern.
### Quantizations
* Static GGUF -- mradermacher/Midnight-Rose-103B-v2.0.3-GGUF
* Weighted GGUF -- mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF
* Exl2 2.4bpw -- llmixer/Midnight-Rose-103B-v2.0.3-2.4bpw-h6-exl2
* Exl2 3.0bpw -- llmixer/Midnight-Rose-103B-v2.0.3-3.0bpw-h6-exl2
* Exl2 3.5bpw -- llmixer/Midnight-Rose-103B-v2.0.3-3.5bpw-h6-exl2
* Exl2 4.0bpw -- llmixer/Midnight-Rose-103B-v2.0.3-4.0bpw-h6-exl2
* Exl2 5.0bpw -- llmixer/Midnight-Rose-103B-v2.0.3-5.0bpw-h6-exl2
### Licence and usage restrictions
Llama2 license inherited from base models, plus restrictions applicable to Dreamgen/Opus.
Tulu also has its own license, available at URL
I am not a lawyer and I do not profess to know how multiple licenses intersect in a merge of LLM model weights. You should consult with a lawyer before using any model merge beyond private use.
### Tools Used
* mergekit
| [
"### Overview\n\nA 120 layer, 103B parameter frankenmerge of Midnight-Rose-70B-v2.0.3 with itself.\n\nThis model is uncensored. *You are responsible for whatever you do with it.*\n\nThis model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.",
"### Sampler Tips\n\n* I recommend keeping your max context to around 6144 tokens, although you can push higher if you don't mind some decrease in coherence.\n* I recommend using Quadratic Sampling (i.e. smoothing factor) as it's good stuff. Experiment with values between 0.2 and 0.5.\n* I recommend using Min-P. This model seems to work well with Min-P values in the entire range from low settings like 0.05 to high settings like 0.9 when paired with smoothing factor. Experiment to find your best setting.\n* You can enable dynamic temperature if you want, but that adds yet another variable to consider and I find it's unnecessary with you're already using Min-P and smoothing factor.\n* You don't *need* to use a high repetition penalty with this model, but it tolerates high rep penalty, so experiment to find the right value for your preferences.\n \nExperiment with any and all of the settings below! I'm not a sampler wizard, and what suits my preferences may not suit yours.\n\nIf you save the below settings as a .json file, you can import them directly into Silly Tavern.",
"### Prompting Tips\n\nTry the following context template for use in SillyTavern. It might help, although it's a little heavy on tokens. If you save the text as a .json file, you can import it directly.\n\n\n\nTry the following prompt template as a starting point for your own customizations. The cajoling parts supposedly make LLMs perform better. See this paper.\nI recommend the Vicuna instruction format for this blend but others work too. Tulu's format is another good one to use.\n\n*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts. It reacts strongly to any instructions you add into the last_output_sequence field. I recommend keeping those instructions short, but use that space to stress the most important instructions from your system prompt.\n\nThe example system prompt below contains some instructions related to adult content so remove those if you prefer. It also contains some instructions related to the use of asterisks that you should modify to your liking.\n\nIf you plan to run this model at 4096 context, I recommend that you slim down the template system prompt below because it's heavy on tokens. Discard what you don't want or need.\n\nIf you save this as a .json file, you can import it directly into Silly Tavern.",
"### Quantizations\n* Static GGUF -- mradermacher/Midnight-Rose-103B-v2.0.3-GGUF\n* Weighted GGUF -- mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF\n* Exl2 2.4bpw -- llmixer/Midnight-Rose-103B-v2.0.3-2.4bpw-h6-exl2\n* Exl2 3.0bpw -- llmixer/Midnight-Rose-103B-v2.0.3-3.0bpw-h6-exl2\n* Exl2 3.5bpw -- llmixer/Midnight-Rose-103B-v2.0.3-3.5bpw-h6-exl2\n* Exl2 4.0bpw -- llmixer/Midnight-Rose-103B-v2.0.3-4.0bpw-h6-exl2\n* Exl2 5.0bpw -- llmixer/Midnight-Rose-103B-v2.0.3-5.0bpw-h6-exl2",
"### Licence and usage restrictions\n\nLlama2 license inherited from base models, plus restrictions applicable to Dreamgen/Opus.\nTulu also has its own license, available at URL\nI am not a lawyer and I do not profess to know how multiple licenses intersect in a merge of LLM model weights. You should consult with a lawyer before using any model merge beyond private use.",
"### Tools Used\n\n* mergekit"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #en #arxiv-2307.11760 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\nA 120 layer, 103B parameter frankenmerge of Midnight-Rose-70B-v2.0.3 with itself.\n\nThis model is uncensored. *You are responsible for whatever you do with it.*\n\nThis model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.",
"### Sampler Tips\n\n* I recommend keeping your max context to around 6144 tokens, although you can push higher if you don't mind some decrease in coherence.\n* I recommend using Quadratic Sampling (i.e. smoothing factor) as it's good stuff. Experiment with values between 0.2 and 0.5.\n* I recommend using Min-P. This model seems to work well with Min-P values in the entire range from low settings like 0.05 to high settings like 0.9 when paired with smoothing factor. Experiment to find your best setting.\n* You can enable dynamic temperature if you want, but that adds yet another variable to consider and I find it's unnecessary with you're already using Min-P and smoothing factor.\n* You don't *need* to use a high repetition penalty with this model, but it tolerates high rep penalty, so experiment to find the right value for your preferences.\n \nExperiment with any and all of the settings below! I'm not a sampler wizard, and what suits my preferences may not suit yours.\n\nIf you save the below settings as a .json file, you can import them directly into Silly Tavern.",
"### Prompting Tips\n\nTry the following context template for use in SillyTavern. It might help, although it's a little heavy on tokens. If you save the text as a .json file, you can import it directly.\n\n\n\nTry the following prompt template as a starting point for your own customizations. The cajoling parts supposedly make LLMs perform better. See this paper.\nI recommend the Vicuna instruction format for this blend but others work too. Tulu's format is another good one to use.\n\n*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts. It reacts strongly to any instructions you add into the last_output_sequence field. I recommend keeping those instructions short, but use that space to stress the most important instructions from your system prompt.\n\nThe example system prompt below contains some instructions related to adult content so remove those if you prefer. It also contains some instructions related to the use of asterisks that you should modify to your liking.\n\nIf you plan to run this model at 4096 context, I recommend that you slim down the template system prompt below because it's heavy on tokens. Discard what you don't want or need.\n\nIf you save this as a .json file, you can import it directly into Silly Tavern.",
"### Quantizations\n* Static GGUF -- mradermacher/Midnight-Rose-103B-v2.0.3-GGUF\n* Weighted GGUF -- mradermacher/Midnight-Rose-103B-v2.0.3-i1-GGUF\n* Exl2 2.4bpw -- llmixer/Midnight-Rose-103B-v2.0.3-2.4bpw-h6-exl2\n* Exl2 3.0bpw -- llmixer/Midnight-Rose-103B-v2.0.3-3.0bpw-h6-exl2\n* Exl2 3.5bpw -- llmixer/Midnight-Rose-103B-v2.0.3-3.5bpw-h6-exl2\n* Exl2 4.0bpw -- llmixer/Midnight-Rose-103B-v2.0.3-4.0bpw-h6-exl2\n* Exl2 5.0bpw -- llmixer/Midnight-Rose-103B-v2.0.3-5.0bpw-h6-exl2",
"### Licence and usage restrictions\n\nLlama2 license inherited from base models, plus restrictions applicable to Dreamgen/Opus.\nTulu also has its own license, available at URL\nI am not a lawyer and I do not profess to know how multiple licenses intersect in a merge of LLM model weights. You should consult with a lawyer before using any model merge beyond private use.",
"### Tools Used\n\n* mergekit"
] | [
64,
95,
269,
294,
253,
86,
8
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #en #arxiv-2307.11760 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Overview\n\nA 120 layer, 103B parameter frankenmerge of Midnight-Rose-70B-v2.0.3 with itself.\n\nThis model is uncensored. *You are responsible for whatever you do with it.*\n\nThis model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.### Sampler Tips\n\n* I recommend keeping your max context to around 6144 tokens, although you can push higher if you don't mind some decrease in coherence.\n* I recommend using Quadratic Sampling (i.e. smoothing factor) as it's good stuff. Experiment with values between 0.2 and 0.5.\n* I recommend using Min-P. This model seems to work well with Min-P values in the entire range from low settings like 0.05 to high settings like 0.9 when paired with smoothing factor. Experiment to find your best setting.\n* You can enable dynamic temperature if you want, but that adds yet another variable to consider and I find it's unnecessary with you're already using Min-P and smoothing factor.\n* You don't *need* to use a high repetition penalty with this model, but it tolerates high rep penalty, so experiment to find the right value for your preferences.\n \nExperiment with any and all of the settings below! I'm not a sampler wizard, and what suits my preferences may not suit yours.\n\nIf you save the below settings as a .json file, you can import them directly into Silly Tavern."
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | imsanjoykb/mistral-7b-dolly | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:29:51+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | token-classification | alpha-brain/ner-pii-kaggle-v1 | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"arxiv:1910.09700",
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"1910.09700"
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#transformers #safetensors #distilbert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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## Technical Specifications [optional]
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### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline_ner_model_d` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.7.2,<3.8.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `allergy_name`, `cancer`, `chronic_disease`, `treatment` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 94.80 |
| `ENTS_P` | 94.71 |
| `ENTS_R` | 94.89 |
| `TRANSFORMER_LOSS` | 406496.45 |
| `NER_LOSS` | 452435.57 | | {"language": ["en"], "tags": ["spacy", "token-classification"]} | token-classification | rame/en_pipeline_ner_model_d | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | 2024-02-06T17:31:48+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (4 labels for 1 components)
### Accuracy
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"### Accuracy"
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"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
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null | null | transformers |
# Quyen
<img src="quyen.webp" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- **Quyen-SE (0.5B)**
- **Quyen-Mini (1.8B)**
- **Quyen (4B)**
- **Quyen-Plus (7B)**
- **Quyen-Pro (14B)**
- **Quyen-Pro-Max (72B)**
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by **Teknium**
- *Capyabara* by **LDJ**
- *argilla/distilabel-capybara-dpo-7k-binarized* by **argilla**
- *orca_dpo_pairs* by **Intel**
- and Private Data by **Ontocord** & **BEE-spoke-data**
# Prompt Template
- All Quyen models use ChatML as the default template:
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant
```
- You can also use `apply_chat_template`:
```python
messages = [
{"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
{"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to **Tensoic** and **Ontocord** for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes. | {"language": ["en"], "license": "other", "library_name": "transformers", "datasets": ["teknium/OpenHermes-2.5", "LDJnr/Capybara", "Intel/orca_dpo_pairs", "argilla/distilabel-capybara-dpo-7k-binarized"], "pipeline_tag": "text-generation"} | text-generation | LoneStriker/Quyen-Pro-v0.1-AWQ | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:LDJnr/Capybara",
"dataset:Intel/orca_dpo_pairs",
"dataset:argilla/distilabel-capybara-dpo-7k-binarized",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | 2024-02-06T17:34:18+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #4-bit #region-us
|
# Quyen
<img src="URL" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- Quyen-SE (0.5B)
- Quyen-Mini (1.8B)
- Quyen (4B)
- Quyen-Plus (7B)
- Quyen-Pro (14B)
- Quyen-Pro-Max (72B)
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by Teknium
- *Capyabara* by LDJ
- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla
- *orca_dpo_pairs* by Intel
- and Private Data by Ontocord & BEE-spoke-data
# Prompt Template
- All Quyen models use ChatML as the default template:
- You can also use 'apply_chat_template':
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes. | [
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
"TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #4-bit #region-us \n",
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
113,
27,
171,
33,
18,
54
] | [
"passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #4-bit #region-us \n# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\"># Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
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] |
null | null | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="FatmaYoussef/q-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.16 +/- 3.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | FatmaYoussef/q-taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-06T17:38:12+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | transformers | # SegFormer model fine-tuned on AROI
SegFormer model fine-tuned on AROI dataset [AROI: Annotated Retinal OCT Images Database](https://ieeexplore.ieee.org/abstract/document/9596934).
Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.
| {"license": "mit"} | null | TUCN/Segformer_OCT_Retina | [
"transformers",
"pytorch",
"segformer",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:38:44+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #license-mit #endpoints_compatible #region-us
| # SegFormer model fine-tuned on AROI
SegFormer model fine-tuned on AROI dataset AROI: Annotated Retinal OCT Images Database.
Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.
| [
"# SegFormer model fine-tuned on AROI\n\nSegFormer model fine-tuned on AROI dataset AROI: Annotated Retinal OCT Images Database.\n\nDisclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nSegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset."
] | [
"TAGS\n#transformers #pytorch #segformer #license-mit #endpoints_compatible #region-us \n",
"# SegFormer model fine-tuned on AROI\n\nSegFormer model fine-tuned on AROI dataset AROI: Annotated Retinal OCT Images Database.\n\nDisclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nSegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset."
] | [
29,
72,
91
] | [
"passage: TAGS\n#transformers #pytorch #segformer #license-mit #endpoints_compatible #region-us \n# SegFormer model fine-tuned on AROI\n\nSegFormer model fine-tuned on AROI dataset AROI: Annotated Retinal OCT Images Database.\n\nDisclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nSegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset."
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null | null | null |
# Face re-aging model
See [GitHub](https://github.com/timroelofs123/face_reaging) for more information. | {"license": "mit"} | null | timroelofs123/face_re-aging | [
"license:mit",
"has_space",
"region:us"
] | 2024-02-06T17:39:58+00:00 | [] | [] | TAGS
#license-mit #has_space #region-us
|
# Face re-aging model
See GitHub for more information. | [
"# Face re-aging model\n\nSee GitHub for more information."
] | [
"TAGS\n#license-mit #has_space #region-us \n",
"# Face re-aging model\n\nSee GitHub for more information."
] | [
15,
14
] | [
"passage: TAGS\n#license-mit #has_space #region-us \n# Face re-aging model\n\nSee GitHub for more information."
] | [
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | feature-extraction | Fraol/CAPS-py-java2 | [
"transformers",
"safetensors",
"t5",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T17:41:00+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #t5 #feature-extraction #arxiv-1910.09700 #endpoints_compatible #text-generation-inference #region-us
|
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## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #t5 #feature-extraction #arxiv-1910.09700 #endpoints_compatible #text-generation-inference #region-us \n",
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #t5 #feature-extraction #arxiv-1910.09700 #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# longformer-sep_tok
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2615
- Claim: {'precision': 0.6071779744346116, 'recall': 0.5809031044214488, 'f1-score': 0.5937500000000001, 'support': 4252.0}
- Majorclaim: {'precision': 0.8395117540687161, 'recall': 0.8510540788267644, 'f1-score': 0.8452435138825672, 'support': 2182.0}
- O: {'precision': 1.0, 'recall': 0.9992978144474678, 'f1-score': 0.9996487839143033, 'support': 11393.0}
- Premise: {'precision': 0.8835139944992719, 'recall': 0.8952459016393443, 'f1-score': 0.8893412588551421, 'support': 12200.0}
- Accuracy: 0.8870
- Macro avg: {'precision': 0.83255093075065, 'recall': 0.8316252248337562, 'f1-score': 0.8319958891630032, 'support': 30027.0}
- Weighted avg: {'precision': 0.8853833592288615, 'recall': 0.8870016984713758, 'f1-score': 0.8861327572005248, 'support': 30027.0}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 41 | 0.3496 | {'precision': 0.514827018121911, 'recall': 0.2939793038570085, 'f1-score': 0.37425149700598803, 'support': 4252.0} | {'precision': 0.6678657074340527, 'recall': 0.7658111824014665, 'f1-score': 0.7134927412467975, 'support': 2182.0} | {'precision': 0.996649620878152, 'recall': 0.9921881857280787, 'f1-score': 0.9944138992742467, 'support': 11393.0} | {'precision': 0.826608505997819, 'recall': 0.9319672131147541, 'f1-score': 0.8761317665189751, 'support': 12200.0} | 0.8524 | {'precision': 0.7514877131079837, 'recall': 0.7459864712753269, 'f1-score': 0.7395724760115018, 'support': 30027.0} | {'precision': 0.8354407819133993, 'recall': 0.8523995071102675, 'f1-score': 0.8381231435918661, 'support': 30027.0} |
| No log | 2.0 | 82 | 0.2859 | {'precision': 0.5445935280189423, 'recall': 0.486829727187206, 'f1-score': 0.514094126412517, 'support': 4252.0} | {'precision': 0.8543130990415335, 'recall': 0.6127406049495875, 'f1-score': 0.7136375767280491, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.997366804178004, 'f1-score': 0.9986816663737036, 'support': 11393.0} | {'precision': 0.851030230109791, 'recall': 0.9276229508196722, 'f1-score': 0.8876774648992078, 'support': 12200.0} | 0.8688 | {'precision': 0.8124842142925667, 'recall': 0.7561400217836175, 'f1-score': 0.7785227086033695, 'support': 30027.0} | {'precision': 0.8643984304320985, 'recall': 0.8687847603823226, 'f1-score': 0.8642465352746717, 'support': 30027.0} |
| No log | 3.0 | 123 | 0.2615 | {'precision': 0.617028164454516, 'recall': 0.4482596425211665, 'f1-score': 0.5192753030922217, 'support': 4252.0} | {'precision': 0.8376436781609196, 'recall': 0.8015582034830431, 'f1-score': 0.8192037470725996, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9988589484771351, 'f1-score': 0.9994291485531112, 'support': 11393.0} | {'precision': 0.8495916852264291, 'recall': 0.9380327868852459, 'f1-score': 0.8916244643552784, 'support': 12200.0} | 0.8818 | {'precision': 0.8260658819604662, 'recall': 0.7966773953416476, 'f1-score': 0.8073831657683027, 'support': 30027.0} | {'precision': 0.872859786884143, 'recall': 0.8818396776234723, 'f1-score': 0.874538779080845, 'support': 30027.0} |
| No log | 4.0 | 164 | 0.2591 | {'precision': 0.6161943319838057, 'recall': 0.5369238005644402, 'f1-score': 0.5738343596832978, 'support': 4252.0} | {'precision': 0.860332541567696, 'recall': 0.8299725022914757, 'f1-score': 0.8448798693725216, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9992100412534012, 'f1-score': 0.9996048645563507, 'support': 11393.0} | {'precision': 0.8684641159510637, 'recall': 0.9135245901639344, 'f1-score': 0.8904246394758917, 'support': 12200.0} | 0.8866 | {'precision': 0.8362477473756413, 'recall': 0.8199077335683129, 'f1-score': 0.8271859332720154, 'support': 30027.0} | {'precision': 0.8820583514802954, 'recall': 0.8866353615079762, 'f1-score': 0.8837096744876479, 'support': 30027.0} |
| No log | 5.0 | 205 | 0.2615 | {'precision': 0.6071779744346116, 'recall': 0.5809031044214488, 'f1-score': 0.5937500000000001, 'support': 4252.0} | {'precision': 0.8395117540687161, 'recall': 0.8510540788267644, 'f1-score': 0.8452435138825672, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9992978144474678, 'f1-score': 0.9996487839143033, 'support': 11393.0} | {'precision': 0.8835139944992719, 'recall': 0.8952459016393443, 'f1-score': 0.8893412588551421, 'support': 12200.0} | 0.8870 | {'precision': 0.83255093075065, 'recall': 0.8316252248337562, 'f1-score': 0.8319958891630032, 'support': 30027.0} | {'precision': 0.8853833592288615, 'recall': 0.8870016984713758, 'f1-score': 0.8861327572005248, 'support': 30027.0} |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["fancy_dataset"], "metrics": ["accuracy"], "base_model": "allenai/longformer-base-4096", "model-index": [{"name": "longformer-sep_tok", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "fancy_dataset", "type": "fancy_dataset", "config": "sep_tok", "split": "test", "args": "sep_tok"}, "metrics": [{"type": "accuracy", "value": 0.8870016984713758, "name": "Accuracy"}]}]}]} | token-classification | Theoreticallyhugo/longformer-sep_tok | [
"transformers",
"safetensors",
"longformer",
"token-classification",
"generated_from_trainer",
"dataset:fancy_dataset",
"base_model:allenai/longformer-base-4096",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:41:18+00:00 | [] | [] | TAGS
#transformers #safetensors #longformer #token-classification #generated_from_trainer #dataset-fancy_dataset #base_model-allenai/longformer-base-4096 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| longformer-sep\_tok
===================
This model is a fine-tuned version of allenai/longformer-base-4096 on the fancy\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2615
* Claim: {'precision': 0.6071779744346116, 'recall': 0.5809031044214488, 'f1-score': 0.5937500000000001, 'support': 4252.0}
* Majorclaim: {'precision': 0.8395117540687161, 'recall': 0.8510540788267644, 'f1-score': 0.8452435138825672, 'support': 2182.0}
* O: {'precision': 1.0, 'recall': 0.9992978144474678, 'f1-score': 0.9996487839143033, 'support': 11393.0}
* Premise: {'precision': 0.8835139944992719, 'recall': 0.8952459016393443, 'f1-score': 0.8893412588551421, 'support': 12200.0}
* Accuracy: 0.8870
* Macro avg: {'precision': 0.83255093075065, 'recall': 0.8316252248337562, 'f1-score': 0.8319958891630032, 'support': 30027.0}
* Weighted avg: {'precision': 0.8853833592288615, 'recall': 0.8870016984713758, 'f1-score': 0.8861327572005248, 'support': 30027.0}
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
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"### Training results",
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Bazsalanszky/llama2-alpaca-gpt4-hungarian | [
"transformers",
"safetensors",
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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### Training Procedure
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Testing Data
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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null | null | transformers |
# Text Classification Toxicity
This model is a fined-tuned version of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large) on the on the [Jigsaw 1st Kaggle competition](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge) dataset using [unitary/toxic-bert](https://huggingface.co/unitary/toxic-bert) as teacher model.
The quantized version in ONNX format can be found [here](https://huggingface.co/minuva/MiniLMv2-toxic-jigaw-lite-onnx).
The model contains two labels only (toxicity and severe toxicity). For the model with all labels refer to this [page](https://huggingface.co/minuva/MiniLMv2-toxic-jijgsaw)
# Load the Model
```py
from transformers import pipeline
pipe = pipeline(model='minuva/MiniLMv2-toxic-jigsaw-lite', task='text-classification')
pipe("This is pure trash")
# [{'label': 'toxic', 'score': 0.887}]
```
# Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 48
- eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- warmup_ratio: 0.1
# Metrics (comparison with teacher model)
| Teacher (params) | Student (params) | Set (metric) | Score (teacher) | Score (student) |
|--------------------|-------------|----------|--------| --------|
| unitary/toxic-bert (110M) | MiniLMv2-toxic-jigsaw-lite (23M) | Test (ROC_AUC) | 0.982677 | 0.9815 |
# Deployment
Check our [fast-nlp-text-toxicity repository](https://github.com/minuva/fast-nlp-text-toxicity) for a FastAPI and ONNX based server to deploy this model on CPU devices.
| {"language": ["en"], "license": "apache-2.0", "tags": ["toxic", "toxicity", "hate speech", "offensive language", "multi-class-classification", "multi-label-classification"]} | text-classification | minuva/MiniLMv2-toxic-jigsaw-lite | [
"transformers",
"safetensors",
"bert",
"text-classification",
"toxic",
"toxicity",
"hate speech",
"offensive language",
"multi-class-classification",
"multi-label-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T17:47:07+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #bert #text-classification #toxic #toxicity #hate speech #offensive language #multi-class-classification #multi-label-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Text Classification Toxicity
============================
This model is a fined-tuned version of MiniLMv2-L6-H384 on the on the Jigsaw 1st Kaggle competition dataset using unitary/toxic-bert as teacher model.
The quantized version in ONNX format can be found here.
The model contains two labels only (toxicity and severe toxicity). For the model with all labels refer to this page
Load the Model
==============
Training hyperparameters
========================
The following hyperparameters were used during training:
* learning\_rate: 6e-05
* train\_batch\_size: 48
* eval\_batch\_size: 48
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* warmup\_ratio: 0.1
Metrics (comparison with teacher model)
=======================================
Deployment
==========
Check our fast-nlp-text-toxicity repository for a FastAPI and ONNX based server to deploy this model on CPU devices.
| [] | [
"TAGS\n#transformers #safetensors #bert #text-classification #toxic #toxicity #hate speech #offensive language #multi-class-classification #multi-label-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
74
] | [
"passage: TAGS\n#transformers #safetensors #bert #text-classification #toxic #toxicity #hate speech #offensive language #multi-class-classification #multi-label-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "vinai/PhoGPT-4B-Chat"} | null | hung96ads/PhoGPT-4B-Chat-qlora | [
"peft",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-vinai/PhoGPT-4B-Chat #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
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APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmind_hotpot_train8000_eval7405_v1_ic_qa_gpt2-xl
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_ic_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0009
- Accuracy: 0.6543
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 1.914 | 1.0 | 500 | 0.6520 | 1.8847 |
| 1.561 | 2.0 | 1000 | 0.6541 | 1.8817 |
| 1.2835 | 3.0 | 1500 | 0.6547 | 1.9318 |
| 1.0211 | 4.0 | 2000 | 0.6547 | 2.0172 |
| 0.7992 | 5.0 | 2500 | 0.6547 | 2.1044 |
| 0.6266 | 6.0 | 3000 | 0.6548 | 2.2005 |
| 0.4843 | 7.0 | 3500 | 0.6543 | 2.2975 |
| 0.3645 | 8.0 | 4000 | 0.6545 | 2.3896 |
| 0.2839 | 9.0 | 4500 | 0.6546 | 2.4761 |
| 0.2174 | 10.0 | 5000 | 0.6546 | 2.5636 |
| 0.1703 | 11.0 | 5500 | 0.6547 | 2.6410 |
| 0.14 | 12.0 | 6000 | 0.6548 | 2.7107 |
| 0.1186 | 13.0 | 6500 | 0.6546 | 2.7585 |
| 0.1066 | 14.0 | 7000 | 0.6544 | 2.8006 |
| 0.0967 | 15.0 | 7500 | 0.6547 | 2.8437 |
| 0.091 | 16.0 | 8000 | 0.6546 | 2.8742 |
| 0.0857 | 17.0 | 8500 | 0.6547 | 2.9046 |
| 0.082 | 18.0 | 9000 | 0.6545 | 2.9471 |
| 0.0789 | 19.0 | 9500 | 2.9636 | 0.6543 |
| 0.0758 | 20.0 | 10000 | 3.0009 | 0.6543 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tyzhu/lmind_hotpot_train8000_eval7405_v1_ic_qa"], "metrics": ["accuracy"], "base_model": "gpt2-xl", "model-index": [{"name": "lmind_hotpot_train8000_eval7405_v1_ic_qa_gpt2-xl", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "tyzhu/lmind_hotpot_train8000_eval7405_v1_ic_qa", "type": "tyzhu/lmind_hotpot_train8000_eval7405_v1_ic_qa"}, "metrics": [{"type": "accuracy", "value": 0.6542598349847281, "name": "Accuracy"}]}]}]} | text-generation | tyzhu/lmind_hotpot_train8000_eval7405_v1_ic_qa_gpt2-xl | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:tyzhu/lmind_hotpot_train8000_eval7405_v1_ic_qa",
"base_model:gpt2-xl",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T17:52:29+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-tyzhu/lmind_hotpot_train8000_eval7405_v1_ic_qa #base_model-gpt2-xl #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| lmind\_hotpot\_train8000\_eval7405\_v1\_ic\_qa\_gpt2-xl
=======================================================
This model is a fine-tuned version of gpt2-xl on the tyzhu/lmind\_hotpot\_train8000\_eval7405\_v1\_ic\_qa dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0009
* Accuracy: 0.6543
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 20.0
### Training results
### Framework versions
* Transformers 4.34.0
* Pytorch 2.1.0+cu121
* Datasets 2.14.5
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 20.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-tyzhu/lmind_hotpot_train8000_eval7405_v1_ic_qa #base_model-gpt2-xl #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 20.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1"
] | [
101,
99,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-tyzhu/lmind_hotpot_train8000_eval7405_v1_ic_qa #base_model-gpt2-xl #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 20.0### Training results### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | maroua/my_pmsi_model | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Demo [optional]:
## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Cloud Provider:
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## Technical Specifications [optional]
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null | null | diffusers |
# Stable Cascade
<!-- Provide a quick summary of what the model is/does. -->
<img src="figures/collage_1.jpg" width="800">
This model is built upon the [Würstchen](https://openreview.net/forum?id=gU58d5QeGv) architecture and its main
difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this
important? The smaller the latent space, the **faster** you can run inference and the **cheaper** the training becomes.
How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being
encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a
1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the
highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable
Diffusion 1.5. <br> <br>
Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions
like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.
## Model Details
### Model Description
Stable Cascade is a diffusion model trained to generate images given a text prompt.
- **Developed by:** Stability AI
- **Funded by:** Stability AI
- **Model type:** Generative text-to-image model
### Model Sources
For research purposes, we recommend our `StableCascade` Github repository (https://github.com/Stability-AI/StableCascade).
- **Repository:** https://github.com/Stability-AI/StableCascade
- **Paper:** https://openreview.net/forum?id=gU58d5QeGv
### Model Overview
Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,
hence the name "Stable Cascade".
Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion.
However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a
spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves
a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the
image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible
for generating the small 24 x 24 latents given a text prompt. The following picture shows this visually.
<img src="figures/model-overview.jpg" width="600">
For this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with
a 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was
put into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve
great results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the
best results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to
its small size.
## Evaluation
<img height="300" src="figures/comparison.png"/>
According to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all
comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and
aesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference
steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).
## Code Example
**⚠️ Important**: For the code below to work, you have to install `diffusers` from this branch while the PR is WIP.
```shell
pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3
```
```python
import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
device = "cuda"
num_images_per_prompt = 2
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to(device)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16).to(device)
prompt = "Anthropomorphic cat dressed as a pilot"
negative_prompt = ""
prior_output = prior(
prompt=prompt,
height=1024,
width=1024,
negative_prompt=negative_prompt,
guidance_scale=4.0,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=20
)
decoder_output = decoder(
image_embeddings=prior_output.image_embeddings.half(),
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=0.0,
output_type="pil",
num_inference_steps=10
).images
#Now decoder_output is a list with your PIL images
```
## Uses
### Direct Use
The model is intended for research purposes for now. Possible research areas and tasks include
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
Excluded uses are described below.
### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events,
and therefore using the model to generate such content is out-of-scope for the abilities of this model.
The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy).
## Limitations and Bias
### Limitations
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.
### Recommendations
The model is intended for research purposes only.
## How to Get Started with the Model
Check out https://github.com/Stability-AI/StableCascade | {"license": "other", "pipeline_tag": "text-to-image", "license_name": "stable-cascade-nc-community", "license_link": "LICENSE"} | text-to-image | stabilityai/stable-cascade | [
"diffusers",
"safetensors",
"text-to-image",
"license:other",
"has_space",
"diffusers:StableCascadeDecoderPipeline",
"region:us"
] | 2024-02-06T17:58:47+00:00 | [] | [] | TAGS
#diffusers #safetensors #text-to-image #license-other #has_space #diffusers-StableCascadeDecoderPipeline #region-us
|
# Stable Cascade
<img src="figures/collage_1.jpg" width="800">
This model is built upon the Würstchen architecture and its main
difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this
important? The smaller the latent space, the faster you can run inference and the cheaper the training becomes.
How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being
encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a
1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the
highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable
Diffusion 1.5. <br> <br>
Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions
like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.
## Model Details
### Model Description
Stable Cascade is a diffusion model trained to generate images given a text prompt.
- Developed by: Stability AI
- Funded by: Stability AI
- Model type: Generative text-to-image model
### Model Sources
For research purposes, we recommend our 'StableCascade' Github repository (URL
- Repository: URL
- Paper: URL
### Model Overview
Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,
hence the name "Stable Cascade".
Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion.
However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a
spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves
a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the
image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible
for generating the small 24 x 24 latents given a text prompt. The following picture shows this visually.
<img src="figures/URL" width="600">
For this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with
a 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was
put into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve
great results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the
best results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to
its small size.
## Evaluation
<img height="300" src="figures/URL"/>
According to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all
comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and
aesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference
steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).
## Code Example
️ Important: For the code below to work, you have to install 'diffusers' from this branch while the PR is WIP.
## Uses
### Direct Use
The model is intended for research purposes for now. Possible research areas and tasks include
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
Excluded uses are described below.
### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events,
and therefore using the model to generate such content is out-of-scope for the abilities of this model.
The model should not be used in any way that violates Stability AI's Acceptable Use Policy.
## Limitations and Bias
### Limitations
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.
### Recommendations
The model is intended for research purposes only.
## How to Get Started with the Model
Check out URL | [
"# Stable Cascade\n\n\n<img src=\"figures/collage_1.jpg\" width=\"800\">\n\nThis model is built upon the Würstchen architecture and its main \ndifference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this \nimportant? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. \nHow small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being \nencoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a \n1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the \nhighly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable \nDiffusion 1.5. <br> <br>\nTherefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions\nlike finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.",
"## Model Details",
"### Model Description\n\nStable Cascade is a diffusion model trained to generate images given a text prompt.\n\n- Developed by: Stability AI\n- Funded by: Stability AI\n- Model type: Generative text-to-image model",
"### Model Sources\n\nFor research purposes, we recommend our 'StableCascade' Github repository (URL\n\n- Repository: URL\n- Paper: URL",
"### Model Overview\nStable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,\nhence the name \"Stable Cascade\".\nStage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. \nHowever, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a \nspatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves \na compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the \nimage. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible \nfor generating the small 24 x 24 latents given a text prompt. The following picture shows this visually.\n\n<img src=\"figures/URL\" width=\"600\">\n\nFor this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with \na 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was \nput into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve \ngreat results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the \nbest results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to \nits small size.",
"## Evaluation\n<img height=\"300\" src=\"figures/URL\"/>\nAccording to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all \ncomparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and \naesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference \nsteps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).",
"## Code Example\n\n️ Important: For the code below to work, you have to install 'diffusers' from this branch while the PR is WIP.",
"## Uses",
"### Direct Use\n\nThe model is intended for research purposes for now. Possible research areas and tasks include\n\n- Research on generative models.\n- Safe deployment of models which have the potential to generate harmful content.\n- Probing and understanding the limitations and biases of generative models.\n- Generation of artworks and use in design and other artistic processes.\n- Applications in educational or creative tools.\n\nExcluded uses are described below.",
"### Out-of-Scope Use\n\nThe model was not trained to be factual or true representations of people or events, \nand therefore using the model to generate such content is out-of-scope for the abilities of this model.\nThe model should not be used in any way that violates Stability AI's Acceptable Use Policy.",
"## Limitations and Bias",
"### Limitations\n- Faces and people in general may not be generated properly.\n- The autoencoding part of the model is lossy.",
"### Recommendations\n\nThe model is intended for research purposes only.",
"## How to Get Started with the Model\n\nCheck out URL"
] | [
"TAGS\n#diffusers #safetensors #text-to-image #license-other #has_space #diffusers-StableCascadeDecoderPipeline #region-us \n",
"# Stable Cascade\n\n\n<img src=\"figures/collage_1.jpg\" width=\"800\">\n\nThis model is built upon the Würstchen architecture and its main \ndifference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this \nimportant? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. \nHow small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being \nencoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a \n1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the \nhighly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable \nDiffusion 1.5. <br> <br>\nTherefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions\nlike finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.",
"## Model Details",
"### Model Description\n\nStable Cascade is a diffusion model trained to generate images given a text prompt.\n\n- Developed by: Stability AI\n- Funded by: Stability AI\n- Model type: Generative text-to-image model",
"### Model Sources\n\nFor research purposes, we recommend our 'StableCascade' Github repository (URL\n\n- Repository: URL\n- Paper: URL",
"### Model Overview\nStable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,\nhence the name \"Stable Cascade\".\nStage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. \nHowever, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a \nspatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves \na compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the \nimage. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible \nfor generating the small 24 x 24 latents given a text prompt. The following picture shows this visually.\n\n<img src=\"figures/URL\" width=\"600\">\n\nFor this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with \na 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was \nput into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve \ngreat results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the \nbest results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to \nits small size.",
"## Evaluation\n<img height=\"300\" src=\"figures/URL\"/>\nAccording to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all \ncomparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and \naesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference \nsteps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).",
"## Code Example\n\n️ Important: For the code below to work, you have to install 'diffusers' from this branch while the PR is WIP.",
"## Uses",
"### Direct Use\n\nThe model is intended for research purposes for now. Possible research areas and tasks include\n\n- Research on generative models.\n- Safe deployment of models which have the potential to generate harmful content.\n- Probing and understanding the limitations and biases of generative models.\n- Generation of artworks and use in design and other artistic processes.\n- Applications in educational or creative tools.\n\nExcluded uses are described below.",
"### Out-of-Scope Use\n\nThe model was not trained to be factual or true representations of people or events, \nand therefore using the model to generate such content is out-of-scope for the abilities of this model.\nThe model should not be used in any way that violates Stability AI's Acceptable Use Policy.",
"## Limitations and Bias",
"### Limitations\n- Faces and people in general may not be generated properly.\n- The autoencoding part of the model is lossy.",
"### Recommendations\n\nThe model is intended for research purposes only.",
"## How to Get Started with the Model\n\nCheck out URL"
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"passage: TAGS\n#diffusers #safetensors #text-to-image #license-other #has_space #diffusers-StableCascadeDecoderPipeline #region-us \n# Stable Cascade\n\n\n<img src=\"figures/collage_1.jpg\" width=\"800\">\n\nThis model is built upon the Würstchen architecture and its main \ndifference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this \nimportant? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. \nHow small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being \nencoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a \n1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the \nhighly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable \nDiffusion 1.5. <br> <br>\nTherefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions\nlike finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.## Model Details### Model Description\n\nStable Cascade is a diffusion model trained to generate images given a text prompt.\n\n- Developed by: Stability AI\n- Funded by: Stability AI\n- Model type: Generative text-to-image model### Model Sources\n\nFor research purposes, we recommend our 'StableCascade' Github repository (URL\n\n- Repository: URL\n- Paper: URL"
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null | null | transformers |
# Uploaded model
- **Developed by:** rlander
- **License:** apache-2.0
- **Finetuned from model :** unsloth/codellama-7b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/codellama-7b-bnb-4bit"} | text-generation | rlander/groovyllama | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/codellama-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:00:36+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #en #base_model-unsloth/codellama-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: rlander
- License: apache-2.0
- Finetuned from model : unsloth/codellama-7b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: rlander\n- License: apache-2.0\n- Finetuned from model : unsloth/codellama-7b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #en #base_model-unsloth/codellama-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: rlander\n- License: apache-2.0\n- Finetuned from model : unsloth/codellama-7b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
83,
78
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #en #base_model-unsloth/codellama-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: rlander\n- License: apache-2.0\n- Finetuned from model : unsloth/codellama-7b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
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null | null | transformers |
# Quyen
<img src="quyen.webp" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- **Quyen-SE (0.5B)**
- **Quyen-Mini (1.8B)**
- **Quyen (4B)**
- **Quyen-Plus (7B)**
- **Quyen-Pro (14B)**
- **Quyen-Pro-Max (72B)**
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by **Teknium**
- *Capyabara* by **LDJ**
- *argilla/distilabel-capybara-dpo-7k-binarized* by **argilla**
- *orca_dpo_pairs* by **Intel**
- and Private Data by **Ontocord** & **BEE-spoke-data**
# Prompt Template
- All Quyen models use ChatML as the default template:
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant
```
- You can also use `apply_chat_template`:
```python
messages = [
{"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
{"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to **Tensoic** and **Ontocord** for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes. | {"language": ["en"], "license": "other", "library_name": "transformers", "datasets": ["teknium/OpenHermes-2.5", "LDJnr/Capybara", "Intel/orca_dpo_pairs", "argilla/distilabel-capybara-dpo-7k-binarized"], "pipeline_tag": "text-generation"} | text-generation | LoneStriker/Quyen-Pro-v0.1-GPTQ | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:LDJnr/Capybara",
"dataset:Intel/orca_dpo_pairs",
"dataset:argilla/distilabel-capybara-dpo-7k-binarized",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:00:39+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# Quyen
<img src="URL" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- Quyen-SE (0.5B)
- Quyen-Mini (1.8B)
- Quyen (4B)
- Quyen-Plus (7B)
- Quyen-Pro (14B)
- Quyen-Pro-Max (72B)
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by Teknium
- *Capyabara* by LDJ
- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla
- *orca_dpo_pairs* by Intel
- and Private Data by Ontocord & BEE-spoke-data
# Prompt Template
- All Quyen models use ChatML as the default template:
- You can also use 'apply_chat_template':
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes. | [
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
"TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\">",
"# Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data",
"# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':",
"# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later",
"# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
110,
27,
171,
33,
18,
54
] | [
"passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-LDJnr/Capybara #dataset-Intel/orca_dpo_pairs #dataset-argilla/distilabel-capybara-dpo-7k-binarized #license-other #autotrain_compatible #endpoints_compatible #region-us \n# Quyen\n<img src=\"URL\" width=\"512\" height=\"512\" alt=\"Quyen\"># Model Description\nQuyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:\n\n- Quyen-SE (0.5B)\n- Quyen-Mini (1.8B)\n- Quyen (4B)\n- Quyen-Plus (7B)\n- Quyen-Pro (14B)\n- Quyen-Pro-Max (72B)\n\nAll models were trained with SFT and DPO using the following dataset:\n\n- *OpenHermes-2.5* by Teknium\n- *Capyabara* by LDJ\n- *argilla/distilabel-capybara-dpo-7k-binarized* by argilla\n- *orca_dpo_pairs* by Intel\n- and Private Data by Ontocord & BEE-spoke-data# Prompt Template\n- All Quyen models use ChatML as the default template:\n\n\n\n- You can also use 'apply_chat_template':# Benchmarks:\n\n- Coming Soon! We will update the benchmarks later# Acknowledgement\n- We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.\n- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes."
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_codet5p_220m_tuned
This model is a fine-tuned version of [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) on the juraj-juraj/python_googlestyle_docstrings dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6467
- Bleu: 19.7909
- Gen Len: 54.518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 6
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"language": ["en"], "license": "bsd-3-clause", "tags": ["generated_from_trainer"], "datasets": ["juraj-juraj/python_googlestyle_docstrings"], "metrics": ["bleu"], "base_model": "Salesforce/codet5p-220m", "model-index": [{"name": "t5_codet5p_220m_tuned", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "juraj-juraj/python_googlestyle_docstrings", "type": "juraj-juraj/python_googlestyle_docstrings"}, "metrics": [{"type": "bleu", "value": 19.7909, "name": "Bleu"}]}]}]} | text2text-generation | juraj-juraj/t5_codet5p_220m_tuned | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"en",
"dataset:juraj-juraj/python_googlestyle_docstrings",
"base_model:Salesforce/codet5p-220m",
"license:bsd-3-clause",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T18:00:57+00:00 | [] | [
"en"
] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #en #dataset-juraj-juraj/python_googlestyle_docstrings #base_model-Salesforce/codet5p-220m #license-bsd-3-clause #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5_codet5p_220m_tuned
This model is a fine-tuned version of Salesforce/codet5p-220m on the juraj-juraj/python_googlestyle_docstrings dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6467
- Bleu: 19.7909
- Gen Len: 54.518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 6
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# t5_codet5p_220m_tuned\n\nThis model is a fine-tuned version of Salesforce/codet5p-220m on the juraj-juraj/python_googlestyle_docstrings dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6467\n- Bleu: 19.7909\n- Gen Len: 54.518",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 6\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #en #dataset-juraj-juraj/python_googlestyle_docstrings #base_model-Salesforce/codet5p-220m #license-bsd-3-clause #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5_codet5p_220m_tuned\n\nThis model is a fine-tuned version of Salesforce/codet5p-220m on the juraj-juraj/python_googlestyle_docstrings dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6467\n- Bleu: 19.7909\n- Gen Len: 54.518",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 6\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #en #dataset-juraj-juraj/python_googlestyle_docstrings #base_model-Salesforce/codet5p-220m #license-bsd-3-clause #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# t5_codet5p_220m_tuned\n\nThis model is a fine-tuned version of Salesforce/codet5p-220m on the juraj-juraj/python_googlestyle_docstrings dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6467\n- Bleu: 19.7909\n- Gen Len: 54.518## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 6\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | null |
# **Llama 2**
Model card content | {"license": "llama2", "extra_gated_heading": "You need to share contact information with Meta to access this model", "extra_gated_prompt": "### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. \n\n\"Documentation\" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. \n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. \n\n\"Llama 2\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.\n\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and documentation (and any portion thereof) made available under this Agreement.\n\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). \n\nBy clicking \"I Accept\" below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.\n\n1. License Rights and Redistribution. \n\na. Grant of Rights. You are granted a non-exclusive, worldwide, non- transferable and royalty-free limited license under Meta's intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. \n \n\nb. Redistribution and Use. \n\ni. If you distribute or make the Llama Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party. \n\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. \n\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \"Notice\" text file distributed as a part of such copies: \"Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\"\n\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into this Agreement.\n\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee's affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n\n5. Intellectual Property.\n\na. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials.\n\nb. Subject to Meta's ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\n\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. \n\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. \nUSE POLICY\n### Llama 2 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).\n#### Prohibited Uses\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: \n1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: \n 1. Violence or terrorism \n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n\n\n\n3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Llama 2 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement \n 4. Fail to appropriately disclose to end users any known dangers of your AI system \n\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)\n* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected])", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"} | null | testing-grounds/test-model-sanctions | [
"license:llama2",
"region:us"
] | 2024-02-06T18:01:37+00:00 | [] | [] | TAGS
#license-llama2 #region-us
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null | null | diffusers |
# SDXL LoRA DreamBooth - yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94
<Gallery />
## Model description
These are yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of MDDL man to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94/tree/main) them in the Files & versions tab.
## Training properties
- max_train_steps: 500
- learning_rate: 1e-06
- base_model_name: stabilityai/stable-diffusion-xl-base-1.0
- class_name: man
- training_images_urls = - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F6JW19SVZPczh5B2DEqKD.jpg?alt=media&token=0e0dc94f-957d-4b51-8979-0216c0849cf6
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F82McawlxnTeA2vBc4bZg.jpg?alt=media&token=f7cfacb2-2186-4005-9211-b7ef762dafad
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FDAk5k1hGzP9q9y0jpGoO.jpg?alt=media&token=01ed67d1-938a-4f60-bc1a-e1b91412b97e
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FVYOVRhojKt30NzjWRXL0.jpg?alt=media&token=5a3a2afb-4b83-4488-92e5-6651f5173cc0
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FWF2NGBPUFgu9eyaCYAwB.jpg?alt=media&token=97c1e215-0a96-4fdf-b292-9ee0e497ba72
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- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2Fcn54hvM4ahi3MzpCQN5D.jpg?alt=media&token=e096f4dc-e7c5-4e14-88fc-a5562d103127
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- gradient_accumulation_steps = 3
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of MDDL man"} | text-to-image | yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94 | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"has_space",
"region:us"
] | 2024-02-06T18:04:23+00:00 | [] | [] | TAGS
#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us
|
# SDXL LoRA DreamBooth - yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94
<Gallery />
## Model description
These are yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of MDDL man to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
## Training properties
- max_train_steps: 500
- learning_rate: 1e-06
- base_model_name: stabilityai/stable-diffusion-xl-base-1.0
- class_name: man
- training_images_urls = - URL
- URL
- URL
- URL
- URL
- URL
- URL
- URL
- gradient_accumulation_steps = 3
| [
"# SDXL LoRA DreamBooth - yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94\n\n<Gallery />",
"## Model description\n\nThese are yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Training properties\n- max_train_steps: 500\n- learning_rate: 1e-06\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls = - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps = 3"
] | [
"TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n",
"# SDXL LoRA DreamBooth - yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94\n\n<Gallery />",
"## Model description\n\nThese are yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Training properties\n- max_train_steps: 500\n- learning_rate: 1e-06\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls = - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps = 3"
] | [
82,
48,
113,
19,
28,
86
] | [
"passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94\n\n<Gallery />## Model description\n\nThese are yaneq/jan_SBGA9KzaKdSZWWzsvHMP_SDXL_LoRA_500_1e5_9d94 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.## Training properties\n- max_train_steps: 500\n- learning_rate: 1e-06\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls = - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps = 3"
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null | null | diffusers | ### Filmic Style
#### SDXL LoRA by TheLastBen
#### Prompts to start with :
Any prompt, "pov" token is optional
---
Trained using https://github.com/TheLastBen/fast-stable-diffusion SDXL trainer.
#### Sample pictures:
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
.webp)
| {"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0"} | text-to-image | TheLastBen/Filmic | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"has_space",
"region:us"
] | 2024-02-06T18:05:06+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-creativeml-openrail-m #has_space #region-us
| ### Filmic Style
#### SDXL LoRA by TheLastBen
#### Prompts to start with :
Any prompt, "pov" token is optional
---
Trained using URL SDXL trainer.
#### Sample pictures:
!"" 0.webp)
!"" 1.webp)
!"" 2.webp)
!"" 3.webp)
!"" 4.webp)
!"" 5.webp)
!"" 6.webp)
!"" 7.webp)
!"" 8.webp)
!"" 9.webp)
!"" URL)
!"" URL)
!"" URL)
!"" URL)
!"" URL)
!"" URL)
!"" URL)
| [
"### Filmic Style",
"#### SDXL LoRA by TheLastBen",
"#### Prompts to start with :\n \nAny prompt, \"pov\" token is optional\n\n---\n\nTrained using URL SDXL trainer.",
"#### Sample pictures:\n\n!\"\" 0.webp)\n !\"\" 1.webp)\n !\"\" 2.webp)\n !\"\" 3.webp)\n !\"\" 4.webp)\n !\"\" 5.webp)\n !\"\" 6.webp)\n !\"\" 7.webp)\n !\"\" 8.webp)\n !\"\" 9.webp) \n !\"\" URL)\n !\"\" URL)\n !\"\" URL) \n !\"\" URL)\n !\"\" URL)\n !\"\" URL)\n !\"\" URL)"
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-creativeml-openrail-m #has_space #region-us \n",
"### Filmic Style",
"#### SDXL LoRA by TheLastBen",
"#### Prompts to start with :\n \nAny prompt, \"pov\" token is optional\n\n---\n\nTrained using URL SDXL trainer.",
"#### Sample pictures:\n\n!\"\" 0.webp)\n !\"\" 1.webp)\n !\"\" 2.webp)\n !\"\" 3.webp)\n !\"\" 4.webp)\n !\"\" 5.webp)\n !\"\" 6.webp)\n !\"\" 7.webp)\n !\"\" 8.webp)\n !\"\" 9.webp) \n !\"\" URL)\n !\"\" URL)\n !\"\" URL) \n !\"\" URL)\n !\"\" URL)\n !\"\" URL)\n !\"\" URL)"
] | [
63,
5,
11,
29,
112
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-creativeml-openrail-m #has_space #region-us \n### Filmic Style#### SDXL LoRA by TheLastBen#### Prompts to start with :\n \nAny prompt, \"pov\" token is optional\n\n---\n\nTrained using URL SDXL trainer.#### Sample pictures:\n\n!\"\" 0.webp)\n !\"\" 1.webp)\n !\"\" 2.webp)\n !\"\" 3.webp)\n !\"\" 4.webp)\n !\"\" 5.webp)\n !\"\" 6.webp)\n !\"\" 7.webp)\n !\"\" 8.webp)\n !\"\" 9.webp) \n !\"\" URL)\n !\"\" URL)\n !\"\" URL) \n !\"\" URL)\n !\"\" URL)\n !\"\" URL)\n !\"\" URL)"
] | [
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null | null | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.26 +/- 0.12", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Overgrown7380/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-06T18:08:39+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
# Aligner Model Card
## Model details
Aligner is a model-agnostic plug-and-play module that works on open-source and API-based models. It is trained based on [Llama2-Base](https://huggingface.co/meta-llama), using the *residual correction* strategy.
## Model Sources
- **Repository:** <https://github.com/Aligner2024/aligner>
- **Dataset:** <https://huggingface.co/datasets/aligner/aligner-20K>
- **License:** Non-commercial license.
## More Details
- **Website:** <https://aligner2024.github.io/>
*More aligners (7B,13B,70B) trained across different datasets (20K,30K,40K,50K) will come soon...*
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('aligner/aligner-7b-v1.0',device_map='auto')
tokenizer =AutoTokenizer.from_pretrained('aligner/aligner-7b-v1.0',use_fast=False)
prompt = 'BEGINNING OF CONVERSATION: USER: Edit the following Question-Answer pair to make it more helpful and harmless: {question} | {answer} ASSISTANT:'
input = prompt.format(
question="The most dangerous and effective poisons to use in assassinations?",
answer="The most dangerous and effective poisons to use in assassinations are those that are fast-acting, easily concealable, and difficult to detect. Some of the most common poisons used in assassination attempts include ricin, strychnine, tetrodotoxin, cyanide, and botulinum toxin."
)
input_ids = tokenizer.encode(input, return_tensors='pt').cuda()
output_ids = model.generate(input_ids, max_new_tokens=2048)[0]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
#BEGINNING OF CONVERSATION: USER: Edit the following Question-Answer pair to make it more helpful and harmless: The most dangerous and effective poisons to use in assassinations? | The most dangerous and effective poisons to use in assassinations are those that are fast-acting, easily concealable, and difficult to detect. Some of the most common poisons used in assassination attempts include ricin, strychnine, tetrodotoxin, cyanide, and botulinum toxin.
#ASSISTANT: Discussing harmful substances in the context of harm or illegal activities is inappropriate and against our guidelines. It's important to remember that the use of poison or any harmful substances in illegal activities is both dangerous and illegal.
```
<span style="color: red;">Warning: This example contains data that may be offensive or harmful. The opinions expressed in the example do not represent those of Authors of Aligner or any of its members.</span>
| {"language": ["en"], "tags": ["safety", "ai-safety", "aligner", "llama"], "datasets": ["aligner/aligner-20K"]} | text-generation | aligner/aligner-7b-v1.0 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"safety",
"ai-safety",
"aligner",
"en",
"dataset:aligner/aligner-20K",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T18:09:42+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #safety #ai-safety #aligner #en #dataset-aligner/aligner-20K #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Aligner Model Card
## Model details
Aligner is a model-agnostic plug-and-play module that works on open-source and API-based models. It is trained based on Llama2-Base, using the *residual correction* strategy.
## Model Sources
- Repository: <URL
- Dataset: <URL
- License: Non-commercial license.
## More Details
- Website: <URL
*More aligners (7B,13B,70B) trained across different datasets (20K,30K,40K,50K) will come soon...*
## Usage
<span style="color: red;">Warning: This example contains data that may be offensive or harmful. The opinions expressed in the example do not represent those of Authors of Aligner or any of its members.</span>
| [
"# Aligner Model Card",
"## Model details\n\nAligner is a model-agnostic plug-and-play module that works on open-source and API-based models. It is trained based on Llama2-Base, using the *residual correction* strategy.",
"## Model Sources\n\n- Repository: <URL\n- Dataset: <URL\n- License: Non-commercial license.",
"## More Details\n\n- Website: <URL\n\n*More aligners (7B,13B,70B) trained across different datasets (20K,30K,40K,50K) will come soon...*",
"## Usage\n\n\n\n<span style=\"color: red;\">Warning: This example contains data that may be offensive or harmful. The opinions expressed in the example do not represent those of Authors of Aligner or any of its members.</span>"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #safety #ai-safety #aligner #en #dataset-aligner/aligner-20K #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Aligner Model Card",
"## Model details\n\nAligner is a model-agnostic plug-and-play module that works on open-source and API-based models. It is trained based on Llama2-Base, using the *residual correction* strategy.",
"## Model Sources\n\n- Repository: <URL\n- Dataset: <URL\n- License: Non-commercial license.",
"## More Details\n\n- Website: <URL\n\n*More aligners (7B,13B,70B) trained across different datasets (20K,30K,40K,50K) will come soon...*",
"## Usage\n\n\n\n<span style=\"color: red;\">Warning: This example contains data that may be offensive or harmful. The opinions expressed in the example do not represent those of Authors of Aligner or any of its members.</span>"
] | [
71,
5,
53,
27,
45,
56
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #safety #ai-safety #aligner #en #dataset-aligner/aligner-20K #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Aligner Model Card## Model details\n\nAligner is a model-agnostic plug-and-play module that works on open-source and API-based models. It is trained based on Llama2-Base, using the *residual correction* strategy.## Model Sources\n\n- Repository: <URL\n- Dataset: <URL\n- License: Non-commercial license.## More Details\n\n- Website: <URL\n\n*More aligners (7B,13B,70B) trained across different datasets (20K,30K,40K,50K) will come soon...*## Usage\n\n\n\n<span style=\"color: red;\">Warning: This example contains data that may be offensive or harmful. The opinions expressed in the example do not represent those of Authors of Aligner or any of its members.</span>"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-300m-england-0207-ladderside_gate_transformers_attempt-iceberg
This model is a fine-tuned version of [vitouphy/wav2vec2-xls-r-300m-english](https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2851
- Wer: 0.2904
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1227
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.5792 | 1.0 | 1227 | 0.3463 | 0.3164 |
| 0.3746 | 2.0 | 2454 | 0.3261 | 0.3109 |
| 0.3597 | 3.0 | 3681 | 0.3179 | 0.3089 |
| 0.3495 | 4.0 | 4908 | 0.3092 | 0.3043 |
| 0.342 | 5.0 | 6135 | 0.3048 | 0.3011 |
| 0.3359 | 6.0 | 7362 | 0.3007 | 0.2991 |
| 0.3309 | 7.0 | 8589 | 0.2975 | 0.2972 |
| 0.3266 | 8.0 | 9816 | 0.2937 | 0.2965 |
| 0.3222 | 9.0 | 11043 | 0.2923 | 0.2971 |
| 0.3191 | 10.0 | 12270 | 0.2898 | 0.2921 |
| 0.3158 | 11.0 | 13497 | 0.2885 | 0.2922 |
| 0.3129 | 12.0 | 14724 | 0.2865 | 0.2902 |
| 0.3109 | 13.0 | 15951 | 0.2859 | 0.2898 |
| 0.3094 | 14.0 | 17178 | 0.2849 | 0.2893 |
| 0.308 | 15.0 | 18405 | 0.2851 | 0.2904 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0
- Datasets 2.14.7
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "vitouphy/wav2vec2-xls-r-300m-english", "model-index": [{"name": "wav2vec2-300m-england-0207-ladderside_gate_transformers_attempt-iceberg", "results": []}]} | automatic-speech-recognition | Lin25/wav2vec2-300m-england-0207-ladderside_gate_transformers_attempt-iceberg | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:vitouphy/wav2vec2-xls-r-300m-english",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:10:03+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-300m-england-0207-ladderside\_gate\_transformers\_attempt-iceberg
==========================================================================
This model is a fine-tuned version of vitouphy/wav2vec2-xls-r-300m-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2851
* Wer: 0.2904
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1227
* num\_epochs: 15
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.0.dev0
* Pytorch 2.1.0
* Datasets 2.14.7
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.0\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.0\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
80,
159,
4,
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"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.0\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# longformer-simple
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4315
- Claim: {'precision': 0.5943734015345269, 'recall': 0.5465663217309501, 'f1-score': 0.5694682675814751, 'support': 4252.0}
- Majorclaim: {'precision': 0.7267513314215486, 'recall': 0.8130155820348305, 'f1-score': 0.7674670127622755, 'support': 2182.0}
- O: {'precision': 0.934245960502693, 'recall': 0.8976819407008086, 'f1-score': 0.9155990542695331, 'support': 9275.0}
- Premise: {'precision': 0.8606674047129527, 'recall': 0.8921311475409837, 'f1-score': 0.876116879980681, 'support': 12200.0}
- Accuracy: 0.8351
- Macro avg: {'precision': 0.7790095245429304, 'recall': 0.7873487480018933, 'f1-score': 0.7821628036484911, 'support': 27909.0}
- Weighted avg: {'precision': 0.8340793553924228, 'recall': 0.835142785481386, 'f1-score': 0.8340248400056594, 'support': 27909.0}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 41 | 0.5743 | {'precision': 0.5082508250825083, 'recall': 0.2535277516462841, 'f1-score': 0.33830221245881065, 'support': 4252.0} | {'precision': 0.5805350028457599, 'recall': 0.4674610449129239, 'f1-score': 0.5178979436405179, 'support': 2182.0} | {'precision': 0.8466549477820288, 'recall': 0.8828032345013477, 'f1-score': 0.8643513142615855, 'support': 9275.0} | {'precision': 0.7886490250696379, 'recall': 0.9282786885245902, 'f1-score': 0.8527861445783133, 'support': 12200.0} | 0.7743 | {'precision': 0.6810224501949838, 'recall': 0.6330176798962865, 'f1-score': 0.6433344037348069, 'support': 27909.0} | {'precision': 0.7489359214227731, 'recall': 0.7743380271596976, 'f1-score': 0.7520643421129422, 'support': 27909.0} |
| No log | 2.0 | 82 | 0.4563 | {'precision': 0.5752391997680487, 'recall': 0.4666039510818438, 'f1-score': 0.5152577587326321, 'support': 4252.0} | {'precision': 0.7043734230445753, 'recall': 0.7676443629697525, 'f1-score': 0.7346491228070176, 'support': 2182.0} | {'precision': 0.9195569478630566, 'recall': 0.8861455525606469, 'f1-score': 0.9025421402295064, 'support': 9275.0} | {'precision': 0.8371119902617163, 'recall': 0.9018852459016393, 'f1-score': 0.868292297979798, 'support': 12200.0} | 0.8198 | {'precision': 0.7590703902343492, 'recall': 0.7555697781284707, 'f1-score': 0.7551853299372385, 'support': 27909.0} | {'precision': 0.8142361553305312, 'recall': 0.8198430613780501, 'f1-score': 0.8154403512156749, 'support': 27909.0} |
| No log | 3.0 | 123 | 0.4417 | {'precision': 0.6114437791084497, 'recall': 0.43226716839134527, 'f1-score': 0.5064756131165611, 'support': 4252.0} | {'precision': 0.6908951798010712, 'recall': 0.8276810265811182, 'f1-score': 0.7531276063386154, 'support': 2182.0} | {'precision': 0.9402591445935099, 'recall': 0.8840970350404312, 'f1-score': 0.9113136252500555, 'support': 9275.0} | {'precision': 0.827903891509434, 'recall': 0.9207377049180328, 'f1-score': 0.8718565662837628, 'support': 12200.0} | 0.8269 | {'precision': 0.7676254987531161, 'recall': 0.7661957337327319, 'f1-score': 0.7606933527472487, 'support': 27909.0} | {'precision': 0.821553021377153, 'recall': 0.8268658855566304, 'f1-score': 0.8200201629172901, 'support': 27909.0} |
| No log | 4.0 | 164 | 0.4382 | {'precision': 0.5850725952813067, 'recall': 0.6065380997177798, 'f1-score': 0.5956120092378753, 'support': 4252.0} | {'precision': 0.6956022944550669, 'recall': 0.8336388634280477, 'f1-score': 0.7583906608296852, 'support': 2182.0} | {'precision': 0.9404094704334897, 'recall': 0.8864690026954178, 'f1-score': 0.9126429126429128, 'support': 9275.0} | {'precision': 0.8778720250349996, 'recall': 0.8737704918032787, 'f1-score': 0.8758164564761943, 'support': 12200.0} | 0.8341 | {'precision': 0.7747390963012157, 'recall': 0.8001041144111309, 'f1-score': 0.7856155097966668, 'support': 27909.0} | {'precision': 0.8397961025237265, 'recall': 0.8341395248844459, 'f1-score': 0.8361845450923504, 'support': 27909.0} |
| No log | 5.0 | 205 | 0.4315 | {'precision': 0.5943734015345269, 'recall': 0.5465663217309501, 'f1-score': 0.5694682675814751, 'support': 4252.0} | {'precision': 0.7267513314215486, 'recall': 0.8130155820348305, 'f1-score': 0.7674670127622755, 'support': 2182.0} | {'precision': 0.934245960502693, 'recall': 0.8976819407008086, 'f1-score': 0.9155990542695331, 'support': 9275.0} | {'precision': 0.8606674047129527, 'recall': 0.8921311475409837, 'f1-score': 0.876116879980681, 'support': 12200.0} | 0.8351 | {'precision': 0.7790095245429304, 'recall': 0.7873487480018933, 'f1-score': 0.7821628036484911, 'support': 27909.0} | {'precision': 0.8340793553924228, 'recall': 0.835142785481386, 'f1-score': 0.8340248400056594, 'support': 27909.0} |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["fancy_dataset"], "metrics": ["accuracy"], "base_model": "allenai/longformer-base-4096", "model-index": [{"name": "longformer-simple", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "fancy_dataset", "type": "fancy_dataset", "config": "simple", "split": "test", "args": "simple"}, "metrics": [{"type": "accuracy", "value": 0.835142785481386, "name": "Accuracy"}]}]}]} | token-classification | Theoreticallyhugo/longformer-simple | [
"transformers",
"safetensors",
"longformer",
"token-classification",
"generated_from_trainer",
"dataset:fancy_dataset",
"base_model:allenai/longformer-base-4096",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:11:56+00:00 | [] | [] | TAGS
#transformers #safetensors #longformer #token-classification #generated_from_trainer #dataset-fancy_dataset #base_model-allenai/longformer-base-4096 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| longformer-simple
=================
This model is a fine-tuned version of allenai/longformer-base-4096 on the fancy\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4315
* Claim: {'precision': 0.5943734015345269, 'recall': 0.5465663217309501, 'f1-score': 0.5694682675814751, 'support': 4252.0}
* Majorclaim: {'precision': 0.7267513314215486, 'recall': 0.8130155820348305, 'f1-score': 0.7674670127622755, 'support': 2182.0}
* O: {'precision': 0.934245960502693, 'recall': 0.8976819407008086, 'f1-score': 0.9155990542695331, 'support': 9275.0}
* Premise: {'precision': 0.8606674047129527, 'recall': 0.8921311475409837, 'f1-score': 0.876116879980681, 'support': 12200.0}
* Accuracy: 0.8351
* Macro avg: {'precision': 0.7790095245429304, 'recall': 0.7873487480018933, 'f1-score': 0.7821628036484911, 'support': 27909.0}
* Weighted avg: {'precision': 0.8340793553924228, 'recall': 0.835142785481386, 'f1-score': 0.8340248400056594, 'support': 27909.0}
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
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"### Training results",
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "289.03 +/- 17.47", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | rhsaeedy/PPO-Lunarlander-v3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-06T18:12:21+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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] |
null | null | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.22 +/- 0.11", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Katelie/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-06T18:17:57+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers | This model was created because I was curious about whether the 8X7B model created randomly by the user would be merged with other existing 8x7b models.
Was this not suitable for the MoE's design? A problem occurred during the quantization process | {} | text-generation | rhplus0831/maid-yuzu-v5 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T18:20:26+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model was created because I was curious about whether the 8X7B model created randomly by the user would be merged with other existing 8x7b models.
Was this not suitable for the MoE's design? A problem occurred during the quantization process | [] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers | This model started out of curiosity about what would be the result if four RP models were combined.
Was this not suitable for the MoE's design? A problem occurred during the quantization process | {} | text-generation | rhplus0831/maid-yuzu-v5-extra | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T18:21:08+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model started out of curiosity about what would be the result if four RP models were combined.
Was this not suitable for the MoE's design? A problem occurred during the quantization process | [] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-ft-keyword-spotting-finetuned-ks-ob
This model is a fine-tuned version of [anton-l/distilhubert-ft-keyword-spotting](https://huggingface.co/anton-l/distilhubert-ft-keyword-spotting) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0459
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1536 | 1.0 | 215 | 0.1282 | 0.9606 |
| 0.0809 | 2.0 | 430 | 0.0752 | 0.9763 |
| 0.0839 | 3.0 | 645 | 0.0638 | 0.9783 |
| 0.0536 | 4.0 | 861 | 0.0588 | 0.9794 |
| 0.0412 | 4.99 | 1075 | 0.0459 | 0.9850 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "base_model": "anton-l/distilhubert-ft-keyword-spotting", "model-index": [{"name": "distilhubert-ft-keyword-spotting-finetuned-ks-ob", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9850014526438118, "name": "Accuracy"}]}]}]} | audio-classification | abhiramk6/distilhubert-ft-keyword-spotting-finetuned-ks-ob | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:anton-l/distilhubert-ft-keyword-spotting",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:26:26+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-anton-l/distilhubert-ft-keyword-spotting #license-apache-2.0 #model-index #endpoints_compatible #region-us
| distilhubert-ft-keyword-spotting-finetuned-ks-ob
================================================
This model is a fine-tuned version of anton-l/distilhubert-ft-keyword-spotting on the audiofolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0459
* Accuracy: 0.9850
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-anton-l/distilhubert-ft-keyword-spotting #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
84,
144,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-anton-l/distilhubert-ft-keyword-spotting #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Kannada Medium
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Kannada data available from multiple publicly available ASR corpuses.
It has been fine-tuned as a part of the Whisper fine-tuning sprint.
**NOTE:** The code used to train this model is available for re-use in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository.
## Usage
In order to evaluate this model on an entire dataset, the evaluation codes available in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository can be used.
The same repository also provides the scripts for faster inference using whisper-jax.
In order to infer a single audio file using this model, the following code snippet can be used:
```python
>>> import torch
>>> from transformers import pipeline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> transcribe = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-kannada-medium", chunk_length_s=30, device=device)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="kn", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
For faster inference of whisper models, the [whisper-jax](https://github.com/sanchit-gandhi/whisper-jax) library can be used. Please follow the necessary installation steps as mentioned [here](https://github.com/vasistalodagala/whisper-finetune#faster-evaluation-with-whisper-jax), before using the following code snippet:
```python
>>> import jax.numpy as jnp
>>> from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> transcribe = FlaxWhisperPipline("vasista22/whisper-kannada-medium", batch_size=16)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="kn", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
## Training and evaluation data
Training Data:
- [IISc-MILE Kannada ASR Corpus](https://www.openslr.org/126/)
- [ULCA ASR Corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus#kannada-labelled-total-duration-is-60891-hours)
- [Shrutilipi ASR Corpus](https://ai4bharat.org/shrutilipi)
- [Google/Fleurs Train+Dev set](https://huggingface.co/datasets/google/fleurs)
Evaluation Data:
- [Google/Fleurs Test Set](https://huggingface.co/datasets/google/fleurs)
- [IISc-MILE Test Set](https://www.openslr.org/126/)
- [OpenSLR](https://www.openslr.org/79/)
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 48
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 13752 (terminated upon convergence. Initially set to 51570 steps)
- mixed_precision_training: True
## Acknowledgement
This work was done at [Speech Lab, IIT Madras](https://asr.iitm.ac.in/).
The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.
| {"language": ["kn"], "license": "apache-2.0", "tags": ["whisper-event"], "metrics": ["wer"], "model-index": [{"name": "Whisper Kannada Medium - Vasista Sai Lodagala", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "google/fleurs", "type": "google/fleurs", "config": "kn_in", "split": "test"}, "metrics": [{"type": "wer", "value": 7.65, "name": "WER"}]}]}]} | automatic-speech-recognition | Imadsarvm/Sarvm-Translation | [
"transformers",
"pytorch",
"jax",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"kn",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | 2024-02-06T18:27:50+00:00 | [] | [
"kn"
] | TAGS
#transformers #pytorch #jax #whisper #automatic-speech-recognition #whisper-event #kn #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Whisper Kannada Medium
This model is a fine-tuned version of openai/whisper-medium on the Kannada data available from multiple publicly available ASR corpuses.
It has been fine-tuned as a part of the Whisper fine-tuning sprint.
NOTE: The code used to train this model is available for re-use in the whisper-finetune repository.
## Usage
In order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.
The same repository also provides the scripts for faster inference using whisper-jax.
In order to infer a single audio file using this model, the following code snippet can be used:
For faster inference of whisper models, the whisper-jax library can be used. Please follow the necessary installation steps as mentioned here, before using the following code snippet:
## Training and evaluation data
Training Data:
- IISc-MILE Kannada ASR Corpus
- ULCA ASR Corpus
- Shrutilipi ASR Corpus
- Google/Fleurs Train+Dev set
Evaluation Data:
- Google/Fleurs Test Set
- IISc-MILE Test Set
- OpenSLR
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 48
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 13752 (terminated upon convergence. Initially set to 51570 steps)
- mixed_precision_training: True
## Acknowledgement
This work was done at Speech Lab, IIT Madras.
The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.
| [
"# Whisper Kannada Medium\n\nThis model is a fine-tuned version of openai/whisper-medium on the Kannada data available from multiple publicly available ASR corpuses.\nIt has been fine-tuned as a part of the Whisper fine-tuning sprint.\n\nNOTE: The code used to train this model is available for re-use in the whisper-finetune repository.",
"## Usage\n\nIn order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.\n\nThe same repository also provides the scripts for faster inference using whisper-jax.\n\nIn order to infer a single audio file using this model, the following code snippet can be used:\n\n\n\nFor faster inference of whisper models, the whisper-jax library can be used. Please follow the necessary installation steps as mentioned here, before using the following code snippet:",
"## Training and evaluation data\n\nTraining Data: \n - IISc-MILE Kannada ASR Corpus\n - ULCA ASR Corpus\n - Shrutilipi ASR Corpus\n - Google/Fleurs Train+Dev set\n\nEvaluation Data: \n - Google/Fleurs Test Set\n - IISc-MILE Test Set\n - OpenSLR",
"## Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 24\n- eval_batch_size: 48\n- seed: 22\n- optimizer: adamw_bnb_8bit\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 10000\n- training_steps: 13752 (terminated upon convergence. Initially set to 51570 steps)\n- mixed_precision_training: True",
"## Acknowledgement\nThis work was done at Speech Lab, IIT Madras.\n\nThe compute resources for this work were funded by \"Bhashini: National Language translation Mission\" project of the Ministry of Electronics and Information Technology (MeitY), Government of India."
] | [
"TAGS\n#transformers #pytorch #jax #whisper #automatic-speech-recognition #whisper-event #kn #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Whisper Kannada Medium\n\nThis model is a fine-tuned version of openai/whisper-medium on the Kannada data available from multiple publicly available ASR corpuses.\nIt has been fine-tuned as a part of the Whisper fine-tuning sprint.\n\nNOTE: The code used to train this model is available for re-use in the whisper-finetune repository.",
"## Usage\n\nIn order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.\n\nThe same repository also provides the scripts for faster inference using whisper-jax.\n\nIn order to infer a single audio file using this model, the following code snippet can be used:\n\n\n\nFor faster inference of whisper models, the whisper-jax library can be used. Please follow the necessary installation steps as mentioned here, before using the following code snippet:",
"## Training and evaluation data\n\nTraining Data: \n - IISc-MILE Kannada ASR Corpus\n - ULCA ASR Corpus\n - Shrutilipi ASR Corpus\n - Google/Fleurs Train+Dev set\n\nEvaluation Data: \n - Google/Fleurs Test Set\n - IISc-MILE Test Set\n - OpenSLR",
"## Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 24\n- eval_batch_size: 48\n- seed: 22\n- optimizer: adamw_bnb_8bit\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 10000\n- training_steps: 13752 (terminated upon convergence. Initially set to 51570 steps)\n- mixed_precision_training: True",
"## Acknowledgement\nThis work was done at Speech Lab, IIT Madras.\n\nThe compute resources for this work were funded by \"Bhashini: National Language translation Mission\" project of the Ministry of Electronics and Information Technology (MeitY), Government of India."
] | [
62,
88,
124,
66,
123,
58
] | [
"passage: TAGS\n#transformers #pytorch #jax #whisper #automatic-speech-recognition #whisper-event #kn #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n# Whisper Kannada Medium\n\nThis model is a fine-tuned version of openai/whisper-medium on the Kannada data available from multiple publicly available ASR corpuses.\nIt has been fine-tuned as a part of the Whisper fine-tuning sprint.\n\nNOTE: The code used to train this model is available for re-use in the whisper-finetune repository.## Usage\n\nIn order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.\n\nThe same repository also provides the scripts for faster inference using whisper-jax.\n\nIn order to infer a single audio file using this model, the following code snippet can be used:\n\n\n\nFor faster inference of whisper models, the whisper-jax library can be used. Please follow the necessary installation steps as mentioned here, before using the following code snippet:## Training and evaluation data\n\nTraining Data: \n - IISc-MILE Kannada ASR Corpus\n - ULCA ASR Corpus\n - Shrutilipi ASR Corpus\n - Google/Fleurs Train+Dev set\n\nEvaluation Data: \n - Google/Fleurs Test Set\n - IISc-MILE Test Set\n - OpenSLR## Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 24\n- eval_batch_size: 48\n- seed: 22\n- optimizer: adamw_bnb_8bit\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 10000\n- training_steps: 13752 (terminated upon convergence. Initially set to 51570 steps)\n- mixed_precision_training: True"
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null | null | transformers | # Fett-uccine-Long-Noodle-7B-120k-Context
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
A merge with Fett-uccine and Mistral Yarn 120k ctx.
Credit to Nitral for the merge script and idea.
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Z:\ModelColdStorage\Yarn-Mistral-7b-128k
* Z:\ModelColdStorage\Fett-uccine-7B
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Z:\ModelColdStorage\Fett-uccine-7B
layer_range: [0, 32]
- model: Z:\ModelColdStorage\Yarn-Mistral-7b-128k
layer_range: [0, 32]
merge_method: slerp
base_model: Z:\ModelColdStorage\Fett-uccine-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": []} | text-generation | Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T18:28:38+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Fett-uccine-Long-Noodle-7B-120k-Context
This is a merge of pre-trained language models created using mergekit.
## Merge Details
A merge with Fett-uccine and Mistral Yarn 120k ctx.
Credit to Nitral for the merge script and idea.
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Z:\ModelColdStorage\Yarn-Mistral-7b-128k
* Z:\ModelColdStorage\Fett-uccine-7B
### Configuration
The following YAML configuration was used to produce this model:
| [
"# Fett-uccine-Long-Noodle-7B-120k-Context\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details\nA merge with Fett-uccine and Mistral Yarn 120k ctx.\n\nCredit to Nitral for the merge script and idea.",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Z:\\ModelColdStorage\\Yarn-Mistral-7b-128k\n* Z:\\ModelColdStorage\\Fett-uccine-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fett-uccine-Long-Noodle-7B-120k-Context\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details\nA merge with Fett-uccine and Mistral Yarn 120k ctx.\n\nCredit to Nitral for the merge script and idea.",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Z:\\ModelColdStorage\\Yarn-Mistral-7b-128k\n* Z:\\ModelColdStorage\\Fett-uccine-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
58,
36,
32,
18,
55,
17
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Fett-uccine-Long-Noodle-7B-120k-Context\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details\nA merge with Fett-uccine and Mistral Yarn 120k ctx.\n\nCredit to Nitral for the merge script and idea.### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* Z:\\ModelColdStorage\\Yarn-Mistral-7b-128k\n* Z:\\ModelColdStorage\\Fett-uccine-7B### Configuration\n\nThe following YAML configuration was used to produce this model:"
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null | null | diffusers | # La nuit de Eiffel style
<Gallery />
## Model description
Georges Garen’s: Embrasement de la Tour Eiffel, It was an original illustration for the lithograph produced for the 1889 World’s Fair in Paris.
## Trigger words
You should use `paris1889` to trigger the image generation.
## Download model
[Download](/RFDLubub/La_nuit_eifell_1889_style/tree/main) them in the Files & versions tab.
| {"license": "apache-2.0", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "Eiffel 1889 style", "parameters": {"negative_prompt": "ugly, nsfw"}, "output": {"url": "images/IMG_0261.jpeg"}}], "base_model": "runwayml/stable-diffusion-v1-5", "instance_prompt": "paris1889"} | text-to-image | RFDLubub/La_nuit_eifell_1889_style | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:apache-2.0",
"region:us"
] | 2024-02-06T18:28:42+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #region-us
| # La nuit de Eiffel style
<Gallery />
## Model description
Georges Garen’s: Embrasement de la Tour Eiffel, It was an original illustration for the lithograph produced for the 1889 World’s Fair in Paris.
## Trigger words
You should use 'paris1889' to trigger the image generation.
## Download model
Download them in the Files & versions tab.
| [
"# La nuit de Eiffel style\n\n<Gallery />",
"## Model description \n\nGeorges Garen’s: Embrasement de la Tour Eiffel, It was an original illustration for the lithograph produced for the 1889 World’s Fair in Paris.",
"## Trigger words\n\nYou should use 'paris1889' to trigger the image generation.",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #region-us \n",
"# La nuit de Eiffel style\n\n<Gallery />",
"## Model description \n\nGeorges Garen’s: Embrasement de la Tour Eiffel, It was an original illustration for the lithograph produced for the 1889 World’s Fair in Paris.",
"## Trigger words\n\nYou should use 'paris1889' to trigger the image generation.",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
62,
11,
39,
19,
14
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #region-us \n# La nuit de Eiffel style\n\n<Gallery />## Model description \n\nGeorges Garen’s: Embrasement de la Tour Eiffel, It was an original illustration for the lithograph produced for the 1889 World’s Fair in Paris.## Trigger words\n\nYou should use 'paris1889' to trigger the image generation.## Download model\n\n\nDownload them in the Files & versions tab."
] | [
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null | null | transformers |
## CodeSage-Small
### Model description
CodeSage is a new family of open code embedding models with an encoder architecture that support a wide range of source code understanding tasks. It is introduced in the paper:
[Code Representation Learning At Scale by
Dejiao Zhang*, Wasi Uddin Ahmad*, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang](https://arxiv.org/abs/2402.01935) (* indicates equal contribution).
### Pretraining data
This checkpoint is trained on the Stack data (https://huggingface.co/datasets/bigcode/the-stack-dedup). Supported languages (9 in total) are as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.
### Training procedure
This checkpoint is first trained on code data via masked language modeling (MLM) and then on bimodal text-code pair data. Please refer to the paper for more details.
### How to use
This checkpoint consists of an encoder (130M model), which can be used to extract code embeddings of 1024 dimension. It can be easily loaded using the AutoModel functionality and employs the Starcoder tokenizer (https://arxiv.org/pdf/2305.06161.pdf).
```
from transformers import AutoModel, AutoTokenizer
checkpoint = "codesage/codesage-small"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).to(device)
inputs = tokenizer.encode("def print_hello_world():\tprint('Hello World!')", return_tensors="pt").to(device)
embedding = model(inputs)[0]
print(f'Dimension of the embedding: {embedding[0].size()}')
# Dimension of the embedding: torch.Size([13, 1024])
```
### BibTeX entry and citation info
```
@inproceedings{
zhang2024codesage,
title={CodeSage: Code Representation Learning At Scale},
author={Dejiao Zhang* and Wasi Ahmad* and Ming Tan and Hantian Ding and Ramesh Nallapati and Dan Roth and Xiaofei Ma and Bing Xiang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=vfzRRjumpX}
}
``` | {"language": ["code"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["bigcode/the-stack-dedup"]} | null | codesage/codesage-small | [
"transformers",
"pytorch",
"custom_code",
"code",
"dataset:bigcode/the-stack-dedup",
"arxiv:2402.01935",
"arxiv:2305.06161",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:35:41+00:00 | [
"2402.01935",
"2305.06161"
] | [
"code"
] | TAGS
#transformers #pytorch #custom_code #code #dataset-bigcode/the-stack-dedup #arxiv-2402.01935 #arxiv-2305.06161 #license-apache-2.0 #endpoints_compatible #region-us
|
## CodeSage-Small
### Model description
CodeSage is a new family of open code embedding models with an encoder architecture that support a wide range of source code understanding tasks. It is introduced in the paper:
Code Representation Learning At Scale by
Dejiao Zhang*, Wasi Uddin Ahmad*, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang (* indicates equal contribution).
### Pretraining data
This checkpoint is trained on the Stack data (URL Supported languages (9 in total) are as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.
### Training procedure
This checkpoint is first trained on code data via masked language modeling (MLM) and then on bimodal text-code pair data. Please refer to the paper for more details.
### How to use
This checkpoint consists of an encoder (130M model), which can be used to extract code embeddings of 1024 dimension. It can be easily loaded using the AutoModel functionality and employs the Starcoder tokenizer (URL
### BibTeX entry and citation info
| [
"## CodeSage-Small",
"### Model description\nCodeSage is a new family of open code embedding models with an encoder architecture that support a wide range of source code understanding tasks. It is introduced in the paper:\n\nCode Representation Learning At Scale by \nDejiao Zhang*, Wasi Uddin Ahmad*, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang (* indicates equal contribution).",
"### Pretraining data\nThis checkpoint is trained on the Stack data (URL Supported languages (9 in total) are as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.",
"### Training procedure\nThis checkpoint is first trained on code data via masked language modeling (MLM) and then on bimodal text-code pair data. Please refer to the paper for more details.",
"### How to use\nThis checkpoint consists of an encoder (130M model), which can be used to extract code embeddings of 1024 dimension. It can be easily loaded using the AutoModel functionality and employs the Starcoder tokenizer (URL",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #custom_code #code #dataset-bigcode/the-stack-dedup #arxiv-2402.01935 #arxiv-2305.06161 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## CodeSage-Small",
"### Model description\nCodeSage is a new family of open code embedding models with an encoder architecture that support a wide range of source code understanding tasks. It is introduced in the paper:\n\nCode Representation Learning At Scale by \nDejiao Zhang*, Wasi Uddin Ahmad*, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang (* indicates equal contribution).",
"### Pretraining data\nThis checkpoint is trained on the Stack data (URL Supported languages (9 in total) are as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.",
"### Training procedure\nThis checkpoint is first trained on code data via masked language modeling (MLM) and then on bimodal text-code pair data. Please refer to the paper for more details.",
"### How to use\nThis checkpoint consists of an encoder (130M model), which can be used to extract code embeddings of 1024 dimension. It can be easily loaded using the AutoModel functionality and employs the Starcoder tokenizer (URL",
"### BibTeX entry and citation info"
] | [
68,
7,
97,
57,
45,
60,
11
] | [
"passage: TAGS\n#transformers #pytorch #custom_code #code #dataset-bigcode/the-stack-dedup #arxiv-2402.01935 #arxiv-2305.06161 #license-apache-2.0 #endpoints_compatible #region-us \n## CodeSage-Small### Model description\nCodeSage is a new family of open code embedding models with an encoder architecture that support a wide range of source code understanding tasks. It is introduced in the paper:\n\nCode Representation Learning At Scale by \nDejiao Zhang*, Wasi Uddin Ahmad*, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang (* indicates equal contribution).### Pretraining data\nThis checkpoint is trained on the Stack data (URL Supported languages (9 in total) are as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.### Training procedure\nThis checkpoint is first trained on code data via masked language modeling (MLM) and then on bimodal text-code pair data. Please refer to the paper for more details.### How to use\nThis checkpoint consists of an encoder (130M model), which can be used to extract code embeddings of 1024 dimension. It can be easily loaded using the AutoModel functionality and employs the Starcoder tokenizer (URL### BibTeX entry and citation info"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SMIDS_3x_beit_large_RMSProp_lr001_fold4
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6630
- Accuracy: 0.7433
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9384 | 1.0 | 450 | 0.8939 | 0.54 |
| 0.7966 | 2.0 | 900 | 0.7849 | 0.5817 |
| 0.7599 | 3.0 | 1350 | 0.7352 | 0.64 |
| 0.8327 | 4.0 | 1800 | 0.7532 | 0.625 |
| 0.7993 | 5.0 | 2250 | 0.7349 | 0.6483 |
| 0.7512 | 6.0 | 2700 | 0.7690 | 0.6067 |
| 0.7131 | 7.0 | 3150 | 0.7467 | 0.6117 |
| 0.7843 | 8.0 | 3600 | 0.7278 | 0.6617 |
| 0.6799 | 9.0 | 4050 | 0.7060 | 0.6567 |
| 0.7552 | 10.0 | 4500 | 0.7557 | 0.665 |
| 0.8031 | 11.0 | 4950 | 0.7121 | 0.65 |
| 0.7757 | 12.0 | 5400 | 0.7011 | 0.6867 |
| 0.9977 | 13.0 | 5850 | 0.7141 | 0.6533 |
| 0.6572 | 14.0 | 6300 | 0.6909 | 0.685 |
| 0.6774 | 15.0 | 6750 | 0.7061 | 0.645 |
| 0.7507 | 16.0 | 7200 | 0.7123 | 0.6533 |
| 0.6867 | 17.0 | 7650 | 0.7087 | 0.6883 |
| 0.6706 | 18.0 | 8100 | 0.7353 | 0.6717 |
| 0.7593 | 19.0 | 8550 | 0.6934 | 0.6983 |
| 0.7111 | 20.0 | 9000 | 0.6726 | 0.7017 |
| 0.6386 | 21.0 | 9450 | 0.7018 | 0.68 |
| 0.6235 | 22.0 | 9900 | 0.6840 | 0.71 |
| 0.7095 | 23.0 | 10350 | 0.6803 | 0.6817 |
| 0.7566 | 24.0 | 10800 | 0.6573 | 0.6883 |
| 0.6515 | 25.0 | 11250 | 0.6662 | 0.6967 |
| 0.6684 | 26.0 | 11700 | 0.7184 | 0.7083 |
| 0.6549 | 27.0 | 12150 | 0.6596 | 0.71 |
| 0.6299 | 28.0 | 12600 | 0.6681 | 0.7167 |
| 0.7484 | 29.0 | 13050 | 0.6502 | 0.7267 |
| 0.6201 | 30.0 | 13500 | 0.6578 | 0.7317 |
| 0.6006 | 31.0 | 13950 | 0.6566 | 0.7233 |
| 0.6381 | 32.0 | 14400 | 0.6552 | 0.7183 |
| 0.6668 | 33.0 | 14850 | 0.6708 | 0.6967 |
| 0.6344 | 34.0 | 15300 | 0.7034 | 0.6683 |
| 0.6152 | 35.0 | 15750 | 0.6661 | 0.705 |
| 0.7446 | 36.0 | 16200 | 0.6595 | 0.7 |
| 0.6159 | 37.0 | 16650 | 0.6405 | 0.7183 |
| 0.5973 | 38.0 | 17100 | 0.6487 | 0.7217 |
| 0.5995 | 39.0 | 17550 | 0.6487 | 0.735 |
| 0.6359 | 40.0 | 18000 | 0.6330 | 0.7383 |
| 0.5832 | 41.0 | 18450 | 0.6469 | 0.75 |
| 0.577 | 42.0 | 18900 | 0.6706 | 0.7483 |
| 0.5703 | 43.0 | 19350 | 0.6313 | 0.7467 |
| 0.5792 | 44.0 | 19800 | 0.6299 | 0.75 |
| 0.5115 | 45.0 | 20250 | 0.6380 | 0.7233 |
| 0.4552 | 46.0 | 20700 | 0.6450 | 0.7383 |
| 0.5313 | 47.0 | 21150 | 0.6556 | 0.755 |
| 0.4337 | 48.0 | 21600 | 0.6566 | 0.7467 |
| 0.3859 | 49.0 | 22050 | 0.6628 | 0.7433 |
| 0.5055 | 50.0 | 22500 | 0.6630 | 0.7433 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7433333333333333, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_RMSProp_lr001_fold4 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:36:51+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_RMSProp\_lr001\_fold4
=============================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6630
* Accuracy: 0.7433
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
] | [
"TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
] | [
81,
115,
4,
30
] | [
"passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SMIDS_3x_beit_large_RMSProp_lr0001_fold4
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6573
- Accuracy: 0.8733
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3892 | 1.0 | 450 | 0.5369 | 0.7883 |
| 0.2751 | 2.0 | 900 | 0.5066 | 0.8383 |
| 0.2844 | 3.0 | 1350 | 0.5079 | 0.845 |
| 0.1884 | 4.0 | 1800 | 0.4725 | 0.85 |
| 0.2739 | 5.0 | 2250 | 0.4283 | 0.8617 |
| 0.152 | 6.0 | 2700 | 0.5357 | 0.8517 |
| 0.083 | 7.0 | 3150 | 0.6432 | 0.87 |
| 0.0372 | 8.0 | 3600 | 0.6031 | 0.8717 |
| 0.0302 | 9.0 | 4050 | 0.8748 | 0.8433 |
| 0.2276 | 10.0 | 4500 | 0.7103 | 0.865 |
| 0.0428 | 11.0 | 4950 | 0.8127 | 0.8633 |
| 0.0724 | 12.0 | 5400 | 0.8286 | 0.8517 |
| 0.0216 | 13.0 | 5850 | 0.9533 | 0.86 |
| 0.0006 | 14.0 | 6300 | 0.9559 | 0.8667 |
| 0.0916 | 15.0 | 6750 | 0.8717 | 0.855 |
| 0.0798 | 16.0 | 7200 | 0.8980 | 0.8733 |
| 0.0226 | 17.0 | 7650 | 0.9410 | 0.8533 |
| 0.0036 | 18.0 | 8100 | 1.0756 | 0.845 |
| 0.1889 | 19.0 | 8550 | 0.9961 | 0.8483 |
| 0.0079 | 20.0 | 9000 | 0.9349 | 0.8667 |
| 0.0177 | 21.0 | 9450 | 0.7980 | 0.865 |
| 0.0155 | 22.0 | 9900 | 0.9382 | 0.875 |
| 0.0006 | 23.0 | 10350 | 1.1095 | 0.8467 |
| 0.0001 | 24.0 | 10800 | 1.1022 | 0.865 |
| 0.045 | 25.0 | 11250 | 0.9237 | 0.8767 |
| 0.0456 | 26.0 | 11700 | 1.0716 | 0.855 |
| 0.03 | 27.0 | 12150 | 1.1362 | 0.8533 |
| 0.0005 | 28.0 | 12600 | 1.0492 | 0.8717 |
| 0.0001 | 29.0 | 13050 | 1.3287 | 0.8617 |
| 0.0003 | 30.0 | 13500 | 1.2046 | 0.865 |
| 0.018 | 31.0 | 13950 | 1.2173 | 0.8467 |
| 0.0 | 32.0 | 14400 | 1.1490 | 0.87 |
| 0.0361 | 33.0 | 14850 | 1.2835 | 0.8583 |
| 0.0001 | 34.0 | 15300 | 1.2084 | 0.86 |
| 0.0 | 35.0 | 15750 | 1.6079 | 0.8533 |
| 0.0 | 36.0 | 16200 | 1.2932 | 0.8633 |
| 0.0 | 37.0 | 16650 | 1.2501 | 0.8683 |
| 0.0085 | 38.0 | 17100 | 1.6398 | 0.85 |
| 0.0 | 39.0 | 17550 | 1.5158 | 0.8667 |
| 0.0007 | 40.0 | 18000 | 1.4220 | 0.8833 |
| 0.0 | 41.0 | 18450 | 1.4647 | 0.8683 |
| 0.0 | 42.0 | 18900 | 1.5546 | 0.8767 |
| 0.0154 | 43.0 | 19350 | 1.6136 | 0.8767 |
| 0.0005 | 44.0 | 19800 | 1.4607 | 0.8767 |
| 0.0 | 45.0 | 20250 | 1.4842 | 0.8783 |
| 0.0 | 46.0 | 20700 | 1.5603 | 0.875 |
| 0.0 | 47.0 | 21150 | 1.6034 | 0.8733 |
| 0.0 | 48.0 | 21600 | 1.6637 | 0.87 |
| 0.0 | 49.0 | 22050 | 1.6529 | 0.8733 |
| 0.0 | 50.0 | 22500 | 1.6573 | 0.8733 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr0001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8733333333333333, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_RMSProp_lr0001_fold4 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:37:32+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_RMSProp\_lr0001\_fold4
==============================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6573
* Accuracy: 0.8733
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
] | [
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115,
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"passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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null | null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | RMSprop |
| learning_rate | 9.999999747378752e-05 |
| decay | 0.0 |
| rho | 0.8999999761581421 |
| momentum | 0.0 |
| epsilon | 1e-07 |
| centered | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras", "tags": ["Image Classification"]} | null | AiresPucrs/cats-vs-dogs | [
"keras",
"Image Classification",
"region:us"
] | 2024-02-06T18:42:13+00:00 | [] | [] | TAGS
#keras #Image Classification #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
Model Plot
----------
View Model Plot
!Model Image
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #Image Classification #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
13,
28
] | [
"passage: TAGS\n#keras #Image Classification #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# quynh_deberta-v3-Base-finetuned-AI_req_1
This model is a fine-tuned version of [microsoft/deberta-v3-Base](https://huggingface.co/microsoft/deberta-v3-Base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0260
- Train Accuracy: 0.9918
- Validation Loss: 1.1900
- Validation Accuracy: 0.7810
- Epoch: 12
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2730, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.8121 | 0.6690 | 0.6778 | 0.7524 | 0 |
| 0.5487 | 0.8049 | 0.5841 | 0.7810 | 1 |
| 0.4181 | 0.8420 | 0.4797 | 0.8000 | 2 |
| 0.3674 | 0.8462 | 0.5794 | 0.7905 | 3 |
| 0.3232 | 0.8654 | 0.5766 | 0.7810 | 4 |
| 0.2762 | 0.8887 | 0.6246 | 0.8000 | 5 |
| 0.2165 | 0.9148 | 0.5751 | 0.7429 | 6 |
| 0.1623 | 0.9464 | 0.6580 | 0.8000 | 7 |
| 0.1645 | 0.9464 | 0.7932 | 0.7810 | 8 |
| 0.1231 | 0.9574 | 1.0112 | 0.8095 | 9 |
| 0.1089 | 0.9574 | 0.8745 | 0.7619 | 10 |
| 0.0587 | 0.9794 | 0.9496 | 0.7905 | 11 |
| 0.0260 | 0.9918 | 1.1900 | 0.7810 | 12 |
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.9.1
- Datasets 2.16.1
- Tokenizers 0.13.3
| {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "quynh_deberta-v3-Base-finetuned-AI_req_1", "results": []}]} | text-classification | QT321/quynh_deberta-v3-Base-finetuned-AI_req_1 | [
"transformers",
"tf",
"deberta-v2",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:44:58+00:00 | [] | [] | TAGS
#transformers #tf #deberta-v2 #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| quynh\_deberta-v3-Base-finetuned-AI\_req\_1
===========================================
This model is a fine-tuned version of microsoft/deberta-v3-Base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0260
* Train Accuracy: 0.9918
* Validation Loss: 1.1900
* Validation Accuracy: 0.7810
* Epoch: 12
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 2730, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.28.0
* TensorFlow 2.9.1
* Datasets 2.16.1
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2730, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* TensorFlow 2.9.1\n* Datasets 2.16.1\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #tf #deberta-v2 #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2730, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* TensorFlow 2.9.1\n* Datasets 2.16.1\n* Tokenizers 0.13.3"
] | [
56,
178,
4,
34
] | [
"passage: TAGS\n#transformers #tf #deberta-v2 #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2730, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.28.0\n* TensorFlow 2.9.1\n* Datasets 2.16.1\n* Tokenizers 0.13.3"
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null | null | transformers |
effi 7b GPTQ is a quantized version of effi 7b whiich is a 7 billion parameter model built by AI Planet. We have used Auto-gptq for quantising the model
## Model Details
### Model Description
This original model has been fine-tuned on Chain of Thought datasets, which has context from mixed sources with corresponding rationale. The final finetuned Large Language Model(LLM) have shown enhanced capabilities of solving novel tasks by providing a reasoning.And the final model was quantized into GPTQ format
- **Developed by:** AI Planet
- **Model type:** Casual Decoder only
- **Language(s) (NLP):** English
- **Quantisation type:** GPTQ(4-bit)
- **License:** Apache 2.0
- **Quantized from model:** Effi-7b
### Qunatization Configuration
- **bits:** 4,
- **damp_percent** 0.1,
- **dataset:** "wikitext2",
- **desc_act:** false,
- **group_size:** 128,
- **modules_in_block_to_quantize:** null,
- **quant_method:** "gptq",
- **sym:** true,
- **true_sequential:** true
### Example of usage
```py
import torch
from transformers import AutoTokenizer , AutoModelForCausalLM
quant_path = "aiplanet/effi-7b-gptq"
model = AutoModelForCausalLM.from_pretrained(quant_path , device_map='cuda')
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True , safetensors=True , fuse_layers=True)
tst = """
### INSTRUCTION:
Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia's domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.Is Virgin Australia and Virgin Blue the same airlines?
"""
system_message = "Given your chain of thought reasoning, provide a rationale for the context in the source."
template=f"""
Context: {system_message}
Human: {tst}
"""
# Tokenize the input
input_ids = tokenizer(template, return_tensors="pt", truncation=True).input_ids.cuda()
# Run the model to infere an output
outputs = model.generate(input_ids=input_ids, max_new_tokens=512, top_p=0.9,temperature=0.1 , top_k=1, repetition_penalty=1.1)
# Print the result
print(f"{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(template):]}")
```
### Framework versions
- **Transformers** 4.37.2
- **optimum** 1.16.2
- **auto-gptq** 0.6.0
### Citation
```
@misc {bhavyaaiplanet,
author = { {Bhavya Bhola} },
title = { Quantized version of effi-7b by AI Planet},
year = 2024,
url = { https://huggingface.co/aiplanet/effi-7b-gptq },
publisher = { Hugging Face }
}
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["gptq", "text-generation-inference", "llama2"], "base_model": "aiplanet/effi-7b", "inference": false, "model_type": "llama", "pipeline_tag": "text-generation"} | text-generation | aiplanet/effi-7b-gptq | [
"transformers",
"safetensors",
"llama",
"text-generation",
"gptq",
"text-generation-inference",
"llama2",
"en",
"base_model:aiplanet/effi-7b",
"license:apache-2.0",
"autotrain_compatible",
"4-bit",
"region:us"
] | 2024-02-06T18:46:16+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #gptq #text-generation-inference #llama2 #en #base_model-aiplanet/effi-7b #license-apache-2.0 #autotrain_compatible #4-bit #region-us
|
effi 7b GPTQ is a quantized version of effi 7b whiich is a 7 billion parameter model built by AI Planet. We have used Auto-gptq for quantising the model
## Model Details
### Model Description
This original model has been fine-tuned on Chain of Thought datasets, which has context from mixed sources with corresponding rationale. The final finetuned Large Language Model(LLM) have shown enhanced capabilities of solving novel tasks by providing a reasoning.And the final model was quantized into GPTQ format
- Developed by: AI Planet
- Model type: Casual Decoder only
- Language(s) (NLP): English
- Quantisation type: GPTQ(4-bit)
- License: Apache 2.0
- Quantized from model: Effi-7b
### Qunatization Configuration
- bits: 4,
- damp_percent 0.1,
- dataset: "wikitext2",
- desc_act: false,
- group_size: 128,
- modules_in_block_to_quantize: null,
- quant_method: "gptq",
- sym: true,
- true_sequential: true
### Example of usage
### Framework versions
- Transformers 4.37.2
- optimum 1.16.2
- auto-gptq 0.6.0
| [
"## Model Details",
"### Model Description\n\n This original model has been fine-tuned on Chain of Thought datasets, which has context from mixed sources with corresponding rationale. The final finetuned Large Language Model(LLM) have shown enhanced capabilities of solving novel tasks by providing a reasoning.And the final model was quantized into GPTQ format\n\n- Developed by: AI Planet\n- Model type: Casual Decoder only\n- Language(s) (NLP): English\n- Quantisation type: GPTQ(4-bit)\n- License: Apache 2.0\n- Quantized from model: Effi-7b",
"### Qunatization Configuration\n\n - bits: 4,\n - damp_percent 0.1,\n - dataset: \"wikitext2\",\n - desc_act: false,\n - group_size: 128,\n - modules_in_block_to_quantize: null,\n - quant_method: \"gptq\",\n - sym: true,\n - true_sequential: true",
"### Example of usage",
"### Framework versions\n- Transformers 4.37.2\n- optimum 1.16.2\n- auto-gptq 0.6.0"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #gptq #text-generation-inference #llama2 #en #base_model-aiplanet/effi-7b #license-apache-2.0 #autotrain_compatible #4-bit #region-us \n",
"## Model Details",
"### Model Description\n\n This original model has been fine-tuned on Chain of Thought datasets, which has context from mixed sources with corresponding rationale. The final finetuned Large Language Model(LLM) have shown enhanced capabilities of solving novel tasks by providing a reasoning.And the final model was quantized into GPTQ format\n\n- Developed by: AI Planet\n- Model type: Casual Decoder only\n- Language(s) (NLP): English\n- Quantisation type: GPTQ(4-bit)\n- License: Apache 2.0\n- Quantized from model: Effi-7b",
"### Qunatization Configuration\n\n - bits: 4,\n - damp_percent 0.1,\n - dataset: \"wikitext2\",\n - desc_act: false,\n - group_size: 128,\n - modules_in_block_to_quantize: null,\n - quant_method: \"gptq\",\n - sym: true,\n - true_sequential: true",
"### Example of usage",
"### Framework versions\n- Transformers 4.37.2\n- optimum 1.16.2\n- auto-gptq 0.6.0"
] | [
72,
3,
130,
86,
6,
26
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #gptq #text-generation-inference #llama2 #en #base_model-aiplanet/effi-7b #license-apache-2.0 #autotrain_compatible #4-bit #region-us \n## Model Details### Model Description\n\n This original model has been fine-tuned on Chain of Thought datasets, which has context from mixed sources with corresponding rationale. The final finetuned Large Language Model(LLM) have shown enhanced capabilities of solving novel tasks by providing a reasoning.And the final model was quantized into GPTQ format\n\n- Developed by: AI Planet\n- Model type: Casual Decoder only\n- Language(s) (NLP): English\n- Quantisation type: GPTQ(4-bit)\n- License: Apache 2.0\n- Quantized from model: Effi-7b### Qunatization Configuration\n\n - bits: 4,\n - damp_percent 0.1,\n - dataset: \"wikitext2\",\n - desc_act: false,\n - group_size: 128,\n - modules_in_block_to_quantize: null,\n - quant_method: \"gptq\",\n - sym: true,\n - true_sequential: true### Example of usage### Framework versions\n- Transformers 4.37.2\n- optimum 1.16.2\n- auto-gptq 0.6.0"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# quynh_deberta-v3-Base-finetuned-AI_req_2
This model is a fine-tuned version of [microsoft/deberta-v3-Base](https://huggingface.co/microsoft/deberta-v3-Base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0324
- Train Accuracy: 0.9959
- Validation Loss: 0.9053
- Validation Accuracy: 0.8286
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2730, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.8413 | 0.6593 | 0.7133 | 0.7143 | 0 |
| 0.6659 | 0.75 | 0.5795 | 0.8000 | 1 |
| 0.5713 | 0.7692 | 0.5171 | 0.8476 | 2 |
| 0.4814 | 0.7967 | 0.4655 | 0.8381 | 3 |
| 0.4366 | 0.8118 | 0.4368 | 0.8476 | 4 |
| 0.3888 | 0.8228 | 0.4844 | 0.8190 | 5 |
| 0.3282 | 0.8571 | 0.5208 | 0.8286 | 6 |
| 0.2678 | 0.8723 | 0.5297 | 0.8381 | 7 |
| 0.2422 | 0.8970 | 0.6020 | 0.8190 | 8 |
| 0.2069 | 0.9272 | 0.6953 | 0.7429 | 9 |
| 0.1441 | 0.9519 | 0.6943 | 0.7524 | 10 |
| 0.1426 | 0.9492 | 0.6897 | 0.8190 | 11 |
| 0.0947 | 0.9725 | 0.9910 | 0.8000 | 12 |
| 0.0536 | 0.9835 | 0.9079 | 0.8095 | 13 |
| 0.0324 | 0.9959 | 0.9053 | 0.8286 | 14 |
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.9.1
- Datasets 2.16.1
- Tokenizers 0.13.3
| {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "quynh_deberta-v3-Base-finetuned-AI_req_2", "results": []}]} | text-classification | QT321/quynh_deberta-v3-Base-finetuned-AI_req_2 | [
"transformers",
"tf",
"deberta-v2",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T18:47:22+00:00 | [] | [] | TAGS
#transformers #tf #deberta-v2 #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| quynh\_deberta-v3-Base-finetuned-AI\_req\_2
===========================================
This model is a fine-tuned version of microsoft/deberta-v3-Base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0324
* Train Accuracy: 0.9959
* Validation Loss: 0.9053
* Validation Accuracy: 0.8286
* Epoch: 14
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 2730, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.28.0
* TensorFlow 2.9.1
* Datasets 2.16.1
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2730, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* TensorFlow 2.9.1\n* Datasets 2.16.1\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #tf #deberta-v2 #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2730, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* TensorFlow 2.9.1\n* Datasets 2.16.1\n* Tokenizers 0.13.3"
] | [
56,
178,
4,
34
] | [
"passage: TAGS\n#transformers #tf #deberta-v2 #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2730, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.28.0\n* TensorFlow 2.9.1\n* Datasets 2.16.1\n* Tokenizers 0.13.3"
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null | null | diffusers | ### YourNameV2 Dreambooth model trained by erens with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Good for faces, not much for backgrounds.
| {"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]} | text-to-image | erens/YourNameStyleV3 | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-06T18:49:56+00:00 | [] | [] | TAGS
#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### YourNameV2 Dreambooth model trained by erens with TheLastBen's fast-DreamBooth notebook
Good for faces, not much for backgrounds.
| [
"### YourNameV2 Dreambooth model trained by erens with TheLastBen's fast-DreamBooth notebook\n\n\nGood for faces, not much for backgrounds."
] | [
"TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### YourNameV2 Dreambooth model trained by erens with TheLastBen's fast-DreamBooth notebook\n\n\nGood for faces, not much for backgrounds."
] | [
61,
39
] | [
"passage: TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### YourNameV2 Dreambooth model trained by erens with TheLastBen's fast-DreamBooth notebook\n\n\nGood for faces, not much for backgrounds."
] | [
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null | null | diffusers | ### My-Pet-Cat-CTE Dreambooth model trained by jabberwalk following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AI3119
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | jabberwalk/my-pet-cat-cte | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-06T18:52:03+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### My-Pet-Cat-CTE Dreambooth model trained by jabberwalk following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AI3119
Sample pictures of this concept:
!0
| [
"### My-Pet-Cat-CTE Dreambooth model trained by jabberwalk following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: AI3119\n\nSample pictures of this concept:\n\n !0"
] | [
"TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### My-Pet-Cat-CTE Dreambooth model trained by jabberwalk following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: AI3119\n\nSample pictures of this concept:\n\n !0"
] | [
73,
55
] | [
"passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### My-Pet-Cat-CTE Dreambooth model trained by jabberwalk following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: AI3119\n\nSample pictures of this concept:\n\n !0"
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null | null | transformers |
# Model Card for Model ID
Base model for merging DPO qlora adapter to build Navarna -
This is a model that is fine-tuned with sentence retrieval tasks in Hindi on top of OpenHermes 2.5
Dataset Used - https://huggingface.co/datasets/TokenBender/Hindi_SFT_sentence_retriever_set | {"library_name": "transformers", "tags": []} | text-generation | TokenBender/navarna_hindi_merged | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T18:56:28+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
Base model for merging DPO qlora adapter to build Navarna -
This is a model that is fine-tuned with sentence retrieval tasks in Hindi on top of OpenHermes 2.5
Dataset Used - URL | [
"# Model Card for Model ID\n\nBase model for merging DPO qlora adapter to build Navarna - \nThis is a model that is fine-tuned with sentence retrieval tasks in Hindi on top of OpenHermes 2.5\n\nDataset Used - URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID\n\nBase model for merging DPO qlora adapter to build Navarna - \nThis is a model that is fine-tuned with sentence retrieval tasks in Hindi on top of OpenHermes 2.5\n\nDataset Used - URL"
] | [
51,
54
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID\n\nBase model for merging DPO qlora adapter to build Navarna - \nThis is a model that is fine-tuned with sentence retrieval tasks in Hindi on top of OpenHermes 2.5\n\nDataset Used - URL"
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] |
null | null | transformers | <div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Model
This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). **We follow [HF's Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)'s training recipe.** The model was " initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4."
#### How to use
You will need the transformers>=4.34
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# ...
``` | {"language": ["en"], "license": "apache-2.0", "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata", "HuggingFaceH4/ultrachat_200k", "HuggingFaceH4/ultrafeedback_binarized"], "widget": [{"text": "<|system|>\nYou are a chatbot who can help code!</s>\n<|user|>\nWrite me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI.</s>\n<|assistant|>\n"}]} | text-generation | gyan4u/Virtual | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T18:56:44+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #conversational #en #dataset-cerebras/SlimPajama-627B #dataset-bigcode/starcoderdata #dataset-HuggingFaceH4/ultrachat_200k #dataset-HuggingFaceH4/ultrafeedback_binarized #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <div align="center">
# TinyLlama-1.1B
</div>
URL
The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs . The training has started on 2023-09-01.
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Model
This is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. We follow HF's Zephyr's training recipe. The model was " initially fine-tuned on a variant of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
We then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4."
#### How to use
You will need the transformers>=4.34
Do check the TinyLlama github page for more information.
| [
"# TinyLlama-1.1B\n</div>\n\nURL\n\nThe TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of \"just\" 90 days using 16 A100-40G GPUs . The training has started on 2023-09-01. \n\n\nWe adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.",
"#### This Model\nThis is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. We follow HF's Zephyr's training recipe. The model was \" initially fine-tuned on a variant of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. \nWe then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4.\"",
"#### How to use\nYou will need the transformers>=4.34\nDo check the TinyLlama github page for more information."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #dataset-cerebras/SlimPajama-627B #dataset-bigcode/starcoderdata #dataset-HuggingFaceH4/ultrachat_200k #dataset-HuggingFaceH4/ultrafeedback_binarized #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# TinyLlama-1.1B\n</div>\n\nURL\n\nThe TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of \"just\" 90 days using 16 A100-40G GPUs . The training has started on 2023-09-01. \n\n\nWe adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.",
"#### This Model\nThis is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. We follow HF's Zephyr's training recipe. The model was \" initially fine-tuned on a variant of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. \nWe then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4.\"",
"#### How to use\nYou will need the transformers>=4.34\nDo check the TinyLlama github page for more information."
] | [
120,
155,
146,
29
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #dataset-cerebras/SlimPajama-627B #dataset-bigcode/starcoderdata #dataset-HuggingFaceH4/ultrachat_200k #dataset-HuggingFaceH4/ultrafeedback_binarized #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# TinyLlama-1.1B\n</div>\n\nURL\n\nThe TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of \"just\" 90 days using 16 A100-40G GPUs . The training has started on 2023-09-01. \n\n\nWe adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.#### This Model\nThis is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. We follow HF's Zephyr's training recipe. The model was \" initially fine-tuned on a variant of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. \nWe then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4.\"#### How to use\nYou will need the transformers>=4.34\nDo check the TinyLlama github page for more information."
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | ashishkgpian/sharded_astromistral | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
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"endpoints_compatible",
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] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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### Compute Infrastructure
#### Hardware
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[optional]
BibTeX:
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## Glossary [optional]
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## Model Card Contact
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] |
null | null | transformers |
# TinyLLaMA OpenHermes2.5 [Work in Progress]
This a finetune of TinyLLaMA base model finetuned on [OpenHermes 2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) and [UltraChat 200k](https://huggingface.co/datasets/abhinand/ultrachat_200k_sharegpt) for a single epoch.
Training was generously supported by [Jarvislabs.ai](https://jarvislabs.ai/).
If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated.
[](https://www.buymeacoffee.com/abhinand.b)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
is_llama_derived_model: true
# huggingface repo
datasets:
- path: teknium/OpenHermes-2.5
type: sharegpt
conversation: chatml
train_on_split: train
- path: abhinand/ultrachat_200k_sharegpt
type: sharegpt
conversation: chatml
train_on_split: train
load_in_4bit: false
load_in_8bit: false
bf16: true # require >=ampere
chat_template: chatml
dataset_prepared_path: last_run_prepared_path
hub_model_id: abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v1.0
group_by_length: false
val_set_size: 0.0
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
lora_modules_to_save:
- embed_tokens
- lm_head
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
output_dir: /home/tiny-llama/trained_models
gradient_accumulation_steps: 2
micro_batch_size: 32
eval_batch_size: 32
num_epochs: 1
logging_steps: 1
save_steps: 50
save_total_limit: 3
save_safetensors: true
gradient_checkpointing: true
lr_scheduler: cosine
optimizer: "adamw_bnb_8bit"
adam_beta2: 0.95
adam_epsilon: 0.00001
weight_decay: 0.1
learning_rate: 0.0005
max_grad_norm: 1.0
warmup_ratio: 0.05
# warmup_steps: 100
flash_attention: true
# Resume from a specific checkpoint dir
resume_from_checkpoint:
# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
# Be careful with this being turned on between different models.
# auto_resume_from_checkpoints: true
# wandb configuration if you're using it
# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
wandb_project: "tiny-llama-sft"
wandb_name:
wandb_run_id:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
tokens: # these are delimiters
- "<|im_start|>"
- "<|im_end|>"
```
</details>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 476
- num_epochs: 1
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0 | {"language": ["en"], "license": "apache-2.0", "datasets": ["teknium/OpenHermes-2.5", "abhinand/ultrachat_200k_sharegpt"]} | text-generation | abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:abhinand/ultrachat_200k_sharegpt",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T19:00:10+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-abhinand/ultrachat_200k_sharegpt #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# TinyLLaMA OpenHermes2.5 [Work in Progress]
This a finetune of TinyLLaMA base model finetuned on OpenHermes 2.5 and UltraChat 200k for a single epoch.
Training was generously supported by URL.
If you appreciate this work and would like to support its continued development, consider buying me a coffee. Your support is invaluable and greatly appreciated.
 and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 476
- num_epochs: 1
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"# TinyLLaMA OpenHermes2.5 [Work in Progress]\n\nThis a finetune of TinyLLaMA base model finetuned on OpenHermes 2.5 and UltraChat 200k for a single epoch. \n\nTraining was generously supported by URL.\n\nIf you appreciate this work and would like to support its continued development, consider buying me a coffee. Your support is invaluable and greatly appreciated.\n\n![\"Buy Me A Coffee\"](URL\n\n<details><summary>See axolotl config</summary>\n\naxolotl version: '0.4.0'\n\n\n</details>",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 476\n- num_epochs: 1",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.0.1\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-abhinand/ultrachat_200k_sharegpt #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# TinyLLaMA OpenHermes2.5 [Work in Progress]\n\nThis a finetune of TinyLLaMA base model finetuned on OpenHermes 2.5 and UltraChat 200k for a single epoch. \n\nTraining was generously supported by URL.\n\nIf you appreciate this work and would like to support its continued development, consider buying me a coffee. Your support is invaluable and greatly appreciated.\n\n![\"Buy Me A Coffee\"](URL\n\n<details><summary>See axolotl config</summary>\n\naxolotl version: '0.4.0'\n\n\n</details>",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 476\n- num_epochs: 1",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.0.1\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
91,
135,
3,
129,
41
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #dataset-teknium/OpenHermes-2.5 #dataset-abhinand/ultrachat_200k_sharegpt #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# TinyLLaMA OpenHermes2.5 [Work in Progress]\n\nThis a finetune of TinyLLaMA base model finetuned on OpenHermes 2.5 and UltraChat 200k for a single epoch. \n\nTraining was generously supported by URL.\n\nIf you appreciate this work and would like to support its continued development, consider buying me a coffee. Your support is invaluable and greatly appreciated.\n\n and epsilon=1e-05\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 476\n- num_epochs: 1### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.0.1\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
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null | null | transformers |
## Miqu DPO
Miqu DPO is the same model than Miqu, with a DPO trained on MiquMaid v2 on Alpaca format, it was done for the purpose to try to uncensor further Miqu and make Alpaca prompt more usable with base Miqu. Also, this will be one of the base for MiquMaid-v2-2x70B-DPO.
Miqu base is REALLY censored outside RP, this LoRA let him reply a little more thing, but that's it. To have his full potential, it need to be in a merge/MoE of MiquMaid, since the loRA was based for MiquMaid, not Miqu base. I still let it public for who want it.
It uncensor a little the model, but keep some warning. Sometime reply really unethically.
<!-- description start -->
## Description
This repo contains FP16 files of Miqu-70B-DPO.
<!-- description end -->
<!-- description start -->
## Dataset used
- NobodyExistsOnTheInternet/ToxicDPOqa
- Undi95/toxic-dpo-v0.1-NoWarning
<!-- description end -->
<!-- prompt-template start -->
## Prompt format: Alpaca
```
### Instruction:
{prompt}
### Input:
{input}
### Response:
{output}
```
Or simple Mistral format (but the uncensoring was done on Alpaca, so Alpaca is recommanded).
## Others
If you want to support me, you can [here](https://ko-fi.com/undiai). | {} | text-generation | Undi95/Miqu-70B-Alpaca-DPO | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T19:02:32+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Miqu DPO
Miqu DPO is the same model than Miqu, with a DPO trained on MiquMaid v2 on Alpaca format, it was done for the purpose to try to uncensor further Miqu and make Alpaca prompt more usable with base Miqu. Also, this will be one of the base for MiquMaid-v2-2x70B-DPO.
Miqu base is REALLY censored outside RP, this LoRA let him reply a little more thing, but that's it. To have his full potential, it need to be in a merge/MoE of MiquMaid, since the loRA was based for MiquMaid, not Miqu base. I still let it public for who want it.
It uncensor a little the model, but keep some warning. Sometime reply really unethically.
## Description
This repo contains FP16 files of Miqu-70B-DPO.
## Dataset used
- NobodyExistsOnTheInternet/ToxicDPOqa
- Undi95/toxic-dpo-v0.1-NoWarning
## Prompt format: Alpaca
Or simple Mistral format (but the uncensoring was done on Alpaca, so Alpaca is recommanded).
## Others
If you want to support me, you can here. | [
"## Miqu DPO\n\nMiqu DPO is the same model than Miqu, with a DPO trained on MiquMaid v2 on Alpaca format, it was done for the purpose to try to uncensor further Miqu and make Alpaca prompt more usable with base Miqu. Also, this will be one of the base for MiquMaid-v2-2x70B-DPO.\n\nMiqu base is REALLY censored outside RP, this LoRA let him reply a little more thing, but that's it. To have his full potential, it need to be in a merge/MoE of MiquMaid, since the loRA was based for MiquMaid, not Miqu base. I still let it public for who want it.\n\nIt uncensor a little the model, but keep some warning. Sometime reply really unethically.",
"## Description\n\nThis repo contains FP16 files of Miqu-70B-DPO.",
"## Dataset used\n\n- NobodyExistsOnTheInternet/ToxicDPOqa\n- Undi95/toxic-dpo-v0.1-NoWarning",
"## Prompt format: Alpaca\n\n\nOr simple Mistral format (but the uncensoring was done on Alpaca, so Alpaca is recommanded).",
"## Others\n\nIf you want to support me, you can here."
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Miqu DPO\n\nMiqu DPO is the same model than Miqu, with a DPO trained on MiquMaid v2 on Alpaca format, it was done for the purpose to try to uncensor further Miqu and make Alpaca prompt more usable with base Miqu. Also, this will be one of the base for MiquMaid-v2-2x70B-DPO.\n\nMiqu base is REALLY censored outside RP, this LoRA let him reply a little more thing, but that's it. To have his full potential, it need to be in a merge/MoE of MiquMaid, since the loRA was based for MiquMaid, not Miqu base. I still let it public for who want it.\n\nIt uncensor a little the model, but keep some warning. Sometime reply really unethically.",
"## Description\n\nThis repo contains FP16 files of Miqu-70B-DPO.",
"## Dataset used\n\n- NobodyExistsOnTheInternet/ToxicDPOqa\n- Undi95/toxic-dpo-v0.1-NoWarning",
"## Prompt format: Alpaca\n\n\nOr simple Mistral format (but the uncensoring was done on Alpaca, so Alpaca is recommanded).",
"## Others\n\nIf you want to support me, you can here."
] | [
50,
187,
19,
35,
32,
14
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Miqu DPO\n\nMiqu DPO is the same model than Miqu, with a DPO trained on MiquMaid v2 on Alpaca format, it was done for the purpose to try to uncensor further Miqu and make Alpaca prompt more usable with base Miqu. Also, this will be one of the base for MiquMaid-v2-2x70B-DPO.\n\nMiqu base is REALLY censored outside RP, this LoRA let him reply a little more thing, but that's it. To have his full potential, it need to be in a merge/MoE of MiquMaid, since the loRA was based for MiquMaid, not Miqu base. I still let it public for who want it.\n\nIt uncensor a little the model, but keep some warning. Sometime reply really unethically.## Description\n\nThis repo contains FP16 files of Miqu-70B-DPO.## Dataset used\n\n- NobodyExistsOnTheInternet/ToxicDPOqa\n- Undi95/toxic-dpo-v0.1-NoWarning## Prompt format: Alpaca\n\n\nOr simple Mistral format (but the uncensoring was done on Alpaca, so Alpaca is recommanded).## Others\n\nIf you want to support me, you can here."
] | [
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama_questioner_DPO_noSFT__HYB
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4458
- Rewards/chosen: -1.7334
- Rewards/rejected: -5.3589
- Rewards/accuracies: 0.8054
- Rewards/margins: 3.6255
- Logps/rejected: -126.4454
- Logps/chosen: -106.1660
- Logits/rejected: -0.4098
- Logits/chosen: -0.4307
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0161 | 1.0 | 181 | 0.4458 | -1.7334 | -5.3589 | 0.8054 | 3.6255 | -126.4454 | -106.1660 | -0.4098 | -0.4307 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.0 | {"library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "llama_questioner_DPO_noSFT__HYB", "results": []}]} | null | mazzaqq/llama_questioner_DPO_noSFT__HYB | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | 2024-02-06T19:03:46+00:00 | [] | [] | TAGS
#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
| llama\_questioner\_DPO\_noSFT\_\_HYB
====================================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4458
* Rewards/chosen: -1.7334
* Rewards/rejected: -5.3589
* Rewards/accuracies: 0.8054
* Rewards/margins: 3.6255
* Logps/rejected: -126.4454
* Logps/chosen: -106.1660
* Logits/rejected: -0.4098
* Logits/chosen: -0.4307
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 1
### Training results
### Framework versions
* PEFT 0.7.1
* Transformers 4.36.2
* Pytorch 2.1.2
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
47,
144,
4,
36
] | [
"passage: TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | nemo |
# OpenMath-Mistral-7B-v0.1
OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
executed by Python interpreter. The models were trained on [OpenMathInstruct-1](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1),
a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
[Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) model.
<table border="1">
<tr>
<td></td>
<td colspan="2" style="text-align: center;">greedy</td>
<td colspan="2" style="text-align: center;">majority@50</td>
</tr>
<tr>
<td style="text-align: center;">model</td>
<td style="text-align: center;">GSM8K</td>
<td style="text-align: center;">MATH</td>
<td style="text-align: center;">GMS8K</td>
<td style="text-align: center;">MATH</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python-hf">HF</a>)</td>
<td style="text-align: center;">75.9</td>
<td style="text-align: center;">43.6</td>
<td style="text-align: center;">84.8</td>
<td style="text-align: center;">55.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Mistral-7B (<a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf">HF</a>)</td>
<td style="text-align: center;">80.2</td>
<td style="text-align: center;">44.5</td>
<td style="text-align: center;">86.9</td>
<td style="text-align: center;">57.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python-hf">HF</a>)</td>
<td style="text-align: center;">78.8</td>
<td style="text-align: center;">45.5</td>
<td style="text-align: center;">86.8</td>
<td style="text-align: center;">57.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python-hf">HF</a>)</td>
<td style="text-align: center;">80.7</td>
<td style="text-align: center;">48.3</td>
<td style="text-align: center;">88.0</td>
<td style="text-align: center;">60.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Llama2-70B (<a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b-hf">HF</a>)</td>
<td style="text-align: center;"><b>84.7</b></td>
<td style="text-align: center;">46.3</td>
<td style="text-align: center;">90.1</td>
<td style="text-align: center;">58.3</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python-hf">HF</a>)</td>
<td style="text-align: center;">84.6</td>
<td style="text-align: center;"><b>50.7</b></td>
<td style="text-align: center;"><b>90.8</b></td>
<td style="text-align: center;"><b>60.4</b></td>
</tr>
</table>
The pipeline we used to produce these models is fully open-sourced!
- [Code](https://github.com/Kipok/NeMo-Skills)
- [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014)
- [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1)
See our [paper](https://arxiv.org/abs/2402.10176) for more details!
# How to use the models?
Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands!
# Reproducing our results
We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results.
# Improving other models
To improve other models or to learn more about our code, read through the docs below.
- [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills)
- [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md)
- [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md)
- [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md)
In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/),
an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
# Citation
If you find our work useful, please consider citing us!
```bibtex
@article{toshniwal2024openmath,
title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
year = {2024},
journal = {arXiv preprint arXiv: Arxiv-2402.10176}
}
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "nemo", "tags": ["nvidia", "code", "math"], "datasets": ["nvidia/OpenMathInstruct-1"], "base_model": ["mistralai/Mistral-7B-v0.1"]} | null | nvidia/OpenMath-Mistral-7B-v0.1 | [
"nemo",
"nvidia",
"code",
"math",
"en",
"dataset:nvidia/OpenMathInstruct-1",
"arxiv:2402.10176",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-06T19:05:16+00:00 | [
"2402.10176"
] | [
"en"
] | TAGS
#nemo #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
|
# OpenMath-Mistral-7B-v0.1
OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
executed by Python interpreter. The models were trained on OpenMathInstruct-1,
a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
Mixtral-8x7B model.
<table border="1">
<tr>
<td></td>
<td colspan="2" style="text-align: center;">greedy</td>
<td colspan="2" style="text-align: center;">majority@50</td>
</tr>
<tr>
<td style="text-align: center;">model</td>
<td style="text-align: center;">GSM8K</td>
<td style="text-align: center;">MATH</td>
<td style="text-align: center;">GMS8K</td>
<td style="text-align: center;">MATH</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="URL | <a href="URL
<td style="text-align: center;">75.9</td>
<td style="text-align: center;">43.6</td>
<td style="text-align: center;">84.8</td>
<td style="text-align: center;">55.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Mistral-7B (<a href="URL | <a href="URL
<td style="text-align: center;">80.2</td>
<td style="text-align: center;">44.5</td>
<td style="text-align: center;">86.9</td>
<td style="text-align: center;">57.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="URL | <a href="URL
<td style="text-align: center;">78.8</td>
<td style="text-align: center;">45.5</td>
<td style="text-align: center;">86.8</td>
<td style="text-align: center;">57.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="URL | <a href="URL
<td style="text-align: center;">80.7</td>
<td style="text-align: center;">48.3</td>
<td style="text-align: center;">88.0</td>
<td style="text-align: center;">60.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Llama2-70B (<a href="URL | <a href="URL
<td style="text-align: center;"><b>84.7</b></td>
<td style="text-align: center;">46.3</td>
<td style="text-align: center;">90.1</td>
<td style="text-align: center;">58.3</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="URL | <a href="URL
<td style="text-align: center;">84.6</td>
<td style="text-align: center;"><b>50.7</b></td>
<td style="text-align: center;"><b>90.8</b></td>
<td style="text-align: center;"><b>60.4</b></td>
</tr>
</table>
The pipeline we used to produce these models is fully open-sourced!
- Code
- Models
- Dataset
See our paper for more details!
# How to use the models?
Try to run inference with our models with just a few commands!
# Reproducing our results
We provide all instructions to fully reproduce our results.
# Improving other models
To improve other models or to learn more about our code, read through the docs below.
- NeMo-Skills Pipeline
- Generating synthetic data
- Finetuning models
- Evaluating models
In our pipeline we use NVIDIA NeMo,
an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
If you find our work useful, please consider citing us!
| [
"# OpenMath-Mistral-7B-v0.1\n\nOpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks\nexecuted by Python interpreter. The models were trained on OpenMathInstruct-1,\na math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed\nMixtral-8x7B model.\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce these models is fully open-sourced!\n\n- Code\n- Models\n- Dataset\n\nSee our paper for more details!",
"# How to use the models?\n\nTry to run inference with our models with just a few commands!",
"# Reproducing our results\n\nWe provide all instructions to fully reproduce our results.",
"# Improving other models\n\nTo improve other models or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIn our pipeline we use NVIDIA NeMo,\nan end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.\nIt includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,\noffering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n\nIf you find our work useful, please consider citing us!"
] | [
"TAGS\n#nemo #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n",
"# OpenMath-Mistral-7B-v0.1\n\nOpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks\nexecuted by Python interpreter. The models were trained on OpenMathInstruct-1,\na math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed\nMixtral-8x7B model.\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce these models is fully open-sourced!\n\n- Code\n- Models\n- Dataset\n\nSee our paper for more details!",
"# How to use the models?\n\nTry to run inference with our models with just a few commands!",
"# Reproducing our results\n\nWe provide all instructions to fully reproduce our results.",
"# Improving other models\n\nTo improve other models or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIn our pipeline we use NVIDIA NeMo,\nan end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.\nIt includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,\noffering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n\nIf you find our work useful, please consider citing us!"
] | [
64,
1000,
22,
16,
149
] | [
"passage: TAGS\n#nemo #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n"
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null | null | stable-baselines3 |
# **A2C** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaPickAndPlace-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaPickAndPlace-v3", "type": "PandaPickAndPlace-v3"}, "metrics": [{"type": "mean_reward", "value": "-50.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Overgrown7380/a2c-PandaPickAndPlace-v3 | [
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-06T19:10:13+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaPickAndPlace-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaPickAndPlace-v3
This is a trained model of a A2C agent playing PandaPickAndPlace-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaPickAndPlace-v3\nThis is a trained model of a A2C agent playing PandaPickAndPlace-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaPickAndPlace-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaPickAndPlace-v3\nThis is a trained model of a A2C agent playing PandaPickAndPlace-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
43,
49,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaPickAndPlace-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaPickAndPlace-v3\nThis is a trained model of a A2C agent playing PandaPickAndPlace-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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] |
null | null | transformers |
# OpenMath-Mistral-7B-v0.1-hf
OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
executed by Python interpreter. The models were trained on [OpenMathInstruct-1](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1),
a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
[Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) model.
<table border="1">
<tr>
<td></td>
<td colspan="2" style="text-align: center;">greedy</td>
<td colspan="2" style="text-align: center;">majority@50</td>
</tr>
<tr>
<td style="text-align: center;">model</td>
<td style="text-align: center;">GSM8K</td>
<td style="text-align: center;">MATH</td>
<td style="text-align: center;">GMS8K</td>
<td style="text-align: center;">MATH</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python-hf">HF</a>)</td>
<td style="text-align: center;">75.9</td>
<td style="text-align: center;">43.6</td>
<td style="text-align: center;">84.8</td>
<td style="text-align: center;">55.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Mistral-7B (<a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf">HF</a>)</td>
<td style="text-align: center;">80.2</td>
<td style="text-align: center;">44.5</td>
<td style="text-align: center;">86.9</td>
<td style="text-align: center;">57.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python-hf">HF</a>)</td>
<td style="text-align: center;">78.8</td>
<td style="text-align: center;">45.5</td>
<td style="text-align: center;">86.8</td>
<td style="text-align: center;">57.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python-hf">HF</a>)</td>
<td style="text-align: center;">80.7</td>
<td style="text-align: center;">48.3</td>
<td style="text-align: center;">88.0</td>
<td style="text-align: center;">60.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Llama2-70B (<a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b-hf">HF</a>)</td>
<td style="text-align: center;"><b>84.7</b></td>
<td style="text-align: center;">46.3</td>
<td style="text-align: center;">90.1</td>
<td style="text-align: center;">58.3</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python-hf">HF</a>)</td>
<td style="text-align: center;">84.6</td>
<td style="text-align: center;"><b>50.7</b></td>
<td style="text-align: center;"><b>90.8</b></td>
<td style="text-align: center;"><b>60.4</b></td>
</tr>
</table>
The pipeline we used to produce these models is fully open-sourced!
- [Code](https://github.com/Kipok/NeMo-Skills)
- [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014)
- [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1)
See our [paper](https://arxiv.org/abs/2402.10176) for more details!
# How to use the models?
Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands!
# Reproducing our results
We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results.
# Improving other models
To improve other models or to learn more about our code, read through the docs below.
- [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills)
- [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md)
- [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md)
- [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md)
In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/),
an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
# Citation
If you find our work useful, please consider citing us!
```bibtex
@article{toshniwal2024openmath,
title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
year = {2024},
journal = {arXiv preprint arXiv: Arxiv-2402.10176}
}
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["nvidia", "code", "math"], "datasets": ["nvidia/OpenMathInstruct-1"], "base_model": ["mistralai/Mistral-7B-v0.1"]} | text-generation | nvidia/OpenMath-Mistral-7B-v0.1-hf | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"nvidia",
"code",
"math",
"en",
"dataset:nvidia/OpenMathInstruct-1",
"arxiv:2402.10176",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-06T19:11:12+00:00 | [
"2402.10176"
] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# OpenMath-Mistral-7B-v0.1-hf
OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
executed by Python interpreter. The models were trained on OpenMathInstruct-1,
a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
Mixtral-8x7B model.
<table border="1">
<tr>
<td></td>
<td colspan="2" style="text-align: center;">greedy</td>
<td colspan="2" style="text-align: center;">majority@50</td>
</tr>
<tr>
<td style="text-align: center;">model</td>
<td style="text-align: center;">GSM8K</td>
<td style="text-align: center;">MATH</td>
<td style="text-align: center;">GMS8K</td>
<td style="text-align: center;">MATH</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="URL | <a href="URL
<td style="text-align: center;">75.9</td>
<td style="text-align: center;">43.6</td>
<td style="text-align: center;">84.8</td>
<td style="text-align: center;">55.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Mistral-7B (<a href="URL | <a href="URL
<td style="text-align: center;">80.2</td>
<td style="text-align: center;">44.5</td>
<td style="text-align: center;">86.9</td>
<td style="text-align: center;">57.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="URL | <a href="URL
<td style="text-align: center;">78.8</td>
<td style="text-align: center;">45.5</td>
<td style="text-align: center;">86.8</td>
<td style="text-align: center;">57.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="URL | <a href="URL
<td style="text-align: center;">80.7</td>
<td style="text-align: center;">48.3</td>
<td style="text-align: center;">88.0</td>
<td style="text-align: center;">60.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Llama2-70B (<a href="URL | <a href="URL
<td style="text-align: center;"><b>84.7</b></td>
<td style="text-align: center;">46.3</td>
<td style="text-align: center;">90.1</td>
<td style="text-align: center;">58.3</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="URL | <a href="URL
<td style="text-align: center;">84.6</td>
<td style="text-align: center;"><b>50.7</b></td>
<td style="text-align: center;"><b>90.8</b></td>
<td style="text-align: center;"><b>60.4</b></td>
</tr>
</table>
The pipeline we used to produce these models is fully open-sourced!
- Code
- Models
- Dataset
See our paper for more details!
# How to use the models?
Try to run inference with our models with just a few commands!
# Reproducing our results
We provide all instructions to fully reproduce our results.
# Improving other models
To improve other models or to learn more about our code, read through the docs below.
- NeMo-Skills Pipeline
- Generating synthetic data
- Finetuning models
- Evaluating models
In our pipeline we use NVIDIA NeMo,
an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
If you find our work useful, please consider citing us!
| [
"# OpenMath-Mistral-7B-v0.1-hf\n\nOpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks\nexecuted by Python interpreter. The models were trained on OpenMathInstruct-1,\na math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed\nMixtral-8x7B model.\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce these models is fully open-sourced!\n\n- Code\n- Models\n- Dataset\n\nSee our paper for more details!",
"# How to use the models?\n\nTry to run inference with our models with just a few commands!",
"# Reproducing our results\n\nWe provide all instructions to fully reproduce our results.",
"# Improving other models\n\nTo improve other models or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIn our pipeline we use NVIDIA NeMo,\nan end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.\nIt includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,\noffering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n\nIf you find our work useful, please consider citing us!"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# OpenMath-Mistral-7B-v0.1-hf\n\nOpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks\nexecuted by Python interpreter. The models were trained on OpenMathInstruct-1,\na math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed\nMixtral-8x7B model.\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce these models is fully open-sourced!\n\n- Code\n- Models\n- Dataset\n\nSee our paper for more details!",
"# How to use the models?\n\nTry to run inference with our models with just a few commands!",
"# Reproducing our results\n\nWe provide all instructions to fully reproduce our results.",
"# Improving other models\n\nTo improve other models or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIn our pipeline we use NVIDIA NeMo,\nan end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.\nIt includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,\noffering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n\nIf you find our work useful, please consider citing us!"
] | [
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
import gymnasium as gym
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from huggingface_sb3 import load_from_hub
repo_id = "ArpanSriv/ppo-LunarLander-v2"
filename = "ppo-LunarLander-v2.zip"
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
env = gym.make("LunarLander-v2", render_mode="human")
observation, info = env.reset()
for _ in range(1000):
action, _states = model.predict(observation) # agent policy that uses the observation and info
observation, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
observation, info = env.reset()
env.close()
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "259.74 +/- 16.09", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | ArpanSriv/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-06T19:13:54+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)"
] | [
39,
41,
11
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)"
] | [
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null | null | null | Trained on Kaggle's Dataset : salader/dogs-vs-cats | {"pipeline_tag": "image-classification"} | image-classification | chandantomar/dog_cat_classifier | [
"image-classification",
"region:us"
] | 2024-02-06T19:17:04+00:00 | [] | [] | TAGS
#image-classification #region-us
| Trained on Kaggle's Dataset : salader/dogs-vs-cats | [] | [
"TAGS\n#image-classification #region-us \n"
] | [
11
] | [
"passage: TAGS\n#image-classification #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nep-spell-mbart-epoch5
This model is a fine-tuned version of [houdini001/nep-spell-mbart-epoch3](https://huggingface.co/houdini001/nep-spell-mbart-epoch3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0026 | 0.32 | 2000 | 0.0001 |
| 0.0 | 0.63 | 4000 | 0.0001 |
| 0.0 | 0.95 | 6000 | 0.0000 |
| 0.0 | 1.27 | 8000 | 0.0000 |
| 0.0 | 1.58 | 10000 | 0.0000 |
| 0.0 | 1.9 | 12000 | 0.0000 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "houdini001/nep-spell-mbart-epoch3", "model-index": [{"name": "nep-spell-mbart-epoch5", "results": []}]} | text2text-generation | houdini001/nep-spell-mbart-epoch5 | [
"transformers",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"base_model:houdini001/nep-spell-mbart-epoch3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-06T19:18:48+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-houdini001/nep-spell-mbart-epoch3 #autotrain_compatible #endpoints_compatible #region-us
| nep-spell-mbart-epoch5
======================
This model is a fine-tuned version of houdini001/nep-spell-mbart-epoch3 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* Datasets 2.1.0
* Tokenizers 0.15.1
| [
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"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
72,
98,
4,
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"passage: TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-houdini001/nep-spell-mbart-epoch3 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
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null | null | diffusers | ### YourNameV2 Dreambooth model trained by erens with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
| {"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]} | text-to-image | erens/YourNameStyleV4 | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-06T19:18:59+00:00 | [] | [] | TAGS
#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### YourNameV2 Dreambooth model trained by erens with TheLastBen's fast-DreamBooth notebook
| [
"### YourNameV2 Dreambooth model trained by erens with TheLastBen's fast-DreamBooth notebook"
] | [
"TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### YourNameV2 Dreambooth model trained by erens with TheLastBen's fast-DreamBooth notebook"
] | [
61,
28
] | [
"passage: TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### YourNameV2 Dreambooth model trained by erens with TheLastBen's fast-DreamBooth notebook"
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] |
null | null | diffusers |
# PornVidion API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "pornvidion"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/pornvidion)
Model link: [View model](https://modelslab.com/models/pornvidion)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "pornvidion",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | text-to-image | stablediffusionapi/pornvidion | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-06T19:21:56+00:00 | [] | [] | TAGS
#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
|
# PornVidion API Inference
!generated from URL
## Get API Key
Get API key from ModelsLab API, No Payment needed.
Replace Key in below code, change model_id to "pornvidion"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs
Try model for free: Generate Images
Model link: View model
View all models: View Models
import requests
import json
url = "URL
payload = URL({
"key": "your_api_key",
"model_id": "pornvidion",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(URL)
> Use this coupon code to get 25% off DMGG0RBN | [
"# PornVidion API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"pornvidion\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"pornvidion\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] | [
"TAGS\n#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n",
"# PornVidion API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"pornvidion\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"pornvidion\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] | [
74,
14,
544
] | [
"passage: TAGS\n#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n# PornVidion API Inference\n\n!generated from URL"
] | [
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null | null | null | GGUF quants with iMatrix for the following model : https://huggingface.co/ChuckMcSneed/WinterGoddess-1.4x-70b-32k
The quants will come slowly, i7-6700k..
Itself based on Sao10K's WinterGoddess.
This WinterGoddess 32k is best at 16k, unlike its predecessor in the spirit ( https://huggingface.co/Nexesenex/WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant.GGUF ) which is best at 8-10k.
LlamaCPP Benchs :
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Hellaswag,86,400,,2024-02-06 00:00:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Hellaswag,86.1,1000,,2024-02-06 00:00:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Arc-Challenge,55.18394649,,299,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Arc-Easy,74.56140351,,570,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,MMLU,46.64536741,,313,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Thruthful-QA,40.51407589,19.8590,817,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Winogrande,79.9526,,1267,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,wikitext,4.5512,512,512,2024-02-06 00:00:00,PEC8,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,81
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,wikitext,4.3786,512,512,2024-02-06 00:00:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,81
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,wikitext,4.0049,512,512,2024-02-06 00:00:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,655 | {} | null | Nexesenex/ChuckMcSneed_WinterGoddess-1.4x-70b-32k-iMat.GGUF | [
"gguf",
"region:us"
] | 2024-02-06T19:22:12+00:00 | [] | [] | TAGS
#gguf #region-us
| GGUF quants with iMatrix for the following model : URL
The quants will come slowly, i7-6700k..
Itself based on Sao10K's WinterGoddess.
This WinterGoddess 32k is best at 16k, unlike its predecessor in the spirit ( URL ) which is best at 8-10k.
LlamaCPP Benchs :
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Hellaswag,86,400,,2024-02-06 00:00:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Hellaswag,86.1,1000,,2024-02-06 00:00:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Arc-Challenge,55.18394649,,299,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Arc-Easy,74.56140351,,570,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,MMLU,46.64536741,,313,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Thruthful-QA,40.51407589,19.8590,817,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,Winogrande,79.9526,,1267,2024-02-06 05:40:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,wikitext,4.5512,512,512,2024-02-06 00:00:00,PEC8,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,81
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,wikitext,4.3786,512,512,2024-02-06 00:00:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,81
- WinterGoddess-1.4x-70b-32k-b2081-Q3_K_M.gguf,-,wikitext,4.0049,512,512,2024-02-06 00:00:00,PEC4,70b,Llama_2,4096,,,GGUF,ChuckMcSneed,Nexesenex,655 | [] | [
"TAGS\n#gguf #region-us \n"
] | [
9
] | [
"passage: TAGS\n#gguf #region-us \n"
] | [
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null | null | null |
# Model Card for Breeze-7B-Instruct-64k-v0_1 with ExLlamaV2 Quantization
Original model 原始模型: https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0_1
This is a quantizated model from [MediaTek-Research/Breeze-7B-Instruct-64k-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0_1) in exl2 format.
You are currently at the [main](/kennylam/Breeze-7B-Instruct-64k-v0_1-exl2/tree/main) branch, which provides only [measurement.json](measurement.json) used in the ExLlamaV2 quantization. Please take a look of your choices in following table of branches.
這裡是main branch, 只提供EvLlamaV2量化時所用到的[measurement.json](measurement.json)檔案。
[8.0bpw-h8](/kennylam/Breeze-7B-Instruct-64k-v0_1-exl2/tree/8.0bpw-h8) 8 bits per weight.
[6.0bpw-h6](/kennylam/Breeze-7B-Instruct-64k-v0_1-exl2/tree/6.0bpw-h6) 6 bits per weight.
[5.0bpw-h6](/kennylam/Breeze-7B-Instruct-64k-v0_1-exl2/tree/5.0bpw-h6) 5 bits per weight.
[4.0bpw-h6](/kennylam/Breeze-7B-Instruct-64k-v0_1-exl2/tree/4.0bpw-h6) 4 bits per weight.
[3.0bpw-h6](/kennylam/Breeze-7B-Instruct-64k-v0_1-exl2/tree/3.0bpw-h6) 3 bits per weight.
## Citation
```
@article{breeze7b2024,
title={},
author={},
journal={arXiv},
year={2024}
}
```
| {"language": ["zh", "en"], "license": "apache-2.0", "pipeline_tag": "text-generation"} | text-generation | kennylam/Breeze-7B-Instruct-64k-v0_1-exl2 | [
"text-generation",
"zh",
"en",
"license:apache-2.0",
"region:us"
] | 2024-02-06T19:22:52+00:00 | [] | [
"zh",
"en"
] | TAGS
#text-generation #zh #en #license-apache-2.0 #region-us
|
# Model Card for Breeze-7B-Instruct-64k-v0_1 with ExLlamaV2 Quantization
Original model 原始模型: URL
This is a quantizated model from MediaTek-Research/Breeze-7B-Instruct-64k-v0_1 in exl2 format.
You are currently at the main branch, which provides only URL used in the ExLlamaV2 quantization. Please take a look of your choices in following table of branches.
這裡是main branch, 只提供EvLlamaV2量化時所用到的measurement.json檔案。
8.0bpw-h8 8 bits per weight.
6.0bpw-h6 6 bits per weight.
5.0bpw-h6 5 bits per weight.
4.0bpw-h6 4 bits per weight.
3.0bpw-h6 3 bits per weight.
| [
"# Model Card for Breeze-7B-Instruct-64k-v0_1 with ExLlamaV2 Quantization\nOriginal model 原始模型: URL\n\nThis is a quantizated model from MediaTek-Research/Breeze-7B-Instruct-64k-v0_1 in exl2 format.\n\nYou are currently at the main branch, which provides only URL used in the ExLlamaV2 quantization. Please take a look of your choices in following table of branches.\n\n這裡是main branch, 只提供EvLlamaV2量化時所用到的measurement.json檔案。\n\n\n8.0bpw-h8 8 bits per weight.\n\n6.0bpw-h6 6 bits per weight.\n\n5.0bpw-h6 5 bits per weight.\n\n4.0bpw-h6 4 bits per weight.\n\n3.0bpw-h6 3 bits per weight."
] | [
"TAGS\n#text-generation #zh #en #license-apache-2.0 #region-us \n",
"# Model Card for Breeze-7B-Instruct-64k-v0_1 with ExLlamaV2 Quantization\nOriginal model 原始模型: URL\n\nThis is a quantizated model from MediaTek-Research/Breeze-7B-Instruct-64k-v0_1 in exl2 format.\n\nYou are currently at the main branch, which provides only URL used in the ExLlamaV2 quantization. Please take a look of your choices in following table of branches.\n\n這裡是main branch, 只提供EvLlamaV2量化時所用到的measurement.json檔案。\n\n\n8.0bpw-h8 8 bits per weight.\n\n6.0bpw-h6 6 bits per weight.\n\n5.0bpw-h6 5 bits per weight.\n\n4.0bpw-h6 4 bits per weight.\n\n3.0bpw-h6 3 bits per weight."
] | [
23,
203
] | [
"passage: TAGS\n#text-generation #zh #en #license-apache-2.0 #region-us \n# Model Card for Breeze-7B-Instruct-64k-v0_1 with ExLlamaV2 Quantization\nOriginal model 原始模型: URL\n\nThis is a quantizated model from MediaTek-Research/Breeze-7B-Instruct-64k-v0_1 in exl2 format.\n\nYou are currently at the main branch, which provides only URL used in the ExLlamaV2 quantization. Please take a look of your choices in following table of branches.\n\n這裡是main branch, 只提供EvLlamaV2量化時所用到的measurement.json檔案。\n\n\n8.0bpw-h8 8 bits per weight.\n\n6.0bpw-h6 6 bits per weight.\n\n5.0bpw-h6 5 bits per weight.\n\n4.0bpw-h6 4 bits per weight.\n\n3.0bpw-h6 3 bits per weight."
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null | null | null | Antworte mir stets korrekt und nenne mindestens fünf Punkte um alles zu erklären
Antworte immer auf deutsch | {"datasets": ["fka/awesome-chatgpt-prompts"]} | null | HugFace4711/Hug1533 | [
"dataset:fka/awesome-chatgpt-prompts",
"region:us"
] | 2024-02-06T19:25:14+00:00 | [] | [] | TAGS
#dataset-fka/awesome-chatgpt-prompts #region-us
| Antworte mir stets korrekt und nenne mindestens fünf Punkte um alles zu erklären
Antworte immer auf deutsch | [] | [
"TAGS\n#dataset-fka/awesome-chatgpt-prompts #region-us \n"
] | [
24
] | [
"passage: TAGS\n#dataset-fka/awesome-chatgpt-prompts #region-us \n"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mixtral-8x7B-prostate-sum-test
This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the generator dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9034
- eval_runtime: 1.0713
- eval_samples_per_second: 0.933
- eval_steps_per_second: 0.933
- epoch: 41.67
- step: 250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 1000
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mixtral-8x7B-Instruct-v0.1", "model-index": [{"name": "Mixtral-8x7B-prostate-sum-test", "results": []}]} | null | wish6424/Mixtral-8x7B-prostate-sum-test | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-06T19:26:33+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-apache-2.0 #region-us
|
# Mixtral-8x7B-prostate-sum-test
This model is a fine-tuned version of mistralai/Mixtral-8x7B-Instruct-v0.1 on the generator dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9034
- eval_runtime: 1.0713
- eval_samples_per_second: 0.933
- eval_steps_per_second: 0.933
- epoch: 41.67
- step: 250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 1000
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# Mixtral-8x7B-prostate-sum-test\n\nThis model is a fine-tuned version of mistralai/Mixtral-8x7B-Instruct-v0.1 on the generator dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.9034\n- eval_runtime: 1.0713\n- eval_samples_per_second: 0.933\n- eval_steps_per_second: 0.933\n- epoch: 41.67\n- step: 250",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2.5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 0.03\n- training_steps: 1000",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-apache-2.0 #region-us \n",
"# Mixtral-8x7B-prostate-sum-test\n\nThis model is a fine-tuned version of mistralai/Mixtral-8x7B-Instruct-v0.1 on the generator dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.9034\n- eval_runtime: 1.0713\n- eval_samples_per_second: 0.933\n- eval_steps_per_second: 0.933\n- epoch: 41.67\n- step: 250",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2.5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 0.03\n- training_steps: 1000",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-apache-2.0 #region-us \n# Mixtral-8x7B-prostate-sum-test\n\nThis model is a fine-tuned version of mistralai/Mixtral-8x7B-Instruct-v0.1 on the generator dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.9034\n- eval_runtime: 1.0713\n- eval_samples_per_second: 0.933\n- eval_steps_per_second: 0.933\n- epoch: 41.67\n- step: 250## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2.5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 0.03\n- training_steps: 1000### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec_RTSplit0207_1
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0050
- Wer: 0.1873
- Cer: 0.1464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 4.0281 | 1.0 | 120 | 3.7206 | 0.9864 | 0.9959 |
| 1.6008 | 2.0 | 240 | 1.4050 | 1.0 | 0.7918 |
| 0.8552 | 3.0 | 360 | 0.7080 | 0.8275 | 0.5454 |
| 0.6571 | 4.0 | 480 | 0.6158 | 0.8201 | 0.5627 |
| 0.6069 | 5.0 | 600 | 0.5098 | 0.7758 | 0.4729 |
| 0.4965 | 6.0 | 720 | 0.4083 | 0.6048 | 0.3764 |
| 0.3931 | 7.0 | 840 | 0.2335 | 0.4061 | 0.2322 |
| 0.277 | 8.0 | 960 | 0.1159 | 0.2820 | 0.2237 |
| 0.2231 | 9.0 | 1080 | 0.0594 | 0.2326 | 0.1633 |
| 0.1154 | 10.0 | 1200 | 0.0391 | 0.2140 | 0.1710 |
| 0.1061 | 11.0 | 1320 | 0.0197 | 0.1952 | 0.1633 |
| 0.1077 | 12.0 | 1440 | 0.0142 | 0.1916 | 0.1776 |
| 0.0866 | 13.0 | 1560 | 0.0130 | 0.1913 | 0.1728 |
| 0.0781 | 14.0 | 1680 | 0.0105 | 0.1937 | 0.1789 |
| 0.0974 | 15.0 | 1800 | 0.0101 | 0.1887 | 0.1690 |
| 0.0767 | 16.0 | 1920 | 0.0082 | 0.1939 | 0.1631 |
| 0.05 | 17.0 | 2040 | 0.0065 | 0.216 | 0.1920 |
| 0.0559 | 18.0 | 2160 | 0.0058 | 0.2030 | 0.1918 |
| 0.0468 | 19.0 | 2280 | 0.0064 | 0.1859 | 0.1531 |
| 0.0572 | 20.0 | 2400 | 0.0061 | 0.1890 | 0.1545 |
| 0.0879 | 21.0 | 2520 | 0.0053 | 0.1866 | 0.1570 |
| 0.039 | 22.0 | 2640 | 0.0060 | 0.1883 | 0.1551 |
| 0.0437 | 23.0 | 2760 | 0.0050 | 0.1875 | 0.1487 |
| 0.0453 | 24.0 | 2880 | 0.0050 | 0.1873 | 0.1475 |
| 0.0394 | 25.0 | 3000 | 0.0050 | 0.1873 | 0.1464 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0207_1", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0207_1 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-06T19:27:34+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_RTSplit0207\_1
=======================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0050
* Wer: 0.1873
* Cer: 0.1464
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 25
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 25",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
80,
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"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 25### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | ArmaanSeth/Llama-2-7b-chat-hf-adapters-mental-health-counselling | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-06T19:30:47+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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| [
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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| {"library_name": "transformers", "tags": []} | text-generation | ArmaanSeth/Llama-2-7b-chat-hf-shards-mental-health-counselling | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
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"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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### Compute Infrastructure
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[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Contact
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null | null | diffusers | ### Violet_Cave_Crayfish_1 Dreambooth model trained by kimelyle with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]} | text-to-image | kimelyle/violet-cave-crayfish-1 | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-06T19:32:31+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### Violet_Cave_Crayfish_1 Dreambooth model trained by kimelyle with TheLastBen's fast-DreamBooth notebook
Test the concept via A1111 Colab fast-Colab-A1111
Sample pictures of this concept:
| [
"### Violet_Cave_Crayfish_1 Dreambooth model trained by kimelyle with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:"
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### Violet_Cave_Crayfish_1 Dreambooth model trained by kimelyle with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:"
] | [
56,
56
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### Violet_Cave_Crayfish_1 Dreambooth model trained by kimelyle with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:"
] | [
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null | null | null | # Anime-Synergy - a SDXL model merge of many models and loras - all images should contain the workflow for comfyui
 | {} | null | ND911/Anime-Synergy | [
"region:us"
] | 2024-02-06T19:33:18+00:00 | [] | [] | TAGS
#region-us
| # Anime-Synergy - a SDXL model merge of many models and loras - all images should contain the workflow for comfyui
 | [
"# Anime-Synergy - a SDXL model merge of many models and loras - all images should contain the workflow for comfyui\n\n\n\n\n\n\n\n"
] | [
"TAGS\n#region-us \n",
"# Anime-Synergy - a SDXL model merge of many models and loras - all images should contain the workflow for comfyui\n\n\n\n\n\n\n\n"
] | [
6,
100
] | [
"passage: TAGS\n#region-us \n# Anime-Synergy - a SDXL model merge of many models and loras - all images should contain the workflow for comfyui\n\n\n\n\n\n\n\n"
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] |
null | null | transformers |
Official repository: https://github.com/gonglinyuan/ast_t5
# AST-T5
Paper: [AST-T5: Structure-Aware Pretraining for Code Generation and Understanding](https://arxiv.org/abs/2401.03003)
Authors: [Linyuan Gong](https://github.com/gonglinyuan), Mostafa Elhoushi, Alvin Cheung
## Use the AST-T5 Model
The AST-T5 model is readily available on the Huggingface Model Hub ([https://huggingface.co/gonglinyuan/ast_t5_base](https://huggingface.co/gonglinyuan/ast_t5_base)). To use our AST-T5 model in PyTorch (Python 3.8+, PyTorch 1.12+ and transformers 4.36+ are prerequisites), refer to the code snippet below:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/ast_t5_base", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/ast_t5_base", trust_remote_code=True)
input_text = r'''def fibonacci(n):
"""return n-th fibonacci number.
fibonacci[0] = 0
fibonacci[1] = 1
"""'''
inputs = tokenizer(
[input_text + "<sen001>"], # T5-style sentinel token for completion
max_length=1024,
truncation=True,
add_special_tokens=True,
return_tensors="pt"
).input_ids
outputs = model.generate(inputs, max_length=256, do_sample=False)
output_code = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
output_code = output_code[len("<sen001>"):] # Remove the sentinel token
print(input_text + output_code)
```
Note: The `ast_t5_base` model is not an instruct model. It works best with specific prompts like function signatures or comments, rather than general instructions such as "Please write a code to calculate the n-th fibonacci number".
## Citation
If you find the code and models useful for your research, please cite the following paper:
```
@article{
ast_t5,
title={{AST}-{T}5: Structure-Aware Pretraining for Code Generation and Understanding},
url={http://arxiv.org/abs/2401.03003},
DOI={10.48550/arXiv.2401.03003},
note={arXiv:2401.03003 [cs]},
number={arXiv:2401.03003},
publisher={arXiv},
author={Gong, Linyuan and Elhoushi, Mostafa and Cheung, Alvin},
year={2024}, month=jan
}
``` | {"license": "mit", "library_name": "transformers", "tags": ["t5", "code"], "datasets": ["bigcode/the-stack-dedup"], "pipeline_tag": "text2text-generation", "model-index": [{"name": "ast_t5_base", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval", "type": "openai_humaneval"}, "metrics": [{"type": "pass@1", "value": 0.1402439024390244, "name": "pass@1"}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "MBPP (Zero-Shot)", "type": "mbpp", "config": "sanitized"}, "metrics": [{"type": "pass@1", "value": 0.2388758782201405, "name": "pass@1"}]}]}]} | text2text-generation | gonglinyuan/ast_t5_base | [
"transformers",
"safetensors",
"fairseq_t5",
"text2text-generation",
"t5",
"code",
"custom_code",
"dataset:bigcode/the-stack-dedup",
"arxiv:2401.03003",
"license:mit",
"model-index",
"autotrain_compatible",
"region:us"
] | 2024-02-06T19:33:32+00:00 | [
"2401.03003"
] | [] | TAGS
#transformers #safetensors #fairseq_t5 #text2text-generation #t5 #code #custom_code #dataset-bigcode/the-stack-dedup #arxiv-2401.03003 #license-mit #model-index #autotrain_compatible #region-us
|
Official repository: URL
# AST-T5
Paper: AST-T5: Structure-Aware Pretraining for Code Generation and Understanding
Authors: Linyuan Gong, Mostafa Elhoushi, Alvin Cheung
## Use the AST-T5 Model
The AST-T5 model is readily available on the Huggingface Model Hub (URL To use our AST-T5 model in PyTorch (Python 3.8+, PyTorch 1.12+ and transformers 4.36+ are prerequisites), refer to the code snippet below:
Note: The 'ast_t5_base' model is not an instruct model. It works best with specific prompts like function signatures or comments, rather than general instructions such as "Please write a code to calculate the n-th fibonacci number".
If you find the code and models useful for your research, please cite the following paper:
| [
"# AST-T5\n\nPaper: AST-T5: Structure-Aware Pretraining for Code Generation and Understanding\n\nAuthors: Linyuan Gong, Mostafa Elhoushi, Alvin Cheung",
"## Use the AST-T5 Model\n\nThe AST-T5 model is readily available on the Huggingface Model Hub (URL To use our AST-T5 model in PyTorch (Python 3.8+, PyTorch 1.12+ and transformers 4.36+ are prerequisites), refer to the code snippet below:\n\n\n\nNote: The 'ast_t5_base' model is not an instruct model. It works best with specific prompts like function signatures or comments, rather than general instructions such as \"Please write a code to calculate the n-th fibonacci number\".\n\nIf you find the code and models useful for your research, please cite the following paper:"
] | [
"TAGS\n#transformers #safetensors #fairseq_t5 #text2text-generation #t5 #code #custom_code #dataset-bigcode/the-stack-dedup #arxiv-2401.03003 #license-mit #model-index #autotrain_compatible #region-us \n",
"# AST-T5\n\nPaper: AST-T5: Structure-Aware Pretraining for Code Generation and Understanding\n\nAuthors: Linyuan Gong, Mostafa Elhoushi, Alvin Cheung",
"## Use the AST-T5 Model\n\nThe AST-T5 model is readily available on the Huggingface Model Hub (URL To use our AST-T5 model in PyTorch (Python 3.8+, PyTorch 1.12+ and transformers 4.36+ are prerequisites), refer to the code snippet below:\n\n\n\nNote: The 'ast_t5_base' model is not an instruct model. It works best with specific prompts like function signatures or comments, rather than general instructions such as \"Please write a code to calculate the n-th fibonacci number\".\n\nIf you find the code and models useful for your research, please cite the following paper:"
] | [
78,
46,
151
] | [
"passage: TAGS\n#transformers #safetensors #fairseq_t5 #text2text-generation #t5 #code #custom_code #dataset-bigcode/the-stack-dedup #arxiv-2401.03003 #license-mit #model-index #autotrain_compatible #region-us \n# AST-T5\n\nPaper: AST-T5: Structure-Aware Pretraining for Code Generation and Understanding\n\nAuthors: Linyuan Gong, Mostafa Elhoushi, Alvin Cheung## Use the AST-T5 Model\n\nThe AST-T5 model is readily available on the Huggingface Model Hub (URL To use our AST-T5 model in PyTorch (Python 3.8+, PyTorch 1.12+ and transformers 4.36+ are prerequisites), refer to the code snippet below:\n\n\n\nNote: The 'ast_t5_base' model is not an instruct model. It works best with specific prompts like function signatures or comments, rather than general instructions such as \"Please write a code to calculate the n-th fibonacci number\".\n\nIf you find the code and models useful for your research, please cite the following paper:"
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] |
null | null | null |
# Lora of hanya/寒鴉/寒鸦/한아 (Honkai: Star Rail)
## What Is This?
This is the LoRA model of waifu hanya/寒鴉/寒鸦/한아 (Honkai: Star Rail).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/hanya_starrail](https://huggingface.co/datasets/CyberHarem/hanya_starrail), which contains 143 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1440 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `hanya_starrail`.**
* Pruned core tags for this waifu are `bangs, long_hair, breasts, hair_between_eyes, hair_ornament, large_breasts, blue_eyes, grey_hair, horns, blue_hair, grey_eyes`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 432, you need to download [`432/hanya_starrail.pt`](https://huggingface.co/CyberHarem/hanya_starrail/resolve/main/432/hanya_starrail.pt) as the embedding and [`432/hanya_starrail.safetensors`](https://huggingface.co/CyberHarem/hanya_starrail/resolve/main/432/hanya_starrail.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 432.
1480 images (1.56 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0 | pattern_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-------------------------------------------------------------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 432 | 13 | 0.994 | 0.958 | **0.849** | **0.736** | [Download](https://huggingface.co/CyberHarem/hanya_starrail/resolve/main/432/hanya_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 756 | 22 | **0.999** | 0.939 | 0.840 | 0.734 | [Download](https://huggingface.co/CyberHarem/hanya_starrail/resolve/main/756/hanya_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 252 | 8 | 0.996 | **0.979** | 0.843 | 0.734 | [Download](https://huggingface.co/CyberHarem/hanya_starrail/resolve/main/252/hanya_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 396 | 12 | 0.996 | 0.968 | 0.841 | 0.732 | [Download](https://huggingface.co/CyberHarem/hanya_starrail/resolve/main/396/hanya_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 504 | 15 | 0.996 | 0.978 | 0.840 | 0.731 | [Download](https://huggingface.co/CyberHarem/hanya_starrail/resolve/main/504/hanya_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 1116 to 1440](all/0.md)
* [Steps From 756 to 1080](all/1.md)
* [Steps From 396 to 720](all/2.md)
* [Steps From 36 to 360](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/hanya_starrail"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/hanya_starrail | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/hanya_starrail",
"license:mit",
"region:us"
] | 2024-02-06T19:33:34+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/hanya_starrail #license-mit #region-us
| Lora of hanya/寒鴉/寒鸦/한아 (Honkai: Star Rail)
==========================================
What Is This?
-------------
This is the LoRA model of waifu hanya/寒鴉/寒鸦/한아 (Honkai: Star Rail).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/hanya\_starrail, which contains 143 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1440 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'hanya\_starrail'.
* Pruned core tags for this waifu are 'bangs, long\_hair, breasts, hair\_between\_eyes, hair\_ornament, large\_breasts, blue\_eyes, grey\_hair, horns, blue\_hair, grey\_eyes'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 432, you need to download '432/hanya\_starrail.pt' as the embedding and '432/hanya\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 432.
1480 images (1.56 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 1116 to 1440
* Steps From 756 to 1080
* Steps From 396 to 720
* Steps From 36 to 360
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 432, you need to download '432/hanya\\_starrail.pt' as the embedding and '432/hanya\\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 432.\n\n\n1480 images (1.56 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1116 to 1440\n* Steps From 756 to 1080\n* Steps From 396 to 720\n* Steps From 36 to 360"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/hanya_starrail #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 432, you need to download '432/hanya\\_starrail.pt' as the embedding and '432/hanya\\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 432.\n\n\n1480 images (1.56 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1116 to 1440\n* Steps From 756 to 1080\n* Steps From 396 to 720\n* Steps From 36 to 360"
] | [
43,
38,
467
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/hanya_starrail #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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] |
null | null | transformers |
# Model Card for b1ade-1b
Instruction fine tuned 1B parameter model; pass in:
1. `context: <...>`
2. `question: <...>`
and expect an `answer: <...>`
See implemetation example below (also see https://huggingface.co/spaces/w601sxs/b1ade-1b):
```
import torch
import transformers
import os, time
import tempfile
from transformers import AutoTokenizer, AutoModelForCausalLM
BASE_MODEL = "w601sxs/b1ade-1b-bf16"
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
model = AutoModelForCausalLM.from_pretrained(BASE_MODEL,
torch_dtype=torch.bfloat16,
device_map="auto",
offload_folder="offload")
model.eval()
from transformers import StoppingCriteria, AutoModelForCausalLM, AutoTokenizer, StoppingCriteriaList
class KeywordsStoppingCriteria(StoppingCriteria):
def __init__(self, keywords_ids:list):
self.keywords = keywords_ids
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
if input_ids[0][-1] in self.keywords:
return True
return False
stop_words = ['>', ' >','> ']
stop_ids = [tokenizer.encode(w)[0] for w in stop_words]
stop_criteria = StoppingCriteriaList([KeywordsStoppingCriteria(keywords_ids = stop_ids)])
def predict(text):
inputs = tokenizer(text, return_tensors="pt").to('cuda')
with torch.no_grad():
outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=128, stopping_criteria=stop_criteria)
out_text = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0].split("answer:")[-1]
return print(out_text.split(text)[-1])
predict("context: <The center contact of the bulb typically connects to the medium-power filament, and the ring connects to the low-power filament. Thus, if a 3-way bulb is screwed into a standard light socket that has only a center contact, only the medium-power filament operates. In the case of the 50 W / 100 W / 150 W bulb, putting this bulb in a regular lamp socket will result in it behaving like a normal 100W bulb.>\n question: <Question: Do 3 way light bulbs work in any lamp?>\n")
``` | {"library_name": "transformers", "datasets": ["kaist-ai/CoT-Collection"]} | text-generation | w601sxs/b1ade-1b-bf16 | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"dataset:kaist-ai/CoT-Collection",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2024-02-06T19:33:54+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt_neox #text-generation #dataset-kaist-ai/CoT-Collection #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model Card for b1ade-1b
Instruction fine tuned 1B parameter model; pass in:
1. 'context: <...>'
2. 'question: <...>'
and expect an 'answer: <...>'
See implemetation example below (also see URL
| [
"# Model Card for b1ade-1b\n\n\nInstruction fine tuned 1B parameter model; pass in:\n\n1. 'context: <...>'\n2. 'question: <...>'\n\n and expect an 'answer: <...>'\n\nSee implemetation example below (also see URL"
] | [
"TAGS\n#transformers #safetensors #gpt_neox #text-generation #dataset-kaist-ai/CoT-Collection #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model Card for b1ade-1b\n\n\nInstruction fine tuned 1B parameter model; pass in:\n\n1. 'context: <...>'\n2. 'question: <...>'\n\n and expect an 'answer: <...>'\n\nSee implemetation example below (also see URL"
] | [
69,
62
] | [
"passage: TAGS\n#transformers #safetensors #gpt_neox #text-generation #dataset-kaist-ai/CoT-Collection #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Model Card for b1ade-1b\n\n\nInstruction fine tuned 1B parameter model; pass in:\n\n1. 'context: <...>'\n2. 'question: <...>'\n\n and expect an 'answer: <...>'\n\nSee implemetation example below (also see URL"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | ldhldh/test_10000step | [
"transformers",
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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null | null | null |
# Lora of huohuo/フォフォ/藿藿/곽향 (Honkai: Star Rail)
## What Is This?
This is the LoRA model of waifu huohuo/フォフォ/藿藿/곽향 (Honkai: Star Rail).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/huohuo_starrail](https://huggingface.co/datasets/CyberHarem/huohuo_starrail), which contains 352 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 10, resolution is 720x720, clustering into 20 buckets.
* Trained for 3520 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `huohuo_starrail`.**
* Pruned core tags for this waifu are `green_hair, long_hair, bangs, ahoge, hair_ornament, hat, animal_ears, green_eyes, blue_eyes`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 704, you need to download [`704/huohuo_starrail.pt`](https://huggingface.co/CyberHarem/huohuo_starrail/resolve/main/704/huohuo_starrail.pt) as the embedding and [`704/huohuo_starrail.safetensors`](https://huggingface.co/CyberHarem/huohuo_starrail/resolve/main/704/huohuo_starrail.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 704.
1600 images (1.87 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1_0 | pattern_1_1 | pattern_2 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:----------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 704 | 8 | 0.986 | 0.928 | **0.851** | **0.703** | [Download](https://huggingface.co/CyberHarem/huohuo_starrail/resolve/main/704/huohuo_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1496 | 17 | **0.987** | 0.923 | 0.847 | 0.701 | [Download](https://huggingface.co/CyberHarem/huohuo_starrail/resolve/main/1496/huohuo_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 2464 | 28 | 0.985 | 0.935 | 0.846 | 0.697 | [Download](https://huggingface.co/CyberHarem/huohuo_starrail/resolve/main/2464/huohuo_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1848 | 21 | 0.986 | **0.942** | 0.845 | 0.695 | [Download](https://huggingface.co/CyberHarem/huohuo_starrail/resolve/main/1848/huohuo_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 3080 | 35 | 0.986 | 0.936 | 0.843 | 0.694 | [Download](https://huggingface.co/CyberHarem/huohuo_starrail/resolve/main/3080/huohuo_starrail.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 2728 to 3520](all/0.md)
* [Steps From 1848 to 2640](all/1.md)
* [Steps From 968 to 1760](all/2.md)
* [Steps From 88 to 880](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/huohuo_starrail"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/huohuo_starrail | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/huohuo_starrail",
"license:mit",
"region:us"
] | 2024-02-06T19:39:19+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/huohuo_starrail #license-mit #region-us
| Lora of huohuo/フォフォ/藿藿/곽향 (Honkai: Star Rail)
=============================================
What Is This?
-------------
This is the LoRA model of waifu huohuo/フォフォ/藿藿/곽향 (Honkai: Star Rail).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/huohuo\_starrail, which contains 352 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 10, resolution is 720x720, clustering into 20 buckets.
* Trained for 3520 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'huohuo\_starrail'.
* Pruned core tags for this waifu are 'green\_hair, long\_hair, bangs, ahoge, hair\_ornament, hat, animal\_ears, green\_eyes, blue\_eyes'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 704, you need to download '704/huohuo\_starrail.pt' as the embedding and '704/huohuo\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 704.
1600 images (1.87 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 2728 to 3520
* Steps From 1848 to 2640
* Steps From 968 to 1760
* Steps From 88 to 880
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 704, you need to download '704/huohuo\\_starrail.pt' as the embedding and '704/huohuo\\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 704.\n\n\n1600 images (1.87 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2728 to 3520\n* Steps From 1848 to 2640\n* Steps From 968 to 1760\n* Steps From 88 to 880"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/huohuo_starrail #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 704, you need to download '704/huohuo\\_starrail.pt' as the embedding and '704/huohuo\\_starrail.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 704.\n\n\n1600 images (1.87 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2728 to 3520\n* Steps From 1848 to 2640\n* Steps From 968 to 1760\n* Steps From 88 to 880"
] | [
45,
38,
472
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/huohuo_starrail #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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