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# **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="Paquique/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": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Paquique/Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-07T12:35:51+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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | YashRawal225/Intel-3-7b-chat-finetune-german2000 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #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
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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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## 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]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## 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. -->
# hubert_RTSPsplit_0207_1
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0153
- Wer: 0.1824
- Cer: 0.0742
## 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: 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 13.9576 | 1.0 | 60 | 11.6234 | 0.9370 | 0.9807 |
| 7.2225 | 2.0 | 120 | 6.6871 | 0.9370 | 0.9807 |
| 5.5382 | 3.0 | 180 | 5.2940 | 0.9370 | 0.9807 |
| 4.2349 | 4.0 | 240 | 4.0352 | 0.9370 | 0.9807 |
| 3.3648 | 5.0 | 300 | 3.2152 | 0.9370 | 0.9807 |
| 2.7737 | 6.0 | 360 | 2.6152 | 0.9370 | 0.9807 |
| 1.9888 | 7.0 | 420 | 1.8691 | 1.0 | 0.8906 |
| 1.4444 | 8.0 | 480 | 1.3196 | 1.0 | 0.5858 |
| 1.0971 | 9.0 | 540 | 0.9712 | 0.9629 | 0.5286 |
| 0.8494 | 10.0 | 600 | 0.7902 | 0.7976 | 0.4642 |
| 0.8617 | 11.0 | 660 | 0.7360 | 0.7887 | 0.4731 |
| 0.7085 | 12.0 | 720 | 0.6723 | 0.8102 | 0.4526 |
| 0.7197 | 13.0 | 780 | 0.7549 | 0.7968 | 0.3755 |
| 0.6945 | 14.0 | 840 | 0.6434 | 0.7909 | 0.3957 |
| 0.703 | 15.0 | 900 | 0.6780 | 0.7512 | 0.4001 |
| 0.6856 | 16.0 | 960 | 0.6239 | 0.7768 | 0.4035 |
| 0.6613 | 17.0 | 1020 | 0.6673 | 0.7653 | 0.3924 |
| 1.2141 | 18.0 | 1080 | 0.6085 | 0.7816 | 0.3975 |
| 0.706 | 19.0 | 1140 | 0.6005 | 0.7653 | 0.3762 |
| 0.6171 | 20.0 | 1200 | 0.5652 | 0.7575 | 0.3894 |
| 0.571 | 21.0 | 1260 | 0.5356 | 0.7634 | 0.3745 |
| 0.7265 | 22.0 | 1320 | 0.5377 | 0.7568 | 0.3754 |
| 0.5664 | 23.0 | 1380 | 0.5183 | 0.7697 | 0.3823 |
| 0.5071 | 24.0 | 1440 | 0.4597 | 0.7423 | 0.3870 |
| 0.4793 | 25.0 | 1500 | 0.4346 | 0.7364 | 0.3579 |
| 0.4421 | 26.0 | 1560 | 0.3954 | 0.7360 | 0.3560 |
| 0.4275 | 27.0 | 1620 | 0.3686 | 0.7253 | 0.3852 |
| 0.4101 | 28.0 | 1680 | 0.3536 | 0.7293 | 0.3960 |
| 0.3288 | 29.0 | 1740 | 0.3105 | 0.7015 | 0.3405 |
| 0.315 | 30.0 | 1800 | 0.2586 | 0.6630 | 0.3297 |
| 0.2939 | 31.0 | 1860 | 0.2205 | 0.6444 | 0.3227 |
| 0.2718 | 32.0 | 1920 | 0.1842 | 0.4690 | 0.2080 |
| 0.2198 | 33.0 | 1980 | 0.1100 | 0.2881 | 0.1063 |
| 0.1595 | 34.0 | 2040 | 0.0802 | 0.2429 | 0.1062 |
| 0.2805 | 35.0 | 2100 | 0.0791 | 0.2518 | 0.1016 |
| 0.1224 | 36.0 | 2160 | 0.0636 | 0.2429 | 0.0924 |
| 0.1107 | 37.0 | 2220 | 0.0663 | 0.2418 | 0.0936 |
| 0.0989 | 38.0 | 2280 | 0.0459 | 0.2143 | 0.0732 |
| 0.1028 | 39.0 | 2340 | 0.0502 | 0.2154 | 0.0713 |
| 0.142 | 40.0 | 2400 | 0.0375 | 0.2076 | 0.0704 |
| 0.0701 | 41.0 | 2460 | 0.0319 | 0.2013 | 0.0861 |
| 0.0673 | 42.0 | 2520 | 0.0294 | 0.2010 | 0.0884 |
| 0.0731 | 43.0 | 2580 | 0.0596 | 0.1969 | 0.0846 |
| 0.064 | 44.0 | 2640 | 0.0217 | 0.1865 | 0.0737 |
| 0.1049 | 45.0 | 2700 | 0.0229 | 0.1902 | 0.0838 |
| 0.0655 | 46.0 | 2760 | 0.0180 | 0.1835 | 0.0761 |
| 0.0587 | 47.0 | 2820 | 0.0177 | 0.1832 | 0.0730 |
| 0.0486 | 48.0 | 2880 | 0.0160 | 0.1817 | 0.0751 |
| 0.0588 | 49.0 | 2940 | 0.0158 | 0.1828 | 0.0751 |
| 0.0529 | 50.0 | 3000 | 0.0153 | 0.1824 | 0.0742 |
### 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": "rinna/japanese-hubert-base", "model-index": [{"name": "hubert_RTSPsplit_0207_1", "results": []}]} | automatic-speech-recognition | tndklab/hubert_RTSPsplit_0207_1 | [
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:rinna/japanese-hubert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:37:35+00:00 | [] | [] | TAGS
#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us
| hubert\_RTSPsplit\_0207\_1
==========================
This model is a fine-tuned version of rinna/japanese-hubert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0153
* Wer: 0.1824
* Cer: 0.0742
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: 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: 50
### 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",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: 50",
"### 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"
] | [
66,
115,
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"passage: TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #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: 0.0002\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: 50### 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 trained on Tiny Stories. Followed up with conversations datasets, followed up with trimmed Cinder Dataset.
Mini Cinder is ok at conversation and story telling for kids stories.
Overview Cinder is an AI chatbot tailored for engaging users in scientific and educational conversations, offering companionship, and sparking imaginative exploration.
This Cinder still has a lot to learn but is very friendly and enjoys telling stories.
Main Character Cinder: AI companion and quirky robot.
Cozmo: The silly one.
Vector: The serious one.
Computer Voice: The narrator.
User: Ship member.
| {"license": "mit", "widget": [{"text": "<|user|>\nCan you tell me a space adventure story?</s>\n<|assistant|>"}]} | text-generation | Josephgflowers/160M-TinyLLama-Mini-Cinder | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T12:39:27+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model trained on Tiny Stories. Followed up with conversations datasets, followed up with trimmed Cinder Dataset.
Mini Cinder is ok at conversation and story telling for kids stories.
Overview Cinder is an AI chatbot tailored for engaging users in scientific and educational conversations, offering companionship, and sparking imaginative exploration.
This Cinder still has a lot to learn but is very friendly and enjoys telling stories.
Main Character Cinder: AI companion and quirky robot.
Cozmo: The silly one.
Vector: The serious one.
Computer Voice: The narrator.
User: Ship member.
!image/png
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
52
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
null | null | transformers |
# 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)
``` | {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | rodrigoasth/autotrain-snkt5-e2mzt | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation",
"conversational",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:39:40+00:00 | [] | [
"en"
] | TAGS
#transformers #tensorboard #safetensors #autotrain #text-generation #conversational #en #license-other #endpoints_compatible #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#transformers #tensorboard #safetensors #autotrain #text-generation #conversational #en #license-other #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
46,
29,
3
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #autotrain #text-generation #conversational #en #license-other #endpoints_compatible #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 |
<!-- 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. -->
# Ellis-QA
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 0.4893 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "base_model": "deepset/roberta-base-squad2", "model-index": [{"name": "Ellis-QA", "results": []}]} | question-answering | gsl22/Ellis-QA | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:deepset/roberta-base-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:42:05+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #base_model-deepset/roberta-base-squad2 #license-cc-by-4.0 #endpoints_compatible #region-us
| Ellis-QA
========
This model is a fine-tuned version of deepset/roberta-base-squad2 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: 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: 1
### 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: 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: 1",
"### 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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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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: 1",
"### 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"
] | [
67,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #base_model-deepset/roberta-base-squad2 #license-cc-by-4.0 #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: 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: 1### 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 | diffusers |
# SDXL LoRA DreamBooth - yaneq/jan_azS4_SDXL_LoRA_500_9d94_
<Gallery />
## Model description
These are yaneq/jan_azS4_SDXL_LoRA_500_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_azS4_SDXL_LoRA_500_9d94_/tree/main) them in the Files & versions tab.
## Training properties
- max_train_steps: 500
- learning_rate: 1e-05
- 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%2FazS4WZxJtGAuVzhZxuys%2FazS4WZxJtGAuVzhZxuys%2F69o1vZPLc7GJXGlpAMMH.jpg?alt=media&token=b01bdfc5-1645-49b4-ac96-726ab2a3fbc3
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FazS4WZxJtGAuVzhZxuys%2FazS4WZxJtGAuVzhZxuys%2F8WWFXPruZHIDj9gfH3jx.jpg?alt=media&token=6c57b1ea-49fa-4321-83de-d59641f24aea
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FazS4WZxJtGAuVzhZxuys%2FazS4WZxJtGAuVzhZxuys%2FK6UvnghSTdYvrdPpLYoq.jpg?alt=media&token=4eeafb6d-ce6f-417a-b6d8-e50c25ca4368
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FazS4WZxJtGAuVzhZxuys%2FazS4WZxJtGAuVzhZxuys%2FVKPcuAllJieRnqxM6yfg.jpg?alt=media&token=fdbb8903-fad7-472c-a394-061a5dcef8aa
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FazS4WZxJtGAuVzhZxuys%2FazS4WZxJtGAuVzhZxuys%2FcKSlO7eCieu2lR7aFa7u.jpg?alt=media&token=b4ffea94-ca2a-4cf2-bcf1-c49e764fe707
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FazS4WZxJtGAuVzhZxuys%2FazS4WZxJtGAuVzhZxuys%2Fts5tpMOSpccBu5qqsTom.jpg?alt=media&token=b8b980ec-daff-46d6-b69b-9d697be73021
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FazS4WZxJtGAuVzhZxuys%2FazS4WZxJtGAuVzhZxuys%2FxXz3PRjpU8X7Ws9DHyxk.jpg?alt=media&token=95cd6951-3f17-4c7f-9749-4f8fd8e500c6
- https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FazS4WZxJtGAuVzhZxuys%2FazS4WZxJtGAuVzhZxuys%2FyyyIJgCkhtFaDGgHWrTO.jpg?alt=media&token=5ae68c94-86d8-483c-9cc5-9a00f124662e
- gradient_accumulation_steps: 3
- GPU: T4
- duration: 3796.2920064926147
| {"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_azS4_SDXL_LoRA_500_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"
] | 2024-02-07T12:44:32+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_azS4_SDXL_LoRA_500_9d94_
<Gallery />
## Model description
These are yaneq/jan_azS4_SDXL_LoRA_500_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-05
- 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
- GPU: T4
- duration: 3796.2920064926147
| [
"# SDXL LoRA DreamBooth - yaneq/jan_azS4_SDXL_LoRA_500_9d94_\n\n<Gallery />",
"## Model description\n\nThese are yaneq/jan_azS4_SDXL_LoRA_500_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-05\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\n- GPU: T4\n- duration: 3796.2920064926147"
] | [
"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_azS4_SDXL_LoRA_500_9d94_\n\n<Gallery />",
"## Model description\n\nThese are yaneq/jan_azS4_SDXL_LoRA_500_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-05\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\n- GPU: T4\n- duration: 3796.2920064926147"
] | [
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"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_azS4_SDXL_LoRA_500_9d94_\n\n<Gallery />## Model description\n\nThese are yaneq/jan_azS4_SDXL_LoRA_500_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-05\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\n- GPU: T4\n- duration: 3796.2920064926147"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "roberta-large"} | null | bsurendar/roberta-large-peft-lora | [
"peft",
"safetensors",
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-roberta-large #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Language(s) (NLP):
- License:
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### 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
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APA:
## Glossary [optional]
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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": "267.74 +/- 21.17", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | arnabmukherjee/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T12:52:33+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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Jayem-11/zephyr-7b-beta_assistant_v0.2 | [
"transformers",
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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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### Model Sources [optional]
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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.
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### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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#### Factors
#### Metrics
### Results
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null | null | transformers | <img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1/resolve/main/llama-tr-image.jpeg"
alt="drawing" width="400"/>
## Trendyol LLM 7b base v0.1
- **Model creator:** [Trendyol](https://huggingface.co/Trendyol)
- **Original model:** [Trendyol-LLM-7b-base-v0.1](https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Trendyol's Trendyol LLM 7b base v0.1](https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1)
<!-- description end --> | {"language": ["tr", "en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "Trendyol/Trendyol-LLM-7b-base-v0.1", "pipeline_tag": "text-generation", "model_type": "llama", "inference": false} | text-generation | faraday/trendyol-llm-7b-base-v0.1-gguf | [
"transformers",
"gguf",
"text-generation",
"tr",
"en",
"base_model:Trendyol/Trendyol-LLM-7b-base-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-07T12:53:29+00:00 | [] | [
"tr",
"en"
] | TAGS
#transformers #gguf #text-generation #tr #en #base_model-Trendyol/Trendyol-LLM-7b-base-v0.1 #license-apache-2.0 #region-us
| <img src="URL
alt="drawing" width="400"/>
## Trendyol LLM 7b base v0.1
- Model creator: Trendyol
- Original model: Trendyol-LLM-7b-base-v0.1
## Description
This repo contains GGUF format model files for Trendyol's Trendyol LLM 7b base v0.1
| [
"## Trendyol LLM 7b base v0.1\n- Model creator: Trendyol\n- Original model: Trendyol-LLM-7b-base-v0.1",
"## Description\nThis repo contains GGUF format model files for Trendyol's Trendyol LLM 7b base v0.1"
] | [
"TAGS\n#transformers #gguf #text-generation #tr #en #base_model-Trendyol/Trendyol-LLM-7b-base-v0.1 #license-apache-2.0 #region-us \n",
"## Trendyol LLM 7b base v0.1\n- Model creator: Trendyol\n- Original model: Trendyol-LLM-7b-base-v0.1",
"## Description\nThis repo contains GGUF format model files for Trendyol's Trendyol LLM 7b base v0.1"
] | [
51,
32,
26
] | [
"passage: TAGS\n#transformers #gguf #text-generation #tr #en #base_model-Trendyol/Trendyol-LLM-7b-base-v0.1 #license-apache-2.0 #region-us \n## Trendyol LLM 7b base v0.1\n- Model creator: Trendyol\n- Original model: Trendyol-LLM-7b-base-v0.1## Description\nThis repo contains GGUF format model files for Trendyol's Trendyol LLM 7b base v0.1"
] | [
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null | null | diffusers |
# BRIA 2.2 ControlNet Recoloring Model Card
[***Click here for Demo***](https://huggingface.co/spaces/briaai/BRIA-2.2-ControlNet-Recoloring)
BRIA 2.2 ControlNet-Recoloring, trained on the foundation of [BRIA 2.2 Text-to-Image](https://huggingface.co/briaai/BRIA-2.2), enables the generation of high-quality images guided by a textual prompt and the grayscale image of the input image. This allows for the creation of different variations of an image, all sharing the same geometry.
[BRIA 2.2](https://huggingface.co/briaai/BRIA-2.2) was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.
### Model Description
- **Developed by:** BRIA AI
- **Model type:** [ControlNet](https://huggingface.co/docs/diffusers/using-diffusers/controlnet) for Latent diffusion
- **License:** [bria-2.2](https://bria.ai/bria-huggingface-model-license-agreement/)
- **Model Description:** ControlNet Recoloring for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the grayscale image of the conditioned image.
- **Resources for more information:** [BRIA AI](https://bria.ai/)
### Get Access
BRIA 2.2 ControlNet-Recoloring requires access to BRIA 2.2 Text-to-Image. For more information, [click here](https://huggingface.co/briaai/BRIA-2.2).
### Code example using Diffusers
```
pip install diffusers
```
```py
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
import torch
controlnet = ControlNetModel.from_pretrained(
"briaai/BRIA-2.2-ControlNet-Recoloring",
torch_dtype=torch.float16
)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"briaai/BRIA-2.2",
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe.to("cuda")
prompt = "A portrait of a Beautiful and playful ethereal singer, golden designs, highly detailed, blurry background"
negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
# Calculate Recoloring image
input_image = cv2.imread('pics/singer.png')
recoloring_image = Image.fromarray(input_image).convert('L').convert('RGB')
image = pipe(prompt=prompt, negative_prompt=negative_prompt, image=recoloring_image, controlnet_conditioning_scale=1.0, height=1024, width=1024).images[0]
``` | {"license": "other", "tags": ["text-to-image", "controlnet model", "legal liability", "commercial use"], "license_name": "bria-2.2", "license_link": "https://bria.ai/customer-general-terms-and-conditions", "inference": false, "extra_gated_prompt": "This model weights by BRIA AI can be obtained after a commercial license is agreed upon. Fill in the form below and we reach out to you.", "extra_gated_fields": {"Name": "text", "Company/Org name": "text", "Org Type (Early/Growth Startup, Enterprise, Academy)": "text", "Role": "text", "Country": "text", "Email": "text", "By submitting this form, I agree to BRIA\u2019s Privacy policy and Terms & conditions, see links below": "checkbox"}} | text-to-image | briaai/BRIA-2.2-ControlNet-Recoloring | [
"diffusers",
"text-to-image",
"controlnet model",
"legal liability",
"commercial use",
"license:other",
"has_space",
"diffusers:ControlNetModel",
"region:us"
] | 2024-02-07T12:58:22+00:00 | [] | [] | TAGS
#diffusers #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #diffusers-ControlNetModel #region-us
|
# BRIA 2.2 ControlNet Recoloring Model Card
*Click here for Demo*
BRIA 2.2 ControlNet-Recoloring, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the grayscale image of the input image. This allows for the creation of different variations of an image, all sharing the same geometry.
BRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.
!controlnet_recoloring_showoff.png
### Model Description
- Developed by: BRIA AI
- Model type: ControlNet for Latent diffusion
- License: bria-2.2
- Model Description: ControlNet Recoloring for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the grayscale image of the conditioned image.
- Resources for more information: BRIA AI
### Get Access
BRIA 2.2 ControlNet-Recoloring requires access to BRIA 2.2 Text-to-Image. For more information, click here.
### Code example using Diffusers
| [
"# BRIA 2.2 ControlNet Recoloring Model Card\n\n\n*Click here for Demo*\n\n\nBRIA 2.2 ControlNet-Recoloring, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the grayscale image of the input image. This allows for the creation of different variations of an image, all sharing the same geometry. \n\n\nBRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!controlnet_recoloring_showoff.png",
"### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.2\n\n- Model Description: ControlNet Recoloring for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the grayscale image of the conditioned image.\n- Resources for more information: BRIA AI",
"### Get Access\nBRIA 2.2 ControlNet-Recoloring requires access to BRIA 2.2 Text-to-Image. For more information, click here.",
"### Code example using Diffusers"
] | [
"TAGS\n#diffusers #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #diffusers-ControlNetModel #region-us \n",
"# BRIA 2.2 ControlNet Recoloring Model Card\n\n\n*Click here for Demo*\n\n\nBRIA 2.2 ControlNet-Recoloring, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the grayscale image of the input image. This allows for the creation of different variations of an image, all sharing the same geometry. \n\n\nBRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!controlnet_recoloring_showoff.png",
"### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.2\n\n- Model Description: ControlNet Recoloring for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the grayscale image of the conditioned image.\n- Resources for more information: BRIA AI",
"### Get Access\nBRIA 2.2 ControlNet-Recoloring requires access to BRIA 2.2 Text-to-Image. For more information, click here.",
"### Code example using Diffusers"
] | [
46,
200,
81,
32,
8
] | [
"passage: TAGS\n#diffusers #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #diffusers-ControlNetModel #region-us \n# BRIA 2.2 ControlNet Recoloring Model Card\n\n\n*Click here for Demo*\n\n\nBRIA 2.2 ControlNet-Recoloring, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the grayscale image of the input image. This allows for the creation of different variations of an image, all sharing the same geometry. \n\n\nBRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!controlnet_recoloring_showoff.png### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.2\n\n- Model Description: ControlNet Recoloring for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the grayscale image of the conditioned image.\n- Resources for more information: BRIA AI### Get Access\nBRIA 2.2 ControlNet-Recoloring requires access to BRIA 2.2 Text-to-Image. For more information, click here.### Code example using Diffusers"
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null | null | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-PixelCopter_v2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "26.10 +/- 18.83", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | alexgastev/Reinforce-PixelCopter_v2 | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-07T12:58:35+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
41,
58
] | [
"passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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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 | Abhishek-1011/my_gec | [
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:58:48+00:00 | [] | [] | TAGS
#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #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 #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
37,
29,
3
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"passage: TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #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 | 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. -->
# bertin-gpt-j-6B_4bit_13
This model is a fine-tuned version of [bertin-project/bertin-gpt-j-6B](https://huggingface.co/bertin-project/bertin-gpt-j-6B) 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: 1.41e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "bertin-project/bertin-gpt-j-6B", "model-index": [{"name": "bertin-gpt-j-6B_4bit_13", "results": []}]} | null | versae/bertin-gpt-j-6B_4bit_13 | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bertin-project/bertin-gpt-j-6B",
"license:apache-2.0",
"region:us"
] | 2024-02-07T13:05:30+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/bertin-gpt-j-6B #license-apache-2.0 #region-us
|
# bertin-gpt-j-6B_4bit_13
This model is a fine-tuned version of bertin-project/bertin-gpt-j-6B 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: 1.41e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | [
"# bertin-gpt-j-6B_4bit_13\n\nThis model is a fine-tuned version of bertin-project/bertin-gpt-j-6B 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: 1.41e-05\n- train_batch_size: 16\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- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/bertin-gpt-j-6B #license-apache-2.0 #region-us \n",
"# bertin-gpt-j-6B_4bit_13\n\nThis model is a fine-tuned version of bertin-project/bertin-gpt-j-6B 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: 1.41e-05\n- train_batch_size: 16\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- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
] | [
52,
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3,
104,
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39
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"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/bertin-gpt-j-6B #license-apache-2.0 #region-us \n# bertin-gpt-j-6B_4bit_13\n\nThis model is a fine-tuned version of bertin-project/bertin-gpt-j-6B 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: 1.41e-05\n- train_batch_size: 16\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- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
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null | null | diffusers |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
| {"license": "apache-2.0", "title": "InstantID", "emoji": "\ud83d\ude3b", "colorFrom": "gray", "colorTo": "gray", "sdk": "gradio", "sdk_version": "4.15.0", "app_file": "app.py", "pinned": false, "disable_embedding": true} | null | Aitrepreneur/InstantID-Controlnet | [
"diffusers",
"onnx",
"safetensors",
"license:apache-2.0",
"region:us"
] | 2024-02-07T13:09:06+00:00 | [] | [] | TAGS
#diffusers #onnx #safetensors #license-apache-2.0 #region-us
|
Check out the configuration reference at URL
| [] | [
"TAGS\n#diffusers #onnx #safetensors #license-apache-2.0 #region-us \n"
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null | null | transformers |
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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# Model Card for Model ID
## Model Details
### Model Description
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- Developed by:
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## Uses
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## Bias, Risks, and Limitations
### Recommendations
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## 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]
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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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
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BibTeX:
APA:
## 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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7374
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 282 | 7.0722 |
| 7.4369 | 2.0 | 564 | 6.8039 |
| 7.4369 | 3.0 | 846 | 6.7374 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | text-generation | thomaslwang/gpt2-wikitext2 | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T13:10:36+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.7374
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: 3.0
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+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: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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"### 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: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
68,
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] | [
"passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #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: 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: 3.0### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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] |
null | null | null | [](https://colab.research.google.com/drive/1r4IRL0UA7JEoZ0ZK8PKfMyTIBHKpyhcw)
# Local Installation
If you already have RVC installed, then just download GUI.py and drop it in the root folder!
If you need to install RVC, I recommend you check the [original repo](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)
Or read this at least.
I recommend you use a virtual environment
```bash
python -m venv RVC
cd RVC
git clone https://github.com/777gt/-EVC-
Scripts/activate.bat
pip install torch torchvision torchaudio
pip install -r "-EVC-/requirements.txt"
```
If you're on Windows, like me, and don't have an NVIDA graphics card, install the requirements from a different .txt:
```bash
pip install -r "-EVC-/requirements-dml.txt"
```
Also, do not forget to download the necessary models. EasyGUI uses RVC 2 40k models.
```bash
wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt -O ./assets/rmvpe/rmvpe.pt
wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.onnx -O ./assets/rmvpe/rmvpe.onnx
wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -O ./assets/hubert/hubert_base.pt
wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -O ./assets/pretrained_v2/D40k.pth
wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -O ./assets/pretrained_v2/G40k.pth
wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -O ./assets/pretrained_v2/f0D40k.pth
wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -O ./assets/pretrained_v2/f0G40k.pth
```
| {} | null | anhhayghen/gaihu | [
"region:us"
] | 2024-02-07T13:15:32+00:00 | [] | [] | TAGS
#region-us
| ![Open In Colab](URL
# Local Installation
If you already have RVC installed, then just download URL and drop it in the root folder!
If you need to install RVC, I recommend you check the original repo
Or read this at least.
I recommend you use a virtual environment
If you're on Windows, like me, and don't have an NVIDA graphics card, install the requirements from a different .txt:
Also, do not forget to download the necessary models. EasyGUI uses RVC 2 40k models.
| [
"# Local Installation\nIf you already have RVC installed, then just download URL and drop it in the root folder!\nIf you need to install RVC, I recommend you check the original repo\nOr read this at least.\n\nI recommend you use a virtual environment\n\n\nIf you're on Windows, like me, and don't have an NVIDA graphics card, install the requirements from a different .txt:\n\nAlso, do not forget to download the necessary models. EasyGUI uses RVC 2 40k models."
] | [
"TAGS\n#region-us \n",
"# Local Installation\nIf you already have RVC installed, then just download URL and drop it in the root folder!\nIf you need to install RVC, I recommend you check the original repo\nOr read this at least.\n\nI recommend you use a virtual environment\n\n\nIf you're on Windows, like me, and don't have an NVIDA graphics card, install the requirements from a different .txt:\n\nAlso, do not forget to download the necessary models. EasyGUI uses RVC 2 40k models."
] | [
6,
109
] | [
"passage: TAGS\n#region-us \n# Local Installation\nIf you already have RVC installed, then just download URL and drop it in the root folder!\nIf you need to install RVC, I recommend you check the original repo\nOr read this at least.\n\nI recommend you use a virtual environment\n\n\nIf you're on Windows, like me, and don't have an NVIDA graphics card, install the requirements from a different .txt:\n\nAlso, do not forget to download the necessary models. EasyGUI uses RVC 2 40k models."
] | [
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null | null | transformers |
# Claire-Mistral-7B-0.1
**Claire-Mistral-7B-0.1 is a 7B parameter causal decoder-only model built by [LINAGORA](https://labs.linagora.com/) and [OpenLLM-France](https://github.com/OpenLLM-France)**
**adapted from [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) on French conversational data.**
Claire-Mistral-7B-0.1 is a pretrained language model designed to be attuned to the dynamics of linguistic interactions in dialogue. Without further training, its expected use is to generate continuations of dialogues. Its main purpose is to serve as a base model for fine-tuning on dialogue generation (e.g., chat) and dialogue understanding (e.g., meeting summarization) tasks. Please note that due to its training, the model is prone to generate dialogues with disfluencies and other constructions common to spoken language.
A qualitatively better variant of this model is available under [Claire-7B-0.1](https://huggingface.co/OpenLLM-France/Claire-7B-0.1).
* [Typical usage](#typical-usage)
* [Typical prompts](#typical-prompts)
* [Training Details](#training-details)
* [Training Data](#training-data)
* [Training Procedure](#training-procedure)
* [Evaluation](#evaluation)
* [License](#license)
* [Acknowledgements](#acknowledgements)
* [Contact](#contact)
## Typical usage
```python
import transformers
import torch
model_name = "OpenLLM-France/Claire-Mistral-7B-0.1"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.AutoModelForCausalLM.from_pretrained(model_name,
device_map="auto",
torch_dtype=torch.bfloat16,
load_in_4bit=True # For efficient inference, if supported by the GPU card
)
pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer)
generation_kwargs = dict(
num_return_sequences=1, # Number of variants to generate.
return_full_text= False, # Do not include the prompt in the generated text.
max_new_tokens=200, # Maximum length for the output text.
do_sample=True, top_k=10, temperature=1.0, # Sampling parameters.
pad_token_id=tokenizer.eos_token_id, # Just to avoid a harmless warning.
)
prompt = """\
- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
- Bonjour Camille,\
"""
completions = pipeline(prompt, **generation_kwargs)
for completion in completions:
print(prompt + " […]" + completion['generated_text'])
```
This will print something like:
```
- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
- Bonjour Camille, […] je vous prépare un plat de saison, une daube provençale.
- Ah je ne connais pas cette recette.
- C'est très facile à préparer, vous n'avez qu'à mettre de l'eau dans une marmite, y mettre de l'oignon émincé, des carottes coupées en petits morceaux, et vous allez mettre votre viande de bœuf coupé en petits morceaux également.
- Je n'ai jamais cuisiné de viande de bœuf, mais c'est vrai que ça a l'air bien facile.
- Vous n'avez plus qu'à laisser mijoter, et ensuite il sera temps de servir les clients.
- Très bien.
```
You will need at least 6GB of VRAM to run inference using 4bit quantization (16GB of VRAM without 4bit quantization).
If you have trouble running this code, make sure you have recent versions of `torch`, `transformers` and `accelerate` (see [requirements.txt](requirements.txt)).
### Typical prompts
Claire-Mistral-7B-0.1 was trained on diarized French conversations. During training, the dialogues were normalized in several formats. The possible formats for expected prompts are as follows:
A monologue can be specified as a single line prompt (though keep in mind that the model might still return a dialogue because of its training):
```python
prompt = "Mesdames et messieurs les députés, chers collègues, bonsoir. Vous l'aurez peut-être remarqué, je cite rarement"
```
A dialogue between two speakers can be specified with one line per speech turn starting with a dash:
```python
prompt = """\
- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
- Bonjour Camille,\
"""
```
A dialogue or multilogue (with two or more speakers) can be specified with lines that start with `[Intervenant X:]` where `X` is a number:
```python
prompt = """\
[Intervenant 1:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
[Intervenant 2:] Bonjour Camille,\
"""
```
A dialogue or multilogue with named speakers can be specified with lines that start with `[SpeakerName:]`
where `SpeakerName` can be a first name, a first and a last name, a nickname, a title…
```python
prompt = """\
[Mme Camille Durand:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
[Mr. Dominique Petit:] Bonjour Camille,\
"""
```
## Training Details
### Training Data
The training dataset is available at [OpenLLM-France/Claire-Dialogue-French-0.1](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-French-0.1)
and described in ["The Claire French Dialogue Dataset" (2023)](https://arxiv.org/abs/2311.16840).
Claire-Mistral-7B-0.1 was tuned from Mistral-7B-v0.1 on the following data distribution:
| **Data type** | **Words** | **Training Sampling Weight** | **Sources** |
|-------------------------------|------------|------------------------------|-----------------------------------------------------|
| Parliamentary Proceedings | 135M | 35% | Assemblée Nationale |
| Theatre | 16M | 18% | Théâtre Classique, Théâtre Gratuit |
| Interviews | 6.4M | 29% | TCOF, CFPP, CFPB (ORFEO), ACSYNT, PFC, Valibel (ORFEO), ESLO|
| Free Conversations | 2.2M | 10% | CRFP (ORFEO), OFROM (ORFEO), CID, Rhapsodie, ParisStories, PFC, CLAPI, C-ORAL-ROM (ORFEO), LinTO, ESLO |
| Meetings | 1.2M | 5% | SUMM-RE, LinTO, Réunions de travail (ORFEO) |
| Debates | 402k | <2% | FREDSum, ESLO |
| Assistance | 159k | <1% | Fleuron (ORFEO), Accueil UBS, OTG, ESLO |
| Presentation, Formal Address | 86k | <0.5% | Valibel (ORFEO), LinTO, ESLO |
Training data was augmented with the following techniques:
* varying the format used to indicate speech turns (dashes or [XXX:])
* substituting [Intervenant X:] for [SpeakerName:] or vice versa, where [SpeakerName:] might be a real name or a randomly generated name
* removing punctuation marks and/or casing (to prepare the model for transcripts produced by some Automatic Speech Recognition systems)
Long conversations were truncated at a maximum of 4096 tokens. Where possible, they were split between speaker turns.
While the model has been trained and evaluated only on French dialogues, it may be able to generate conversations in other languages from the original Mistral-7B-v0.1 training data.
### Training Procedure
The training code is available at [https://github.com/OpenLLM-France/Lit-Claire](https://github.com/OpenLLM-France/Lit-Claire).
Claire-Mistral-7B-0.1 is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
See [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) for more details.
Claire-Mistral-7B-0.1 was trained on 8 A100 80GB GPUs for about 50 GPU hours.
Hyperparameters were the following:
| **Hyperparameter** | **Value** |
|--------------------|------------|
| Precision | `bfloat16` |
| Optimizer | AdamW |
| Learning rate | 1e-4 |
| Weight decay | 1e-2 |
| Batch size | 128 |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| Dropout | 0.05 |
| gradient clipping | 1 |
## Evaluation
See the [Evaluation section of Claire-7B-0.1](https://huggingface.co/OpenLLM-France/Claire-7B-0.1#evaluation).
## License
Given that some of the corpora used for training are only available under CC-BY-NC-SA licenses,
Claire-Mistral-7B-0.1 is made available under the [CC-BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
## Acknowledgements
This work was performed using HPC resources from GENCI–IDRIS (Grant 2023-AD011014561).
Claire-Mistral-7B-0.1 was created by members of [LINAGORA](https://labs.linagora.com/) (in alphabetical order): Ismaïl Harrando, Julie Hunter, Jean-Pierre Lorré, Jérôme Louradour, Michel-Marie Maudet, Virgile Rennard, Guokan Shang.
Special thanks to partners from the OpenLLM-France community, especially Christophe Cerisara (LORIA), Pierre-Carl Langlais and Anastasia Stasenko (OpSci), and Pierre Colombo, for valuable advice.
## Contact
[email protected] | {"language": ["fr"], "license": "cc-by-nc-sa-4.0", "tags": ["pretrained", "conversational"], "pipeline_tag": "text-generation", "base_model": "mistralai/Mistral-7B-v0.1", "widget": [{"text": "- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?\n- Bonjour Camille,", "example_title": "Request for a recipe", "group": "Dash"}, {"text": "[Intervenant 1:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?\n[Intervenant 2:] Bonjour Camille,", "example_title": "Request for a recipe", "group": "Intervenant"}, {"text": "[Camille:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?\n[Dominique:] Bonjour Camille,", "example_title": "Request for a recipe", "group": "FirstName"}, {"text": "[Camille Durand:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?\n[Dominique Petit:] Bonjour Camille,", "example_title": "Request for a recipe", "group": "Named"}], "inference": {"parameters": {"temperature": 1.0, "max_new_tokens": 200, "top_k": 10}}} | text-generation | ExAi/Claire-Mistral-7B-v0.1.3-exl2-4.0 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pretrained",
"conversational",
"fr",
"arxiv:2311.16840",
"base_model:mistralai/Mistral-7B-v0.1",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T13:20:35+00:00 | [
"2311.16840"
] | [
"fr"
] | TAGS
#transformers #safetensors #mistral #text-generation #pretrained #conversational #fr #arxiv-2311.16840 #base_model-mistralai/Mistral-7B-v0.1 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Claire-Mistral-7B-0.1
=====================
Claire-Mistral-7B-0.1 is a 7B parameter causal decoder-only model built by LINAGORA and OpenLLM-France
adapted from Mistral-7B on French conversational data.
Claire-Mistral-7B-0.1 is a pretrained language model designed to be attuned to the dynamics of linguistic interactions in dialogue. Without further training, its expected use is to generate continuations of dialogues. Its main purpose is to serve as a base model for fine-tuning on dialogue generation (e.g., chat) and dialogue understanding (e.g., meeting summarization) tasks. Please note that due to its training, the model is prone to generate dialogues with disfluencies and other constructions common to spoken language.
A qualitatively better variant of this model is available under Claire-7B-0.1.
* Typical usage
+ Typical prompts
* Training Details
+ Training Data
+ Training Procedure
* Evaluation
* License
* Acknowledgements
* Contact
Typical usage
-------------
This will print something like:
You will need at least 6GB of VRAM to run inference using 4bit quantization (16GB of VRAM without 4bit quantization).
If you have trouble running this code, make sure you have recent versions of 'torch', 'transformers' and 'accelerate' (see URL).
### Typical prompts
Claire-Mistral-7B-0.1 was trained on diarized French conversations. During training, the dialogues were normalized in several formats. The possible formats for expected prompts are as follows:
A monologue can be specified as a single line prompt (though keep in mind that the model might still return a dialogue because of its training):
A dialogue between two speakers can be specified with one line per speech turn starting with a dash:
A dialogue or multilogue (with two or more speakers) can be specified with lines that start with '[Intervenant X:]' where 'X' is a number:
A dialogue or multilogue with named speakers can be specified with lines that start with '[SpeakerName:]'
where 'SpeakerName' can be a first name, a first and a last name, a nickname, a title…
Training Details
----------------
### Training Data
The training dataset is available at OpenLLM-France/Claire-Dialogue-French-0.1
and described in "The Claire French Dialogue Dataset" (2023).
Claire-Mistral-7B-0.1 was tuned from Mistral-7B-v0.1 on the following data distribution:
Training data was augmented with the following techniques:
* varying the format used to indicate speech turns (dashes or [XXX:])
* substituting [Intervenant X:] for [SpeakerName:] or vice versa, where [SpeakerName:] might be a real name or a randomly generated name
* removing punctuation marks and/or casing (to prepare the model for transcripts produced by some Automatic Speech Recognition systems)
Long conversations were truncated at a maximum of 4096 tokens. Where possible, they were split between speaker turns.
While the model has been trained and evaluated only on French dialogues, it may be able to generate conversations in other languages from the original Mistral-7B-v0.1 training data.
### Training Procedure
The training code is available at URL
Claire-Mistral-7B-0.1 is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
See Mistral-7B for more details.
Claire-Mistral-7B-0.1 was trained on 8 A100 80GB GPUs for about 50 GPU hours.
Hyperparameters were the following:
Evaluation
----------
See the Evaluation section of Claire-7B-0.1.
License
-------
Given that some of the corpora used for training are only available under CC-BY-NC-SA licenses,
Claire-Mistral-7B-0.1 is made available under the CC-BY-NC-SA 4.0 license.
Acknowledgements
----------------
This work was performed using HPC resources from GENCI–IDRIS (Grant 2023-AD011014561).
Claire-Mistral-7B-0.1 was created by members of LINAGORA (in alphabetical order): Ismaïl Harrando, Julie Hunter, Jean-Pierre Lorré, Jérôme Louradour, Michel-Marie Maudet, Virgile Rennard, Guokan Shang.
Special thanks to partners from the OpenLLM-France community, especially Christophe Cerisara (LORIA), Pierre-Carl Langlais and Anastasia Stasenko (OpSci), and Pierre Colombo, for valuable advice.
Contact
-------
contact@URL
| [
"### Typical prompts\n\n\nClaire-Mistral-7B-0.1 was trained on diarized French conversations. During training, the dialogues were normalized in several formats. The possible formats for expected prompts are as follows:\n\n\nA monologue can be specified as a single line prompt (though keep in mind that the model might still return a dialogue because of its training):\n\n\nA dialogue between two speakers can be specified with one line per speech turn starting with a dash:\n\n\nA dialogue or multilogue (with two or more speakers) can be specified with lines that start with '[Intervenant X:]' where 'X' is a number:\n\n\nA dialogue or multilogue with named speakers can be specified with lines that start with '[SpeakerName:]'\nwhere 'SpeakerName' can be a first name, a first and a last name, a nickname, a title…\n\n\nTraining Details\n----------------",
"### Training Data\n\n\nThe training dataset is available at OpenLLM-France/Claire-Dialogue-French-0.1\nand described in \"The Claire French Dialogue Dataset\" (2023).\n\n\nClaire-Mistral-7B-0.1 was tuned from Mistral-7B-v0.1 on the following data distribution:\n\n\n\nTraining data was augmented with the following techniques:\n\n\n* varying the format used to indicate speech turns (dashes or [XXX:])\n* substituting [Intervenant X:] for [SpeakerName:] or vice versa, where [SpeakerName:] might be a real name or a randomly generated name\n* removing punctuation marks and/or casing (to prepare the model for transcripts produced by some Automatic Speech Recognition systems)\n\n\nLong conversations were truncated at a maximum of 4096 tokens. Where possible, they were split between speaker turns.\n\n\nWhile the model has been trained and evaluated only on French dialogues, it may be able to generate conversations in other languages from the original Mistral-7B-v0.1 training data.",
"### Training Procedure\n\n\nThe training code is available at URL\n\n\nClaire-Mistral-7B-0.1 is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).\nSee Mistral-7B for more details.\n\n\nClaire-Mistral-7B-0.1 was trained on 8 A100 80GB GPUs for about 50 GPU hours.\n\n\nHyperparameters were the following:\n\n\n\nEvaluation\n----------\n\n\nSee the Evaluation section of Claire-7B-0.1.\n\n\nLicense\n-------\n\n\nGiven that some of the corpora used for training are only available under CC-BY-NC-SA licenses,\nClaire-Mistral-7B-0.1 is made available under the CC-BY-NC-SA 4.0 license.\n\n\nAcknowledgements\n----------------\n\n\nThis work was performed using HPC resources from GENCI–IDRIS (Grant 2023-AD011014561).\n\n\nClaire-Mistral-7B-0.1 was created by members of LINAGORA (in alphabetical order): Ismaïl Harrando, Julie Hunter, Jean-Pierre Lorré, Jérôme Louradour, Michel-Marie Maudet, Virgile Rennard, Guokan Shang.\n\n\nSpecial thanks to partners from the OpenLLM-France community, especially Christophe Cerisara (LORIA), Pierre-Carl Langlais and Anastasia Stasenko (OpSci), and Pierre Colombo, for valuable advice.\n\n\nContact\n-------\n\n\ncontact@URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #pretrained #conversational #fr #arxiv-2311.16840 #base_model-mistralai/Mistral-7B-v0.1 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Typical prompts\n\n\nClaire-Mistral-7B-0.1 was trained on diarized French conversations. During training, the dialogues were normalized in several formats. The possible formats for expected prompts are as follows:\n\n\nA monologue can be specified as a single line prompt (though keep in mind that the model might still return a dialogue because of its training):\n\n\nA dialogue between two speakers can be specified with one line per speech turn starting with a dash:\n\n\nA dialogue or multilogue (with two or more speakers) can be specified with lines that start with '[Intervenant X:]' where 'X' is a number:\n\n\nA dialogue or multilogue with named speakers can be specified with lines that start with '[SpeakerName:]'\nwhere 'SpeakerName' can be a first name, a first and a last name, a nickname, a title…\n\n\nTraining Details\n----------------",
"### Training Data\n\n\nThe training dataset is available at OpenLLM-France/Claire-Dialogue-French-0.1\nand described in \"The Claire French Dialogue Dataset\" (2023).\n\n\nClaire-Mistral-7B-0.1 was tuned from Mistral-7B-v0.1 on the following data distribution:\n\n\n\nTraining data was augmented with the following techniques:\n\n\n* varying the format used to indicate speech turns (dashes or [XXX:])\n* substituting [Intervenant X:] for [SpeakerName:] or vice versa, where [SpeakerName:] might be a real name or a randomly generated name\n* removing punctuation marks and/or casing (to prepare the model for transcripts produced by some Automatic Speech Recognition systems)\n\n\nLong conversations were truncated at a maximum of 4096 tokens. Where possible, they were split between speaker turns.\n\n\nWhile the model has been trained and evaluated only on French dialogues, it may be able to generate conversations in other languages from the original Mistral-7B-v0.1 training data.",
"### Training Procedure\n\n\nThe training code is available at URL\n\n\nClaire-Mistral-7B-0.1 is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).\nSee Mistral-7B for more details.\n\n\nClaire-Mistral-7B-0.1 was trained on 8 A100 80GB GPUs for about 50 GPU hours.\n\n\nHyperparameters were the following:\n\n\n\nEvaluation\n----------\n\n\nSee the Evaluation section of Claire-7B-0.1.\n\n\nLicense\n-------\n\n\nGiven that some of the corpora used for training are only available under CC-BY-NC-SA licenses,\nClaire-Mistral-7B-0.1 is made available under the CC-BY-NC-SA 4.0 license.\n\n\nAcknowledgements\n----------------\n\n\nThis work was performed using HPC resources from GENCI–IDRIS (Grant 2023-AD011014561).\n\n\nClaire-Mistral-7B-0.1 was created by members of LINAGORA (in alphabetical order): Ismaïl Harrando, Julie Hunter, Jean-Pierre Lorré, Jérôme Louradour, Michel-Marie Maudet, Virgile Rennard, Guokan Shang.\n\n\nSpecial thanks to partners from the OpenLLM-France community, especially Christophe Cerisara (LORIA), Pierre-Carl Langlais and Anastasia Stasenko (OpSci), and Pierre Colombo, for valuable advice.\n\n\nContact\n-------\n\n\ncontact@URL"
] | [
95,
200,
241,
315
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #pretrained #conversational #fr #arxiv-2311.16840 #base_model-mistralai/Mistral-7B-v0.1 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Typical prompts\n\n\nClaire-Mistral-7B-0.1 was trained on diarized French conversations. During training, the dialogues were normalized in several formats. The possible formats for expected prompts are as follows:\n\n\nA monologue can be specified as a single line prompt (though keep in mind that the model might still return a dialogue because of its training):\n\n\nA dialogue between two speakers can be specified with one line per speech turn starting with a dash:\n\n\nA dialogue or multilogue (with two or more speakers) can be specified with lines that start with '[Intervenant X:]' where 'X' is a number:\n\n\nA dialogue or multilogue with named speakers can be specified with lines that start with '[SpeakerName:]'\nwhere 'SpeakerName' can be a first name, a first and a last name, a nickname, a title…\n\n\nTraining Details\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": "meta-llama/Llama-2-7b-hf"} | null | noza-kit/Adapter_llama2_translate_Q_enpt_ex2-1epoch | [
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"safetensors",
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#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
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### 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]
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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. -->
# badokorach/xlm-roberta-base-finetuned-mlqa
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5409
- Validation Loss: 0.0
- Epoch: 4
## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 9540, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.0174 | 0.0 | 0 |
| 1.0319 | 0.0 | 1 |
| 0.8021 | 0.0 | 2 |
| 0.6385 | 0.0 | 3 |
| 0.5409 | 0.0 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "xlm-roberta-base", "model-index": [{"name": "badokorach/xlm-roberta-base-finetuned-mlqa", "results": []}]} | question-answering | badokorach/xlm-roberta-base-finetuned-mlqa | [
"transformers",
"tf",
"xlm-roberta",
"question-answering",
"generated_from_keras_callback",
"base_model:xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-07T13:20:52+00:00 | [] | [] | TAGS
#transformers #tf #xlm-roberta #question-answering #generated_from_keras_callback #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us
| badokorach/xlm-roberta-base-finetuned-mlqa
==========================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5409
* Validation Loss: 0.0
* Epoch: 4
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': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 9540, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.02}
* 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': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 1e-05, 'decay\\_steps': 9540, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.02}\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 #xlm-roberta #question-answering #generated_from_keras_callback #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 1e-05, 'decay\\_steps': 9540, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.02}\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"
] | [
60,
231,
4,
31
] | [
"passage: TAGS\n#transformers #tf #xlm-roberta #question-answering #generated_from_keras_callback #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 1e-05, 'decay\\_steps': 9540, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.02}\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"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | omarfarooq908/llama2-qlora-finetunined-french | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Evaluation
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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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null | null | transformers |
# Model Card for Model ID
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# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
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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.
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Use the code below to get started with the model.
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### Training Data
### Training Procedure
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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null | null | transformers |
# Pygmalion 1.3B
## Model description
Pymalion 1.3B is a proof-of-concept dialogue model based on EleutherAI's [pythia-1.3b-deduped](https://huggingface.co/EleutherAI/pythia-1.3b-deduped).
**Warning:** This model is **NOT** suitable for use by minors. It **will** output X-rated content under certain circumstances.
## Training data
The fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real _and_ partially machine-generated conversations.
## Training procedure
Fine-tuning was done using [ColossalAI](https://github.com/hpcaitech/ColossalAI) (specifically, with a slightly modified version of their [OPT fine-tune example](https://github.com/hpcaitech/ColossalAI/blob/78509124d32b63b7fc36f6508e0576a326d51422/examples/language/opt/run_clm.py)) for around 11.4 million tokens over 5440 steps on a single 24GB GPU. The run took just under 21 hours.
## Intended use
### The easy way
We provide a notebook with a Gradio UI for playing around with the model without having to manually format inputs. This notebook can be found [here](https://github.com/PygmalionAI/gradio-ui/blob/master/notebooks/GPU.ipynb).
### The manual way
The model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format:
```
[CHARACTER]'s Persona: [A few sentences about the character you want the model to play]
[DIALOGUE HISTORY]
You: [Your input message here]
[CHARACTER]:
```
Where `[CHARACTER] `is, as you can probably guess, the name of the character you want the model to portray, and `[DIALOGUE HISTORY]` is chat history so the model can have some conversational context to draw from. Ideally it'll be pairs of messages like:
```
[CHARACTER]: [some dialogue here]
You: [your response to the dialogue above]
```
Apart from chat history, you can also just add example conversations in `[DIALOGUE HISTORY]` to show how the character should speak - ideally at the beginning, so it doesn't get confused as to what's conversation history vs. character definition.
## Known issues
- The model can get stuck repeating certain phrases, or sometimes even entire sentences.
- We believe this is due to that behavior being present in the training data itself, and plan to investigate and adjust accordingly for future versions. | {"language": ["en"], "license": "agpl-3.0", "tags": ["text generation", "conversational"], "inference": true, "pipeline_tag": "conversational"} | text-generation | shapermindai/pygmalion-free | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"text generation",
"conversational",
"en",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2024-02-07T13:28:02+00:00 | [] | [
"en"
] | TAGS
#transformers #tensorboard #safetensors #gpt_neox #text-generation #text generation #conversational #en #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Pygmalion 1.3B
## Model description
Pymalion 1.3B is a proof-of-concept dialogue model based on EleutherAI's pythia-1.3b-deduped.
Warning: This model is NOT suitable for use by minors. It will output X-rated content under certain circumstances.
## Training data
The fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real _and_ partially machine-generated conversations.
## Training procedure
Fine-tuning was done using ColossalAI (specifically, with a slightly modified version of their OPT fine-tune example) for around 11.4 million tokens over 5440 steps on a single 24GB GPU. The run took just under 21 hours.
## Intended use
### The easy way
We provide a notebook with a Gradio UI for playing around with the model without having to manually format inputs. This notebook can be found here.
### The manual way
The model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format:
Where '[CHARACTER] 'is, as you can probably guess, the name of the character you want the model to portray, and '[DIALOGUE HISTORY]' is chat history so the model can have some conversational context to draw from. Ideally it'll be pairs of messages like:
Apart from chat history, you can also just add example conversations in '[DIALOGUE HISTORY]' to show how the character should speak - ideally at the beginning, so it doesn't get confused as to what's conversation history vs. character definition.
## Known issues
- The model can get stuck repeating certain phrases, or sometimes even entire sentences.
- We believe this is due to that behavior being present in the training data itself, and plan to investigate and adjust accordingly for future versions. | [
"# Pygmalion 1.3B",
"## Model description\n\nPymalion 1.3B is a proof-of-concept dialogue model based on EleutherAI's pythia-1.3b-deduped.\n\nWarning: This model is NOT suitable for use by minors. It will output X-rated content under certain circumstances.",
"## Training data\n\nThe fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real _and_ partially machine-generated conversations.",
"## Training procedure\n\nFine-tuning was done using ColossalAI (specifically, with a slightly modified version of their OPT fine-tune example) for around 11.4 million tokens over 5440 steps on a single 24GB GPU. The run took just under 21 hours.",
"## Intended use",
"### The easy way\n\nWe provide a notebook with a Gradio UI for playing around with the model without having to manually format inputs. This notebook can be found here.",
"### The manual way\n\nThe model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format:\n\n\n\nWhere '[CHARACTER] 'is, as you can probably guess, the name of the character you want the model to portray, and '[DIALOGUE HISTORY]' is chat history so the model can have some conversational context to draw from. Ideally it'll be pairs of messages like:\n\n\n\nApart from chat history, you can also just add example conversations in '[DIALOGUE HISTORY]' to show how the character should speak - ideally at the beginning, so it doesn't get confused as to what's conversation history vs. character definition.",
"## Known issues\n\n- The model can get stuck repeating certain phrases, or sometimes even entire sentences.\n - We believe this is due to that behavior being present in the training data itself, and plan to investigate and adjust accordingly for future versions."
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-generation #text generation #conversational #en #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Pygmalion 1.3B",
"## Model description\n\nPymalion 1.3B is a proof-of-concept dialogue model based on EleutherAI's pythia-1.3b-deduped.\n\nWarning: This model is NOT suitable for use by minors. It will output X-rated content under certain circumstances.",
"## Training data\n\nThe fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real _and_ partially machine-generated conversations.",
"## Training procedure\n\nFine-tuning was done using ColossalAI (specifically, with a slightly modified version of their OPT fine-tune example) for around 11.4 million tokens over 5440 steps on a single 24GB GPU. The run took just under 21 hours.",
"## Intended use",
"### The easy way\n\nWe provide a notebook with a Gradio UI for playing around with the model without having to manually format inputs. This notebook can be found here.",
"### The manual way\n\nThe model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format:\n\n\n\nWhere '[CHARACTER] 'is, as you can probably guess, the name of the character you want the model to portray, and '[DIALOGUE HISTORY]' is chat history so the model can have some conversational context to draw from. Ideally it'll be pairs of messages like:\n\n\n\nApart from chat history, you can also just add example conversations in '[DIALOGUE HISTORY]' to show how the character should speak - ideally at the beginning, so it doesn't get confused as to what's conversation history vs. character definition.",
"## Known issues\n\n- The model can get stuck repeating certain phrases, or sometimes even entire sentences.\n - We believe this is due to that behavior being present in the training data itself, and plan to investigate and adjust accordingly for future versions."
] | [
75,
7,
64,
41,
59,
5,
36,
159,
54
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-generation #text generation #conversational #en #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Pygmalion 1.3B## Model description\n\nPymalion 1.3B is a proof-of-concept dialogue model based on EleutherAI's pythia-1.3b-deduped.\n\nWarning: This model is NOT suitable for use by minors. It will output X-rated content under certain circumstances.## Training data\n\nThe fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real _and_ partially machine-generated conversations.## Training procedure\n\nFine-tuning was done using ColossalAI (specifically, with a slightly modified version of their OPT fine-tune example) for around 11.4 million tokens over 5440 steps on a single 24GB GPU. The run took just under 21 hours.## Intended use### The easy way\n\nWe provide a notebook with a Gradio UI for playing around with the model without having to manually format inputs. This notebook can be found here.### The manual way\n\nThe model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format:\n\n\n\nWhere '[CHARACTER] 'is, as you can probably guess, the name of the character you want the model to portray, and '[DIALOGUE HISTORY]' is chat history so the model can have some conversational context to draw from. Ideally it'll be pairs of messages like:\n\n\n\nApart from chat history, you can also just add example conversations in '[DIALOGUE HISTORY]' to show how the character should speak - ideally at the beginning, so it doesn't get confused as to what's conversation history vs. character definition.## Known issues\n\n- The model can get stuck repeating certain phrases, or sometimes even entire sentences.\n - We believe this is due to that behavior being present in the training data itself, and plan to investigate and adjust accordingly for future versions."
] | [
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] |
null | null | transformers |
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
I have provided a .gguf Q4_K_M for cpu inference as well.
This repository serves as a learning experience for myself to experiment with merged models & gguf conversions.
Credits:
* mergekit
* llama.cpp
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [macadeliccc/WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
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
``` | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["OpenPipe/mistral-ft-optimized-1218", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo"]} | text-generation | BryanSwk/LaserPipe-7B-SLERP | [
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T13:30:26+00:00 | [] | [] | TAGS
#transformers #safetensors #gguf #mistral #text-generation #mergekit #merge #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a merge of pre-trained language models created using mergekit.
I have provided a .gguf Q4_K_M for cpu inference as well.
This repository serves as a learning experience for myself to experiment with merged models & gguf conversions.
Credits:
* mergekit
* URL
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* OpenPipe/mistral-ft-optimized-1218
* macadeliccc/WestLake-7B-v2-laser-truthy-dpo
### Configuration
The following YAML configuration was used to produce this model:
| [
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* OpenPipe/mistral-ft-optimized-1218\n* macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #gguf #mistral #text-generation #mergekit #merge #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* OpenPipe/mistral-ft-optimized-1218\n* macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
112,
4,
18,
51,
17
] | [
"passage: TAGS\n#transformers #safetensors #gguf #mistral #text-generation #mergekit #merge #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* OpenPipe/mistral-ft-optimized-1218\n* macadeliccc/WestLake-7B-v2-laser-truthy-dpo### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
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null | null | null |
# MoMask: Generative Masked Modeling of 3D Human Motions
## [[Project Page]](https://ericguo5513.github.io/momask) [[Paper]](https://arxiv.org/abs/2312.00063)
If you find our code or paper helpful, please consider citing:
```
@article{guo2023momask,
title={MoMask: Generative Masked Modeling of 3D Human Motions},
author={Chuan Guo and Yuxuan Mu and Muhammad Gohar Javed and Sen Wang and Li Cheng},
year={2023},
eprint={2312.00063},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## :postbox: News
📢 **2023-12-19** --- Release scripts for temporal inpainting.
📢 **2023-12-15** --- Release codes and models for momask. Including training/eval/generation scripts.
📢 **2023-11-29** --- Initialized the webpage and git project.
## :round_pushpin: Get You Ready
<details>
### 1. Conda Environment
```
conda env create -f environment.yml
conda activate momask
pip install git+https://github.com/openai/CLIP.git
```
We test our code on Python 3.7.13 and PyTorch 1.7.1
### 2. Models and Dependencies
#### Download Pre-trained Models
```
bash prepare/download_models.sh
```
#### Download Evaluation Models and Gloves
For evaluation only.
```
bash prepare/download_evaluator.sh
bash prepare/download_glove.sh
```
#### Troubleshooting
To address the download error related to gdown: "Cannot retrieve the public link of the file. You may need to change the permission to 'Anyone with the link', or have had many accesses". A potential solution is to run `pip install --upgrade --no-cache-dir gdown`, as suggested on https://github.com/wkentaro/gdown/issues/43. This should help resolve the issue.
#### (Optional) Download Mannually
Visit [[Google Drive]](https://drive.google.com/drive/folders/1b3GnAbERH8jAoO5mdWgZhyxHB73n23sK?usp=drive_link) to download the models and evaluators mannually.
### 3. Get Data
You have two options here:
* **Skip getting data**, if you just want to generate motions using *own* descriptions.
* **Get full data**, if you want to *re-train* and *evaluate* the model.
**(a). Full data (text + motion)**
**HumanML3D** - Follow the instruction in [HumanML3D](https://github.com/EricGuo5513/HumanML3D.git), then copy the result dataset to our repository:
```
cp -r ../HumanML3D/HumanML3D ./dataset/HumanML3D
```
**KIT**-Download from [HumanML3D](https://github.com/EricGuo5513/HumanML3D.git), then place result in `./dataset/KIT-ML`
####
</details>
## :rocket: Demo
<details>
### (a) Generate from a single prompt
```
python gen_t2m.py --gpu_id 1 --ext exp1 --text_prompt "A person is running on a treadmill."
```
### (b) Generate from a prompt file
An example of prompt file is given in `./assets/text_prompt.txt`. Please follow the format of `<text description>#<motion length>` at each line. Motion length indicates the number of poses, which must be integeter and will be rounded by 4. In our work, motion is in 20 fps.
If you write `<text description>#NA`, our model will determine a length. Note once there is **one** NA, all the others will be **NA** automatically.
```
python gen_t2m.py --gpu_id 1 --ext exp2 --text_path ./assets/text_prompt.txt
```
A few more parameters you may be interested:
* `--repeat_times`: number of replications for generation, default `1`.
* `--motion_length`: specify the number of poses for generation, only applicable in (a).
The output files are stored under folder `./generation/<ext>/`. They are
* `numpy files`: generated motions with shape of (nframe, 22, 3), under subfolder `./joints`.
* `video files`: stick figure animation in mp4 format, under subfolder `./animation`.
* `bvh files`: bvh files of the generated motion, under subfolder `./animation`.
We also apply naive foot ik to the generated motions, see files with suffix `_ik`. It sometimes works well, but sometimes will fail.
</details>
## :dancers: Visualization
<details>
All the animations are manually rendered in blender. We use the characters from [mixamo](https://www.mixamo.com/#/). You need to download the characters in T-Pose with skeleton.
### Retargeting
For retargeting, we found rokoko usually leads to large error on foot. On the other hand, [keemap.rig.transfer](https://github.com/nkeeline/Keemap-Blender-Rig-ReTargeting-Addon/releases) shows more precise retargetting. You could watch the [tutorial](https://www.youtube.com/watch?v=EG-VCMkVpxg) here.
Following these steps:
* Download keemap.rig.transfer from the github, and install it in blender.
* Import both the motion files (.bvh) and character files (.fbx) in blender.
* `Shift + Select` the both source and target skeleton. (Do not need to be Rest Position)
* Switch to `Pose Mode`, then unfold the `KeeMapRig` tool at the top-right corner of the view window.
* Load and read the bone mapping file `./assets/mapping.json`(or `mapping6.json` if it doesn't work). This file is manually made by us. It works for most characters in mixamo. You could make your own.
* Adjust the `Number of Samples`, `Source Rig`, `Destination Rig Name`.
* Clik `Transfer Animation from Source Destination`, wait a few seconds.
We didn't tried other retargetting tools. Welcome to comment if you find others are more useful.
### Scene
We use this [scene](https://drive.google.com/file/d/1lg62nugD7RTAIz0Q_YP2iZsxpUzzOkT1/view?usp=sharing) for animation.
</details>
## :clapper: Temporal Inpainting
<details>
We conduct mask-based editing in the m-transformer stage, followed by the regeneration of residual tokens for the entire sequence. To load your own motion, provide the path through `--source_motion`. Utilize `-msec` to specify the mask section, supporting either ratio or frame index. For instance, `-msec 0.3,0.6` with `max_motion_length=196` is equivalent to `-msec 59,118`, indicating the editing of the frame section [59, 118].
```
python edit_t2m.py --gpu_id 1 --ext exp3 --use_res_model -msec 0.4,0.7 --text_prompt "A man picks something from the ground using his right hand."
```
Note: Presently, the source motion must adhere to the format of a HumanML3D dim-263 feature vector. An example motion vector data from the HumanML3D test set is available in `example_data/000612.npy`. To process your own motion data, you can utilize the `process_file` function from `utils/motion_process.py`.
</details>
## :space_invader: Train Your Own Models
<details>
**Note**: You have to train RVQ **BEFORE** training masked/residual transformers. The latter two can be trained simultaneously.
### Train RVQ
```
python train_vq.py --name rvq_name --gpu_id 1 --dataset_name t2m --batch_size 512 --num_quantizers 6 --max_epoch 500 --quantize_drop_prob 0.2
```
### Train Masked Transformer
```
python train_t2m_transformer.py --name mtrans_name --gpu_id 2 --dataset_name t2m --batch_size 64 --vq_name rvq_name
```
### Train Residual Transformer
```
python train_res_transformer.py --name rtrans_name --gpu_id 2 --dataset_name t2m --batch_size 64 --vq_name rvq_name --cond_drop_prob 0.2 --share_weight
```
* `--dataset_name`: motion dataset, `t2m` for HumanML3D and `kit` for KIT-ML.
* `--name`: name your model. This will create to model space as `./checkpoints/<dataset_name>/<name>`
* `--gpu_id`: GPU id.
* `--batch_size`: we use `512` for rvq training. For masked/residual transformer, we use `64` on HumanML3D and `16` for KIT-ML.
* `--num_quantizers`: number of quantization layers, `6` is used in our case.
* `--quantize_drop_prob`: quantization dropout ratio, `0.2` is used.
* `--vq_name`: when training masked/residual transformer, you need to specify the name of rvq model for tokenization.
* `--cond_drop_prob`: condition drop ratio, for classifier-free guidance. `0.2` is used.
* `--share_weight`: whether to share the projection/embedding weights in residual transformer.
All the pre-trained models and intermediate results will be saved in space `./checkpoints/<dataset_name>/<name>`.
</details>
## :book: Evaluation
<details>
### Evaluate RVQ Reconstruction:
HumanML3D:
```
python eval_t2m_vq.py --gpu_id 0 --name rvq_nq6_dc512_nc512_noshare_qdp0.2 --dataset_name t2m --ext rvq_nq6
```
KIT-ML:
```
python eval_t2m_vq.py --gpu_id 0 --name rvq_nq6_dc512_nc512_noshare_qdp0.2_k --dataset_name kit --ext rvq_nq6
```
### Evaluate Text2motion Generation:
HumanML3D:
```
python eval_t2m_trans_res.py --res_name tres_nlayer8_ld384_ff1024_rvq6ns_cdp0.2_sw --dataset_name t2m --name t2m_nlayer8_nhead6_ld384_ff1024_cdp0.1_rvq6ns --gpu_id 1 --cond_scale 4 --time_steps 10 --ext evaluation
```
KIT-ML:
```
python eval_t2m_trans_res.py --res_name tres_nlayer8_ld384_ff1024_rvq6ns_cdp0.2_sw_k --dataset_name kit --name t2m_nlayer8_nhead6_ld384_ff1024_cdp0.1_rvq6ns_k --gpu_id 0 --cond_scale 2 --time_steps 10 --ext evaluation
```
* `--res_name`: model name of `residual transformer`.
* `--name`: model name of `masked transformer`.
* `--cond_scale`: scale of classifer-free guidance.
* `--time_steps`: number of iterations for inference.
* `--ext`: filename for saving evaluation results.
The final evaluation results will be saved in `./checkpoints/<dataset_name>/<name>/eval/<ext>.log`
</details>
## Acknowlegements
We sincerely thank the open-sourcing of these works where our code is based on:
[deep-motion-editing](https://github.com/DeepMotionEditing/deep-motion-editing), [Muse](https://github.com/lucidrains/muse-maskgit-pytorch), [vector-quantize-pytorch](https://github.com/lucidrains/vector-quantize-pytorch), [T2M-GPT](https://github.com/Mael-zys/T2M-GPT), [MDM](https://github.com/GuyTevet/motion-diffusion-model/tree/main) and [MLD](https://github.com/ChenFengYe/motion-latent-diffusion/tree/main)
## License
This code is distributed under an [MIT LICENSE](https://github.com/EricGuo5513/momask-codes/tree/main?tab=MIT-1-ov-file#readme).
Note that our code depends on other libraries, including SMPL, SMPL-X, PyTorch3D, and uses datasets which each have their own respective licenses that must also be followed. | {"title": "MoMask", "emoji": "\ud83c\udfad", "colorFrom": "pink", "colorTo": "purple", "sdk": "gradio", "sdk_version": "3.24.1", "app_file": "app.py", "pinned": false} | null | andrewatef/MoMask-test | [
"arxiv:2312.00063",
"region:us"
] | 2024-02-07T13:33:10+00:00 | [
"2312.00063"
] | [] | TAGS
#arxiv-2312.00063 #region-us
|
# MoMask: Generative Masked Modeling of 3D Human Motions
## [[Project Page]](URL [[Paper]](URL
!teaser_image
If you find our code or paper helpful, please consider citing:
## :postbox: News
2023-12-19 --- Release scripts for temporal inpainting.
2023-12-15 --- Release codes and models for momask. Including training/eval/generation scripts.
2023-11-29 --- Initialized the webpage and git project.
## :round_pushpin: Get You Ready
<details>
### 1. Conda Environment
We test our code on Python 3.7.13 and PyTorch 1.7.1
### 2. Models and Dependencies
#### Download Pre-trained Models
#### Download Evaluation Models and Gloves
For evaluation only.
#### Troubleshooting
To address the download error related to gdown: "Cannot retrieve the public link of the file. You may need to change the permission to 'Anyone with the link', or have had many accesses". A potential solution is to run 'pip install --upgrade --no-cache-dir gdown', as suggested on URL This should help resolve the issue.
#### (Optional) Download Mannually
Visit [[Google Drive]](URL to download the models and evaluators mannually.
### 3. Get Data
You have two options here:
* Skip getting data, if you just want to generate motions using *own* descriptions.
* Get full data, if you want to *re-train* and *evaluate* the model.
(a). Full data (text + motion)
HumanML3D - Follow the instruction in HumanML3D, then copy the result dataset to our repository:
KIT-Download from HumanML3D, then place result in './dataset/KIT-ML'
####
</details>
## :rocket: Demo
<details>
### (a) Generate from a single prompt
### (b) Generate from a prompt file
An example of prompt file is given in './assets/text_prompt.txt'. Please follow the format of '<text description>#<motion length>' at each line. Motion length indicates the number of poses, which must be integeter and will be rounded by 4. In our work, motion is in 20 fps.
If you write '<text description>#NA', our model will determine a length. Note once there is one NA, all the others will be NA automatically.
A few more parameters you may be interested:
* '--repeat_times': number of replications for generation, default '1'.
* '--motion_length': specify the number of poses for generation, only applicable in (a).
The output files are stored under folder './generation/<ext>/'. They are
* 'numpy files': generated motions with shape of (nframe, 22, 3), under subfolder './joints'.
* 'video files': stick figure animation in mp4 format, under subfolder './animation'.
* 'bvh files': bvh files of the generated motion, under subfolder './animation'.
We also apply naive foot ik to the generated motions, see files with suffix '_ik'. It sometimes works well, but sometimes will fail.
</details>
## :dancers: Visualization
<details>
All the animations are manually rendered in blender. We use the characters from mixamo. You need to download the characters in T-Pose with skeleton.
### Retargeting
For retargeting, we found rokoko usually leads to large error on foot. On the other hand, URL.transfer shows more precise retargetting. You could watch the tutorial here.
Following these steps:
* Download URL.transfer from the github, and install it in blender.
* Import both the motion files (.bvh) and character files (.fbx) in blender.
* 'Shift + Select' the both source and target skeleton. (Do not need to be Rest Position)
* Switch to 'Pose Mode', then unfold the 'KeeMapRig' tool at the top-right corner of the view window.
* Load and read the bone mapping file './assets/URL'(or 'URL' if it doesn't work). This file is manually made by us. It works for most characters in mixamo. You could make your own.
* Adjust the 'Number of Samples', 'Source Rig', 'Destination Rig Name'.
* Clik 'Transfer Animation from Source Destination', wait a few seconds.
We didn't tried other retargetting tools. Welcome to comment if you find others are more useful.
### Scene
We use this scene for animation.
</details>
## :clapper: Temporal Inpainting
<details>
We conduct mask-based editing in the m-transformer stage, followed by the regeneration of residual tokens for the entire sequence. To load your own motion, provide the path through '--source_motion'. Utilize '-msec' to specify the mask section, supporting either ratio or frame index. For instance, '-msec 0.3,0.6' with 'max_motion_length=196' is equivalent to '-msec 59,118', indicating the editing of the frame section [59, 118].
Note: Presently, the source motion must adhere to the format of a HumanML3D dim-263 feature vector. An example motion vector data from the HumanML3D test set is available in 'example_data/URL'. To process your own motion data, you can utilize the 'process_file' function from 'utils/motion_process.py'.
</details>
## :space_invader: Train Your Own Models
<details>
Note: You have to train RVQ BEFORE training masked/residual transformers. The latter two can be trained simultaneously.
### Train RVQ
### Train Masked Transformer
### Train Residual Transformer
* '--dataset_name': motion dataset, 't2m' for HumanML3D and 'kit' for KIT-ML.
* '--name': name your model. This will create to model space as './checkpoints/<dataset_name>/<name>'
* '--gpu_id': GPU id.
* '--batch_size': we use '512' for rvq training. For masked/residual transformer, we use '64' on HumanML3D and '16' for KIT-ML.
* '--num_quantizers': number of quantization layers, '6' is used in our case.
* '--quantize_drop_prob': quantization dropout ratio, '0.2' is used.
* '--vq_name': when training masked/residual transformer, you need to specify the name of rvq model for tokenization.
* '--cond_drop_prob': condition drop ratio, for classifier-free guidance. '0.2' is used.
* '--share_weight': whether to share the projection/embedding weights in residual transformer.
All the pre-trained models and intermediate results will be saved in space './checkpoints/<dataset_name>/<name>'.
</details>
## :book: Evaluation
<details>
### Evaluate RVQ Reconstruction:
HumanML3D:
KIT-ML:
### Evaluate Text2motion Generation:
HumanML3D:
KIT-ML:
* '--res_name': model name of 'residual transformer'.
* '--name': model name of 'masked transformer'.
* '--cond_scale': scale of classifer-free guidance.
* '--time_steps': number of iterations for inference.
* '--ext': filename for saving evaluation results.
The final evaluation results will be saved in './checkpoints/<dataset_name>/<name>/eval/<ext>.log'
</details>
## Acknowlegements
We sincerely thank the open-sourcing of these works where our code is based on:
deep-motion-editing, Muse, vector-quantize-pytorch, T2M-GPT, MDM and MLD
## License
This code is distributed under an MIT LICENSE.
Note that our code depends on other libraries, including SMPL, SMPL-X, PyTorch3D, and uses datasets which each have their own respective licenses that must also be followed. | [
"# MoMask: Generative Masked Modeling of 3D Human Motions",
"## [[Project Page]](URL [[Paper]](URL\n!teaser_image\n\nIf you find our code or paper helpful, please consider citing:",
"## :postbox: News\n 2023-12-19 --- Release scripts for temporal inpainting.\n\n 2023-12-15 --- Release codes and models for momask. Including training/eval/generation scripts.\n\n 2023-11-29 --- Initialized the webpage and git project.",
"## :round_pushpin: Get You Ready\n\n<details>",
"### 1. Conda Environment\n\nWe test our code on Python 3.7.13 and PyTorch 1.7.1",
"### 2. Models and Dependencies",
"#### Download Pre-trained Models",
"#### Download Evaluation Models and Gloves\nFor evaluation only.",
"#### Troubleshooting\nTo address the download error related to gdown: \"Cannot retrieve the public link of the file. You may need to change the permission to 'Anyone with the link', or have had many accesses\". A potential solution is to run 'pip install --upgrade --no-cache-dir gdown', as suggested on URL This should help resolve the issue.",
"#### (Optional) Download Mannually\nVisit [[Google Drive]](URL to download the models and evaluators mannually.",
"### 3. Get Data\n\nYou have two options here:\n* Skip getting data, if you just want to generate motions using *own* descriptions.\n* Get full data, if you want to *re-train* and *evaluate* the model.\n\n(a). Full data (text + motion)\n\nHumanML3D - Follow the instruction in HumanML3D, then copy the result dataset to our repository:\n\nKIT-Download from HumanML3D, then place result in './dataset/KIT-ML'",
"#### \n\n</details>",
"## :rocket: Demo\n<details>",
"### (a) Generate from a single prompt",
"### (b) Generate from a prompt file\nAn example of prompt file is given in './assets/text_prompt.txt'. Please follow the format of '<text description>#<motion length>' at each line. Motion length indicates the number of poses, which must be integeter and will be rounded by 4. In our work, motion is in 20 fps.\n\nIf you write '<text description>#NA', our model will determine a length. Note once there is one NA, all the others will be NA automatically.\n\n\n\n\nA few more parameters you may be interested:\n* '--repeat_times': number of replications for generation, default '1'.\n* '--motion_length': specify the number of poses for generation, only applicable in (a).\n\nThe output files are stored under folder './generation/<ext>/'. They are\n* 'numpy files': generated motions with shape of (nframe, 22, 3), under subfolder './joints'.\n* 'video files': stick figure animation in mp4 format, under subfolder './animation'.\n* 'bvh files': bvh files of the generated motion, under subfolder './animation'.\n\nWe also apply naive foot ik to the generated motions, see files with suffix '_ik'. It sometimes works well, but sometimes will fail.\n \n</details>",
"## :dancers: Visualization\n<details>\n\nAll the animations are manually rendered in blender. We use the characters from mixamo. You need to download the characters in T-Pose with skeleton.",
"### Retargeting\nFor retargeting, we found rokoko usually leads to large error on foot. On the other hand, URL.transfer shows more precise retargetting. You could watch the tutorial here.\n\nFollowing these steps:\n* Download URL.transfer from the github, and install it in blender.\n* Import both the motion files (.bvh) and character files (.fbx) in blender.\n* 'Shift + Select' the both source and target skeleton. (Do not need to be Rest Position)\n* Switch to 'Pose Mode', then unfold the 'KeeMapRig' tool at the top-right corner of the view window.\n* Load and read the bone mapping file './assets/URL'(or 'URL' if it doesn't work). This file is manually made by us. It works for most characters in mixamo. You could make your own.\n* Adjust the 'Number of Samples', 'Source Rig', 'Destination Rig Name'.\n* Clik 'Transfer Animation from Source Destination', wait a few seconds.\n\nWe didn't tried other retargetting tools. Welcome to comment if you find others are more useful.",
"### Scene\n\nWe use this scene for animation.\n\n\n</details>",
"## :clapper: Temporal Inpainting\n<details>\nWe conduct mask-based editing in the m-transformer stage, followed by the regeneration of residual tokens for the entire sequence. To load your own motion, provide the path through '--source_motion'. Utilize '-msec' to specify the mask section, supporting either ratio or frame index. For instance, '-msec 0.3,0.6' with 'max_motion_length=196' is equivalent to '-msec 59,118', indicating the editing of the frame section [59, 118]. \n\n\n\nNote: Presently, the source motion must adhere to the format of a HumanML3D dim-263 feature vector. An example motion vector data from the HumanML3D test set is available in 'example_data/URL'. To process your own motion data, you can utilize the 'process_file' function from 'utils/motion_process.py'.\n\n</details>",
"## :space_invader: Train Your Own Models\n<details>\n\n\nNote: You have to train RVQ BEFORE training masked/residual transformers. The latter two can be trained simultaneously.",
"### Train RVQ",
"### Train Masked Transformer",
"### Train Residual Transformer\n\n\n* '--dataset_name': motion dataset, 't2m' for HumanML3D and 'kit' for KIT-ML. \n* '--name': name your model. This will create to model space as './checkpoints/<dataset_name>/<name>'\n* '--gpu_id': GPU id.\n* '--batch_size': we use '512' for rvq training. For masked/residual transformer, we use '64' on HumanML3D and '16' for KIT-ML.\n* '--num_quantizers': number of quantization layers, '6' is used in our case.\n* '--quantize_drop_prob': quantization dropout ratio, '0.2' is used.\n* '--vq_name': when training masked/residual transformer, you need to specify the name of rvq model for tokenization.\n* '--cond_drop_prob': condition drop ratio, for classifier-free guidance. '0.2' is used.\n* '--share_weight': whether to share the projection/embedding weights in residual transformer.\n\nAll the pre-trained models and intermediate results will be saved in space './checkpoints/<dataset_name>/<name>'.\n</details>",
"## :book: Evaluation\n<details>",
"### Evaluate RVQ Reconstruction:\nHumanML3D:\n\nKIT-ML:",
"### Evaluate Text2motion Generation:\nHumanML3D:\n\nKIT-ML:\n\n\n* '--res_name': model name of 'residual transformer'. \n* '--name': model name of 'masked transformer'. \n* '--cond_scale': scale of classifer-free guidance.\n* '--time_steps': number of iterations for inference.\n* '--ext': filename for saving evaluation results.\n\nThe final evaluation results will be saved in './checkpoints/<dataset_name>/<name>/eval/<ext>.log'\n\n</details>",
"## Acknowlegements\n\nWe sincerely thank the open-sourcing of these works where our code is based on: \n\ndeep-motion-editing, Muse, vector-quantize-pytorch, T2M-GPT, MDM and MLD",
"## License\nThis code is distributed under an MIT LICENSE.\n\nNote that our code depends on other libraries, including SMPL, SMPL-X, PyTorch3D, and uses datasets which each have their own respective licenses that must also be followed."
] | [
"TAGS\n#arxiv-2312.00063 #region-us \n",
"# MoMask: Generative Masked Modeling of 3D Human Motions",
"## [[Project Page]](URL [[Paper]](URL\n!teaser_image\n\nIf you find our code or paper helpful, please consider citing:",
"## :postbox: News\n 2023-12-19 --- Release scripts for temporal inpainting.\n\n 2023-12-15 --- Release codes and models for momask. Including training/eval/generation scripts.\n\n 2023-11-29 --- Initialized the webpage and git project.",
"## :round_pushpin: Get You Ready\n\n<details>",
"### 1. Conda Environment\n\nWe test our code on Python 3.7.13 and PyTorch 1.7.1",
"### 2. Models and Dependencies",
"#### Download Pre-trained Models",
"#### Download Evaluation Models and Gloves\nFor evaluation only.",
"#### Troubleshooting\nTo address the download error related to gdown: \"Cannot retrieve the public link of the file. You may need to change the permission to 'Anyone with the link', or have had many accesses\". A potential solution is to run 'pip install --upgrade --no-cache-dir gdown', as suggested on URL This should help resolve the issue.",
"#### (Optional) Download Mannually\nVisit [[Google Drive]](URL to download the models and evaluators mannually.",
"### 3. Get Data\n\nYou have two options here:\n* Skip getting data, if you just want to generate motions using *own* descriptions.\n* Get full data, if you want to *re-train* and *evaluate* the model.\n\n(a). Full data (text + motion)\n\nHumanML3D - Follow the instruction in HumanML3D, then copy the result dataset to our repository:\n\nKIT-Download from HumanML3D, then place result in './dataset/KIT-ML'",
"#### \n\n</details>",
"## :rocket: Demo\n<details>",
"### (a) Generate from a single prompt",
"### (b) Generate from a prompt file\nAn example of prompt file is given in './assets/text_prompt.txt'. Please follow the format of '<text description>#<motion length>' at each line. Motion length indicates the number of poses, which must be integeter and will be rounded by 4. In our work, motion is in 20 fps.\n\nIf you write '<text description>#NA', our model will determine a length. Note once there is one NA, all the others will be NA automatically.\n\n\n\n\nA few more parameters you may be interested:\n* '--repeat_times': number of replications for generation, default '1'.\n* '--motion_length': specify the number of poses for generation, only applicable in (a).\n\nThe output files are stored under folder './generation/<ext>/'. They are\n* 'numpy files': generated motions with shape of (nframe, 22, 3), under subfolder './joints'.\n* 'video files': stick figure animation in mp4 format, under subfolder './animation'.\n* 'bvh files': bvh files of the generated motion, under subfolder './animation'.\n\nWe also apply naive foot ik to the generated motions, see files with suffix '_ik'. It sometimes works well, but sometimes will fail.\n \n</details>",
"## :dancers: Visualization\n<details>\n\nAll the animations are manually rendered in blender. We use the characters from mixamo. You need to download the characters in T-Pose with skeleton.",
"### Retargeting\nFor retargeting, we found rokoko usually leads to large error on foot. On the other hand, URL.transfer shows more precise retargetting. You could watch the tutorial here.\n\nFollowing these steps:\n* Download URL.transfer from the github, and install it in blender.\n* Import both the motion files (.bvh) and character files (.fbx) in blender.\n* 'Shift + Select' the both source and target skeleton. (Do not need to be Rest Position)\n* Switch to 'Pose Mode', then unfold the 'KeeMapRig' tool at the top-right corner of the view window.\n* Load and read the bone mapping file './assets/URL'(or 'URL' if it doesn't work). This file is manually made by us. It works for most characters in mixamo. You could make your own.\n* Adjust the 'Number of Samples', 'Source Rig', 'Destination Rig Name'.\n* Clik 'Transfer Animation from Source Destination', wait a few seconds.\n\nWe didn't tried other retargetting tools. Welcome to comment if you find others are more useful.",
"### Scene\n\nWe use this scene for animation.\n\n\n</details>",
"## :clapper: Temporal Inpainting\n<details>\nWe conduct mask-based editing in the m-transformer stage, followed by the regeneration of residual tokens for the entire sequence. To load your own motion, provide the path through '--source_motion'. Utilize '-msec' to specify the mask section, supporting either ratio or frame index. For instance, '-msec 0.3,0.6' with 'max_motion_length=196' is equivalent to '-msec 59,118', indicating the editing of the frame section [59, 118]. \n\n\n\nNote: Presently, the source motion must adhere to the format of a HumanML3D dim-263 feature vector. An example motion vector data from the HumanML3D test set is available in 'example_data/URL'. To process your own motion data, you can utilize the 'process_file' function from 'utils/motion_process.py'.\n\n</details>",
"## :space_invader: Train Your Own Models\n<details>\n\n\nNote: You have to train RVQ BEFORE training masked/residual transformers. The latter two can be trained simultaneously.",
"### Train RVQ",
"### Train Masked Transformer",
"### Train Residual Transformer\n\n\n* '--dataset_name': motion dataset, 't2m' for HumanML3D and 'kit' for KIT-ML. \n* '--name': name your model. This will create to model space as './checkpoints/<dataset_name>/<name>'\n* '--gpu_id': GPU id.\n* '--batch_size': we use '512' for rvq training. For masked/residual transformer, we use '64' on HumanML3D and '16' for KIT-ML.\n* '--num_quantizers': number of quantization layers, '6' is used in our case.\n* '--quantize_drop_prob': quantization dropout ratio, '0.2' is used.\n* '--vq_name': when training masked/residual transformer, you need to specify the name of rvq model for tokenization.\n* '--cond_drop_prob': condition drop ratio, for classifier-free guidance. '0.2' is used.\n* '--share_weight': whether to share the projection/embedding weights in residual transformer.\n\nAll the pre-trained models and intermediate results will be saved in space './checkpoints/<dataset_name>/<name>'.\n</details>",
"## :book: Evaluation\n<details>",
"### Evaluate RVQ Reconstruction:\nHumanML3D:\n\nKIT-ML:",
"### Evaluate Text2motion Generation:\nHumanML3D:\n\nKIT-ML:\n\n\n* '--res_name': model name of 'residual transformer'. \n* '--name': model name of 'masked transformer'. \n* '--cond_scale': scale of classifer-free guidance.\n* '--time_steps': number of iterations for inference.\n* '--ext': filename for saving evaluation results.\n\nThe final evaluation results will be saved in './checkpoints/<dataset_name>/<name>/eval/<ext>.log'\n\n</details>",
"## Acknowlegements\n\nWe sincerely thank the open-sourcing of these works where our code is based on: \n\ndeep-motion-editing, Muse, vector-quantize-pytorch, T2M-GPT, MDM and MLD",
"## License\nThis code is distributed under an MIT LICENSE.\n\nNote that our code depends on other libraries, including SMPL, SMPL-X, PyTorch3D, and uses datasets which each have their own respective licenses that must also be followed."
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"passage: TAGS\n#arxiv-2312.00063 #region-us \n# MoMask: Generative Masked Modeling of 3D Human Motions## [[Project Page]](URL [[Paper]](URL\n!teaser_image\n\nIf you find our code or paper helpful, please consider citing:## :postbox: News\n 2023-12-19 --- Release scripts for temporal inpainting.\n\n 2023-12-15 --- Release codes and models for momask. Including training/eval/generation scripts.\n\n 2023-11-29 --- Initialized the webpage and git project.## :round_pushpin: Get You Ready\n\n<details>### 1. Conda Environment\n\nWe test our code on Python 3.7.13 and PyTorch 1.7.1### 2. Models and Dependencies#### Download Pre-trained Models#### Download Evaluation Models and Gloves\nFor evaluation only.#### Troubleshooting\nTo address the download error related to gdown: \"Cannot retrieve the public link of the file. You may need to change the permission to 'Anyone with the link', or have had many accesses\". A potential solution is to run 'pip install --upgrade --no-cache-dir gdown', as suggested on URL This should help resolve the issue.#### (Optional) Download Mannually\nVisit [[Google Drive]](URL to download the models and evaluators mannually.### 3. Get Data\n\nYou have two options here:\n* Skip getting data, if you just want to generate motions using *own* descriptions.\n* Get full data, if you want to *re-train* and *evaluate* the model.\n\n(a). Full data (text + motion)\n\nHumanML3D - Follow the instruction in HumanML3D, then copy the result dataset to our repository:\n\nKIT-Download from HumanML3D, then place result in './dataset/KIT-ML'#### \n\n</details>## :rocket: Demo\n<details>### (a) Generate from a single prompt",
"passage: ### (b) Generate from a prompt file\nAn example of prompt file is given in './assets/text_prompt.txt'. Please follow the format of '<text description>#<motion length>' at each line. Motion length indicates the number of poses, which must be integeter and will be rounded by 4. In our work, motion is in 20 fps.\n\nIf you write '<text description>#NA', our model will determine a length. Note once there is one NA, all the others will be NA automatically.\n\n\n\n\nA few more parameters you may be interested:\n* '--repeat_times': number of replications for generation, default '1'.\n* '--motion_length': specify the number of poses for generation, only applicable in (a).\n\nThe output files are stored under folder './generation/<ext>/'. They are\n* 'numpy files': generated motions with shape of (nframe, 22, 3), under subfolder './joints'.\n* 'video files': stick figure animation in mp4 format, under subfolder './animation'.\n* 'bvh files': bvh files of the generated motion, under subfolder './animation'.\n\nWe also apply naive foot ik to the generated motions, see files with suffix '_ik'. It sometimes works well, but sometimes will fail.\n \n</details>## :dancers: Visualization\n<details>\n\nAll the animations are manually rendered in blender. We use the characters from mixamo. You need to download the characters in T-Pose with skeleton.### Retargeting\nFor retargeting, we found rokoko usually leads to large error on foot. On the other hand, URL.transfer shows more precise retargetting. You could watch the tutorial here.\n\nFollowing these steps:\n* Download URL.transfer from the github, and install it in blender.\n* Import both the motion files (.bvh) and character files (.fbx) in blender.\n* 'Shift + Select' the both source and target skeleton. (Do not need to be Rest Position)\n* Switch to 'Pose Mode', then unfold the 'KeeMapRig' tool at the top-right corner of the view window.\n* Load and read the bone mapping file './assets/URL'(or 'URL' if it doesn't work). This file is manually made by us. It works for most characters in mixamo. You could make your own.\n* Adjust the 'Number of Samples', 'Source Rig', 'Destination Rig Name'.\n* Clik 'Transfer Animation from Source Destination', wait a few seconds.\n\nWe didn't tried other retargetting tools. Welcome to comment if you find others are more useful.### Scene\n\nWe use this scene for animation.\n\n\n</details>",
"passage: ## :clapper: Temporal Inpainting\n<details>\nWe conduct mask-based editing in the m-transformer stage, followed by the regeneration of residual tokens for the entire sequence. To load your own motion, provide the path through '--source_motion'. Utilize '-msec' to specify the mask section, supporting either ratio or frame index. For instance, '-msec 0.3,0.6' with 'max_motion_length=196' is equivalent to '-msec 59,118', indicating the editing of the frame section [59, 118]. \n\n\n\nNote: Presently, the source motion must adhere to the format of a HumanML3D dim-263 feature vector. An example motion vector data from the HumanML3D test set is available in 'example_data/URL'. To process your own motion data, you can utilize the 'process_file' function from 'utils/motion_process.py'.\n\n</details>## :space_invader: Train Your Own Models\n<details>\n\n\nNote: You have to train RVQ BEFORE training masked/residual transformers. The latter two can be trained simultaneously.### Train RVQ### Train Masked Transformer### Train Residual Transformer\n\n\n* '--dataset_name': motion dataset, 't2m' for HumanML3D and 'kit' for KIT-ML. \n* '--name': name your model. This will create to model space as './checkpoints/<dataset_name>/<name>'\n* '--gpu_id': GPU id.\n* '--batch_size': we use '512' for rvq training. For masked/residual transformer, we use '64' on HumanML3D and '16' for KIT-ML.\n* '--num_quantizers': number of quantization layers, '6' is used in our case.\n* '--quantize_drop_prob': quantization dropout ratio, '0.2' is used.\n* '--vq_name': when training masked/residual transformer, you need to specify the name of rvq model for tokenization.\n* '--cond_drop_prob': condition drop ratio, for classifier-free guidance. '0.2' is used.\n* '--share_weight': whether to share the projection/embedding weights in residual transformer.\n\nAll the pre-trained models and intermediate results will be saved in space './checkpoints/<dataset_name>/<name>'.\n</details>## :book: Evaluation\n<details>### Evaluate RVQ Reconstruction:\nHumanML3D:\n\nKIT-ML:"
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null | null | null |
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** YLFCY
- **Model type:** [More Information Needed]
- **License:** unlicense
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
用于表情分类。使用keras导入。分类结果对应的数字与fer2013训练集中各表情对应文件夹数字相同。
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"license": "unlicense", "datasets": ["Jeneral/fer-2013"], "metrics": ["accuracy"]} | null | YLFCY/buckwheat-mini | [
"dataset:Jeneral/fer-2013",
"arxiv:1910.09700",
"license:unlicense",
"region:us"
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"1910.09700"
] | [] | TAGS
#dataset-Jeneral/fer-2013 #arxiv-1910.09700 #license-unlicense #region-us
|
## Model Details
### Model Description
- Developed by: YLFCY
- Model type:
- License: unlicense
### Model Sources [optional]
- Demo [optional]:
## Uses
用于表情分类。使用keras导入。分类结果对应的数字与fer2013训练集中各表情对应文件夹数字相同。
### 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
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APA:
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null | null | transformers | [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# KoSOLAR-10.7B-v0.3
## Join Our Community on Discord!
If you're passionate about the field of Large Language Models and wish to exchange knowledge and insights, we warmly invite you to join our Discord server. It's worth noting that Korean is the primary language used in this server. The landscape of LLM is evolving rapidly, and without active sharing, our collective knowledge risks becoming outdated swiftly. Let's collaborate and drive greater impact together! Join us here: [Discord Link](https://discord.gg/b27bAHg95m).
## Our Dedicated Team (Alphabetical Order)
| Research | Engineering | Product Management | UX Design |
|-----------------|-----------------|--------------------|--------------
| Myeongho Jeong | Geon Kim | Bokyung Huh | Eunsue Choi |
| Seungduk Kim | Rifqi Alfi | | |
| Seungtaek Choi | Sanghoon Han | | |
| | Suhyun Kang | | |
## About the Model
This model is a Korean vocabulary-extended version of [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0), specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the `lm_head` embeddings for the already existing tokens while preserving the original parameters of the base model.
### Technical Deep Dive
TBU
### Usage and Limitations
Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.
### Training Details
TBU | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "upstage/SOLAR-10.7B-v1.0", "model-index": [{"name": "yanolja/KoSOLAR-10.7B-v0.3", "results": []}]} | text-generation | yanolja/KoSOLAR-10.7B-v0.3 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:upstage/SOLAR-10.7B-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T13:34:50+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #base_model-upstage/SOLAR-10.7B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
KoSOLAR-10.7B-v0.3
==================
Join Our Community on Discord!
------------------------------
If you're passionate about the field of Large Language Models and wish to exchange knowledge and insights, we warmly invite you to join our Discord server. It's worth noting that Korean is the primary language used in this server. The landscape of LLM is evolving rapidly, and without active sharing, our collective knowledge risks becoming outdated swiftly. Let's collaborate and drive greater impact together! Join us here: Discord Link.
Our Dedicated Team (Alphabetical Order)
---------------------------------------
About the Model
---------------
This model is a Korean vocabulary-extended version of upstage/SOLAR-10.7B-v1.0, specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the 'lm\_head' embeddings for the already existing tokens while preserving the original parameters of the base model.
### Technical Deep Dive
TBU
### Usage and Limitations
Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.
### Training Details
TBU
| [
"### Technical Deep Dive\n\n\nTBU",
"### Usage and Limitations\n\n\nKeep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.",
"### Training Details\n\n\nTBU"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #base_model-upstage/SOLAR-10.7B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Technical Deep Dive\n\n\nTBU",
"### Usage and Limitations\n\n\nKeep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.",
"### Training Details\n\n\nTBU"
] | [
83,
9,
51,
6
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #base_model-upstage/SOLAR-10.7B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Technical Deep Dive\n\n\nTBU### Usage and Limitations\n\n\nKeep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.### Training Details\n\n\nTBU"
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] |
null | null | transformers |
## Exllama v2 Quantizations of Kunocchini-7b-128k-test
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
# The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/Test157t/Kunocchini-7b-128k-test
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/Bartowski/Kunocchini-7b-128k-test-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Kunocchini-7b-128k-test-exl2 Kunocchini-7b-128k-test-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Kunocchini-7b-128k-test-exl2`:
```shell
mkdir Kunocchini-7b-128k-test-exl2
huggingface-cli download bartowski/Kunocchini-7b-128k-test-exl2 --local-dir Kunocchini-7b-128k-test-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Kunocchini-7b-128k-test-exl2-6_5
huggingface-cli download bartowski/Kunocchini-7b-128k-test-exl2 --revision 6_5 --local-dir Kunocchini-7b-128k-test-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Kunocchini-7b-128k-test-exl2-6.5
huggingface-cli download bartowski/Kunocchini-7b-128k-test-exl2 --revision 6_5 --local-dir Kunocchini-7b-128k-test-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski | {"library_name": "transformers", "tags": ["mergekit", "merge", "alpaca", "mistral"], "base_model": ["SanjiWatsuki/Kunoichi-DPO-v2-7B", "Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context"], "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | text-generation | bartowski/Kunocchini-7b-128k-test-exl2 | [
"transformers",
"mergekit",
"merge",
"alpaca",
"mistral",
"text-generation",
"base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"base_model:Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context",
"endpoints_compatible",
"region:us"
] | 2024-02-07T13:35:21+00:00 | [] | [] | TAGS
#transformers #mergekit #merge #alpaca #mistral #text-generation #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context #endpoints_compatible #region-us
| Exllama v2 Quantizations of Kunocchini-7b-128k-test
---------------------------------------------------
Using <a href="URL ExLlamaV2 v0.0.13 for quantization.
The "main" branch only contains the URL, download one of the other branches for the model (see below)
=====================================================================================================
Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions.
Original model: URL
Download instructions
---------------------
With git:
With huggingface hub (credit to TheBloke for instructions):
To download the 'main' (only useful if you only care about URL) branch to a folder called 'Kunocchini-7b-128k-test-exl2':
To download from a different branch, add the '--revision' parameter:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#transformers #mergekit #merge #alpaca #mistral #text-generation #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context #endpoints_compatible #region-us \n"
] | [
87
] | [
"passage: TAGS\n#transformers #mergekit #merge #alpaca #mistral #text-generation #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context #endpoints_compatible #region-us \n"
] | [
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null | null | diffusers |
# LoRA DreamBooth - danaleee/CL_rank10_iter500_noval
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks teddybear", "inference": true} | text-to-image | danaleee/CL_rank10_iter500_noval | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-07T13:36:38+00:00 | [] | [] | TAGS
#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
|
# LoRA DreamBooth - danaleee/CL_rank10_iter500_noval
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| [
"# LoRA DreamBooth - danaleee/CL_rank10_iter500_noval\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n\n\nLoRA for the text encoder was enabled: False."
] | [
"TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n",
"# LoRA DreamBooth - danaleee/CL_rank10_iter500_noval\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n\n\nLoRA for the text encoder was enabled: False."
] | [
70,
88
] | [
"passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank10_iter500_noval\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n\n\nLoRA for the text encoder was enabled: False."
] | [
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null | null | null |
Creative Adversarial Network
epochs: 100
dataset jlbaker361/wikiart-balanced1000
n classes 27
batch_size 128
images where resized to 384
and then center cropped to: 256
used clip=True
conditional =False
discriminator parameters:
init_dim: 32
final_dim 512
generator parameters:
input noise_dim: 100
| {} | null | jlbaker361/dcgan-wikiart1000-clip-resized-256 | [
"region:us"
] | 2024-02-07T13:39:20+00:00 | [] | [] | TAGS
#region-us
|
Creative Adversarial Network
epochs: 100
dataset jlbaker361/wikiart-balanced1000
n classes 27
batch_size 128
images where resized to 384
and then center cropped to: 256
used clip=True
conditional =False
discriminator parameters:
init_dim: 32
final_dim 512
generator parameters:
input noise_dim: 100
| [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
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null | null | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 1983 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 198,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | kate-arb/e5-base-fine | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | 2024-02-07T13:42:02+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 1983 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 1983 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 1983 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
43,
50,
38,
29,
101,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 1983 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | null |
Creative Adversarial Network
epochs: 100
dataset jlbaker361/wikiart-balanced1000
n classes 27
batch_size 32
images where resized to 384
and then center cropped to: 256
used clip=False
conditional =False
discriminator parameters:
init_dim: 32
final_dim 512
generator parameters:
input noise_dim: 100
| {} | null | jlbaker361/dcgan-wikiart1000-resized-256 | [
"region:us"
] | 2024-02-07T13:42:10+00:00 | [] | [] | TAGS
#region-us
|
Creative Adversarial Network
epochs: 100
dataset jlbaker361/wikiart-balanced1000
n classes 27
batch_size 32
images where resized to 384
and then center cropped to: 256
used clip=False
conditional =False
discriminator parameters:
init_dim: 32
final_dim 512
generator parameters:
input noise_dim: 100
| [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
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null | null | null | Spade Echo HololiveCN rmvpe 200epoches | {"license": "unknown"} | null | IshimaIshimsky/spade_echo | [
"license:unknown",
"region:us"
] | 2024-02-07T13:42:34+00:00 | [] | [] | TAGS
#license-unknown #region-us
| Spade Echo HololiveCN rmvpe 200epoches | [] | [
"TAGS\n#license-unknown #region-us \n"
] | [
13
] | [
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null | null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | automatic-speech-recognition | spsither/wav2vec2_run9.18 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #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.
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## 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
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#### 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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[optional]
BibTeX:
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## Glossary [optional]
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null | null | diffusers | ### My-Pet-Dog Dreambooth model trained by naviam following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
.jpg)
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | naviam/my-pet-dog | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-07T13:48:56+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-Dog Dreambooth model trained by naviam following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
!0.jpg)
| [
"### My-Pet-Dog Dreambooth model trained by naviam following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: GoX19932gAS\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
"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-Dog Dreambooth model trained by naviam following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: GoX19932gAS\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
73,
58
] | [
"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-Dog Dreambooth model trained by naviam following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: GoX19932gAS\n\nSample pictures of this concept:\n\n !0.jpg)"
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null | null | null |
<!-- 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. -->
# experiments
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the dialogstudio dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8522
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9036 | 0.4 | 22 | 1.9222 |
| 1.8237 | 0.8 | 44 | 1.8800 |
| 1.6753 | 1.2 | 66 | 1.8615 |
| 1.7803 | 1.6 | 88 | 1.8537 |
| 1.646 | 2.0 | 110 | 1.8522 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "datasets": ["dialogstudio"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "experiments", "results": []}]} | null | smrynrz20/experiments | [
"safetensors",
"generated_from_trainer",
"dataset:dialogstudio",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | 2024-02-07T13:50:11+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #dataset-dialogstudio #base_model-meta-llama/Llama-2-7b-hf #region-us
| experiments
===========
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the dialogstudio dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8522
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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1+cu117
* Datasets 2.14.4
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.13.3"
] | [
"TAGS\n#safetensors #generated_from_trainer #dataset-dialogstudio #base_model-meta-llama/Llama-2-7b-hf #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: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.13.3"
] | [
43,
145,
4,
33
] | [
"passage: TAGS\n#safetensors #generated_from_trainer #dataset-dialogstudio #base_model-meta-llama/Llama-2-7b-hf #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: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.13.3"
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null | null | peft |
# Model Card for Model ID
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| {"library_name": "peft", "base_model": "IB13/sft_t5_base_processed_model"} | null | IB13/t5_ppo_model_3 | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:IB13/sft_t5_base_processed_model",
"region:us"
] | 2024-02-07T13:50:42+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-IB13/sft_t5_base_processed_model #region-us
|
# Model Card for Model ID
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## Evaluation
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\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",
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"## Evaluation",
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"#### 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",
"## Training procedure",
"### Framework versions\n\n\n- PEFT 0.6.2"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-IB13/sft_t5_base_processed_model #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\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",
"## Training procedure",
"### Framework versions\n\n\n- PEFT 0.6.2"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-IB13/sft_t5_base_processed_model #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\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## Training procedure### Framework versions\n\n\n- PEFT 0.6.2"
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] |
null | null | transformers |
# mitkox/sqlcoder-7b-2-2
This model was converted to MLX format from [`defog/sqlcoder-7b-2`]().
Refer to the [original model card](https://huggingface.co/defog/sqlcoder-7b-2) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mitkox/sqlcoder-7b-2-2")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "cc-by-sa-4.0", "library_name": "transformers", "tags": ["mlx"], "pipeline_tag": "text-generation"} | text-generation | mitkox/sqlcoder-7b-2-2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mlx",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T13:51:14+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mlx #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mitkox/sqlcoder-7b-2-2
This model was converted to MLX format from ['defog/sqlcoder-7b-2']().
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mitkox/sqlcoder-7b-2-2\nThis model was converted to MLX format from ['defog/sqlcoder-7b-2']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mlx #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mitkox/sqlcoder-7b-2-2\nThis model was converted to MLX format from ['defog/sqlcoder-7b-2']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
61,
52,
5
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mlx #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mitkox/sqlcoder-7b-2-2\nThis model was converted to MLX format from ['defog/sqlcoder-7b-2']().\nRefer to the original model card for more details on the model.## Use with mlx"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | Americo/phi-2-finetuned-farmatodo | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T13:52:19+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #phi #text-generation #custom_code #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]:",
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"## Uses",
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"## 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 #phi #text-generation #custom_code #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 #phi #text-generation #custom_code #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 | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: itsdhanoob/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | reinforcement-learning | itsdhanoob/ppo-Pyramids | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | 2024-02-07T13:53:10+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: itsdhanoob/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: itsdhanoob/ppo-Pyramids\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: itsdhanoob/ppo-Pyramids\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
48,
204
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: itsdhanoob/ppo-Pyramids\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | null | # Sbaitso AI Voice model for RVC
This is a voice model trained on sbaitso, most famously known for the voice of SCP 079 in the SCP : Containement Breach video game.
If you use this AI voice model please credit me by linking this page in the description. | {"language": ["en"]} | null | DoctorKrazy/sbaitso | [
"en",
"region:us"
] | 2024-02-07T13:56:12+00:00 | [] | [
"en"
] | TAGS
#en #region-us
| # Sbaitso AI Voice model for RVC
This is a voice model trained on sbaitso, most famously known for the voice of SCP 079 in the SCP : Containement Breach video game.
If you use this AI voice model please credit me by linking this page in the description. | [
"# Sbaitso AI Voice model for RVC\n\nThis is a voice model trained on sbaitso, most famously known for the voice of SCP 079 in the SCP : Containement Breach video game.\n\nIf you use this AI voice model please credit me by linking this page in the description."
] | [
"TAGS\n#en #region-us \n",
"# Sbaitso AI Voice model for RVC\n\nThis is a voice model trained on sbaitso, most famously known for the voice of SCP 079 in the SCP : Containement Breach video game.\n\nIf you use this AI voice model please credit me by linking this page in the description."
] | [
8,
66
] | [
"passage: TAGS\n#en #region-us \n# Sbaitso AI Voice model for RVC\n\nThis is a voice model trained on sbaitso, most famously known for the voice of SCP 079 in the SCP : Containement Breach video game.\n\nIf you use this AI voice model please credit me by linking this page in the description."
] | [
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null | null | null |
Titel: Die Schlange und der Verräter
Panel 1:
(Weite Einstellung. Ein dunkler Wald mit dichten Bäumen und einem schmalen Pfad. Die Sonne scheint durch die Baumkronen. Im Vordergrund sehen wir eine Schlange, die elegant über den Pfad gleitet.)
Erzähler: In einem geheimnisvollen Wald, weit weg von jeglicher Zivilisation, lebte eine kluge Schlange namens Seraphina.
Panel 2:
(Nahaufnahme von Seraphina. Sie hat glänzende Schuppen und leuchtende Augen. Sie sieht misstrauisch aus.)
Seraphina: Dieser Wald birgt viele Geheimnisse. Ich muss vorsichtig sein und darauf achten, wem ich vertraue.
Panel 3:
(Seraphina nähert sich einem anderen Tier, das halb im Schatten liegt. Es ist ein fuchsähnliches Wesen mit einem schelmischen Ausdruck.)
Seraphina: Guten Tag, Fremder. Ich bin Seraphina. Was verschlägt dich in diesen Wald?
Panel 4:
(Das fuchsähnliche Wesen lächelt und entblößt seine spitzen Zähne. Es sieht bedrohlich aus.)
Fuchsähnliches Wesen: Ich bin Vex, und ich durchstreife diesen Wald auf der Suche nach Abenteuern. Vielleicht können wir zusammen auf Entdeckungsreise gehen?
Panel 5:
(Seraphina betrachtet Vex skeptisch. Ihre Augen schimmern verdächtig.)
Seraphina: Ich bin misstrauisch gegenüber Fremden, Vex. Warum sollte ich dir vertrauen?
Panel 6:
(Vex legt eine Pfote auf sein Herz und sieht Seraphina mit einem unschuldigen Blick an.)
Vex: Mein Herz ist rein, Seraphina. Ich schwöre, ich werde dir kein Leid zufügen. Ich suche nur nach einem Freund, mit dem ich diese Abenteuer teilen kann.
Panel 7:
(Seraphina denkt einen Moment nach, dann nickt sie langsam.)
Seraphina: Gut, Vex. Wir können zusammen reisen, aber sei gewarnt: Wenn du mich betrügst, wird es Konsequenzen geben.
Panel 8:
(Die beiden setzen ihre Reise durch den Wald fort, während die Sonne langsam untergeht. Seraphina bleibt wachsam, während Vex fröhlich plappert.)
Erzähler: Und so begann die ungewöhnliche Freundschaft zwischen Seraphina und Vex. Doch in den Schatten lauerte ein düsteres Geheimnis, das bald ans Licht kommen würde. | {} | null | OptimusAz/Comic | [
"region:us"
] | 2024-02-07T13:57:03+00:00 | [] | [] | TAGS
#region-us
|
Titel: Die Schlange und der Verräter
Panel 1:
(Weite Einstellung. Ein dunkler Wald mit dichten Bäumen und einem schmalen Pfad. Die Sonne scheint durch die Baumkronen. Im Vordergrund sehen wir eine Schlange, die elegant über den Pfad gleitet.)
Erzähler: In einem geheimnisvollen Wald, weit weg von jeglicher Zivilisation, lebte eine kluge Schlange namens Seraphina.
Panel 2:
(Nahaufnahme von Seraphina. Sie hat glänzende Schuppen und leuchtende Augen. Sie sieht misstrauisch aus.)
Seraphina: Dieser Wald birgt viele Geheimnisse. Ich muss vorsichtig sein und darauf achten, wem ich vertraue.
Panel 3:
(Seraphina nähert sich einem anderen Tier, das halb im Schatten liegt. Es ist ein fuchsähnliches Wesen mit einem schelmischen Ausdruck.)
Seraphina: Guten Tag, Fremder. Ich bin Seraphina. Was verschlägt dich in diesen Wald?
Panel 4:
(Das fuchsähnliche Wesen lächelt und entblößt seine spitzen Zähne. Es sieht bedrohlich aus.)
Fuchsähnliches Wesen: Ich bin Vex, und ich durchstreife diesen Wald auf der Suche nach Abenteuern. Vielleicht können wir zusammen auf Entdeckungsreise gehen?
Panel 5:
(Seraphina betrachtet Vex skeptisch. Ihre Augen schimmern verdächtig.)
Seraphina: Ich bin misstrauisch gegenüber Fremden, Vex. Warum sollte ich dir vertrauen?
Panel 6:
(Vex legt eine Pfote auf sein Herz und sieht Seraphina mit einem unschuldigen Blick an.)
Vex: Mein Herz ist rein, Seraphina. Ich schwöre, ich werde dir kein Leid zufügen. Ich suche nur nach einem Freund, mit dem ich diese Abenteuer teilen kann.
Panel 7:
(Seraphina denkt einen Moment nach, dann nickt sie langsam.)
Seraphina: Gut, Vex. Wir können zusammen reisen, aber sei gewarnt: Wenn du mich betrügst, wird es Konsequenzen geben.
Panel 8:
(Die beiden setzen ihre Reise durch den Wald fort, während die Sonne langsam untergeht. Seraphina bleibt wachsam, während Vex fröhlich plappert.)
Erzähler: Und so begann die ungewöhnliche Freundschaft zwischen Seraphina und Vex. Doch in den Schatten lauerte ein düsteres Geheimnis, das bald ans Licht kommen würde. | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
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null | null | sentence-transformers |
# ahessamb/sentence-transformers-all-MiniLM-L6-v2-20epoch-100perp-contrastiveloss
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ahessamb/sentence-transformers-all-MiniLM-L6-v2-20epoch-100perp-contrastiveloss')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ahessamb/sentence-transformers-all-MiniLM-L6-v2-20epoch-100perp-contrastiveloss)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2334 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 2, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 15,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 233,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | ahessamb/sentence-transformers-all-MiniLM-L6-v2-20epoch-100perp-contrastiveloss | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | 2024-02-07T13:58:44+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# ahessamb/sentence-transformers-all-MiniLM-L6-v2-20epoch-100perp-contrastiveloss
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 2334 with parameters:
Loss:
'sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# ahessamb/sentence-transformers-all-MiniLM-L6-v2-20epoch-100perp-contrastiveloss\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2334 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# ahessamb/sentence-transformers-all-MiniLM-L6-v2-20epoch-100perp-contrastiveloss\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2334 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
40,
76,
38,
29,
76,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n# ahessamb/sentence-transformers-all-MiniLM-L6-v2-20epoch-100perp-contrastiveloss\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2334 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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] |
null | null | transformers |
# MusicGen - Small - 300M
MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts.
It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass.
By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.
MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*.
Four checkpoints are released:
- [**small** (this checkpoint)](https://huggingface.co/facebook/musicgen-small)
- [medium](https://huggingface.co/facebook/musicgen-medium)
- [large](https://huggingface.co/facebook/musicgen-large)
- [melody](https://huggingface.co/facebook/musicgen-melody)
## Example
Try out MusicGen yourself!
* Audiocraft Colab:
<a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
* Hugging Face Colab:
<a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
* Hugging Face Demo:
<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
</a>
## 🤗 Transformers Usage
You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards.
1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) and scipy:
```
pip install --upgrade pip
pip install --upgrade transformers scipy
```
2. Run inference via the `Text-to-Audio` (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code!
```python
from transformers import pipeline
import scipy
synthesiser = pipeline("text-to-audio", "facebook/musicgen-small")
music = synthesiser("lo-fi music with a soothing melody", forward_params={"do_sample": True})
scipy.io.wavfile.write("musicgen_out.wav", rate=music["sampling_rate"], data=music["audio"])
```
3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control.
```python
from transformers import AutoProcessor, MusicgenForConditionalGeneration
processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
inputs = processor(
text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
padding=True,
return_tensors="pt",
)
audio_values = model.generate(**inputs, max_new_tokens=256)
```
3. Listen to the audio samples either in an ipynb notebook:
```python
from IPython.display import Audio
sampling_rate = model.config.audio_encoder.sampling_rate
Audio(audio_values[0].numpy(), rate=sampling_rate)
```
Or save them as a `.wav` file using a third-party library, e.g. `scipy`:
```python
import scipy
sampling_rate = model.config.audio_encoder.sampling_rate
scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy())
```
For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen).
## Audiocraft Usage
You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft):
1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft)
```
pip install git+https://github.com/facebookresearch/audiocraft.git
```
2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed:
```
apt-get install ffmpeg
```
3. Run the following Python code:
```py
from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write
model = MusicGen.get_pretrained("small")
model.set_generation_params(duration=8) # generate 8 seconds.
descriptions = ["happy rock", "energetic EDM"]
wav = model.generate(descriptions) # generates 2 samples.
for idx, one_wav in enumerate(wav):
# Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness")
```
## Model details
**Organization developing the model:** The FAIR team of Meta AI.
**Model date:** MusicGen was trained between April 2023 and May 2023.
**Model version:** This is the version 1 of the model.
**Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.
**Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284).
**Citation details:**
```
@misc{copet2023simple,
title={Simple and Controllable Music Generation},
author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
year={2023},
eprint={2306.05284},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
**License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
**Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
## Intended use
**Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including:
- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
- Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs
**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
**Out-of-scope use cases:** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
## Metrics
**Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark:
- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish)
- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST)
- CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model
Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes:
- Overall quality of the music samples;
- Text relevance to the provided text input;
- Adherence to the melody for melody-guided music generation.
More details on performance measures and human studies can be found in the paper.
**Decision thresholds:** Not applicable.
## Evaluation datasets
The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set.
## Training datasets
The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing.
## Evaluation results
Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper.
| Model | Frechet Audio Distance | KLD | Text Consistency | Chroma Cosine Similarity |
|---|---|---|---|---|
| **facebook/musicgen-small** | 4.88 | 1.42 | 0.27 | - |
| facebook/musicgen-medium | 5.14 | 1.38 | 0.28 | - |
| facebook/musicgen-large | 5.48 | 1.37 | 0.28 | - |
| facebook/musicgen-melody | 4.93 | 1.41 | 0.27 | 0.44 |
More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284), in the Results section.
## Limitations and biases
**Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model.
**Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs).
**Limitations:**
- The model is not able to generate realistic vocals.
- The model has been trained with English descriptions and will not perform as well in other languages.
- The model does not perform equally well for all music styles and cultures.
- The model sometimes generates end of songs, collapsing to silence.
- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results.
**Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive.
**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
**Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks. | {"license": "cc-by-nc-4.0", "tags": ["musicgen", "audiocraft"], "inference": true, "pipeline_tag": "text-to-audio", "widget": [{"text": "a funky house with 80s hip hop vibes", "example_title": "Prompt 1"}, {"text": "a chill song with influences from lofi, chillstep and downtempo", "example_title": "Prompt 2"}, {"text": "a catchy beat for a podcast intro", "example_title": "Prompt 3"}]} | text-to-audio | reach-vb/musicgen-small-test | [
"transformers",
"pytorch",
"safetensors",
"musicgen",
"text-to-audio",
"audiocraft",
"arxiv:2306.05284",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T14:01:19+00:00 | [
"2306.05284"
] | [] | TAGS
#transformers #pytorch #safetensors #musicgen #text-to-audio #audiocraft #arxiv-2306.05284 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| MusicGen - Small - 300M
=======================
MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts.
It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass.
By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.
MusicGen was published in Simple and Controllable Music Generation by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*.
Four checkpoints are released:
* small (this checkpoint)
* medium
* large
* melody
Example
-------
Try out MusicGen yourself!
* Audiocraft Colab:
<a target="\_blank" href="URL
<img src="URL alt="Open In Colab"/>
* Hugging Face Colab:
<a target="\_blank" href="URL
<img src="URL alt="Open In Colab"/>
* Hugging Face Demo:
<a target="\_blank" href="URL
<img src="URL alt="Open in HuggingFace"/>
Transformers Usage
------------------
You can run MusicGen locally with the Transformers library from version 4.31.0 onwards.
1. First install the Transformers library and scipy:
2. Run inference via the 'Text-to-Audio' (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code!
3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control.
4. Listen to the audio samples either in an ipynb notebook:
Or save them as a '.wav' file using a third-party library, e.g. 'scipy':
For more details on using the MusicGen model for inference using the Transformers library, refer to the MusicGen docs.
Audiocraft Usage
----------------
You can also run MusicGen locally through the original Audiocraft library:
1. First install the 'audiocraft' library
2. Make sure to have 'ffmpeg' installed:
3. Run the following Python code:
Model details
-------------
Organization developing the model: The FAIR team of Meta AI.
Model date: MusicGen was trained between April 2023 and May 2023.
Model version: This is the version 1 of the model.
Model type: MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.
Paper or resources for more information: More information can be found in the paper Simple and Controllable Music Generation.
Citation details:
License: Code is released under MIT, model weights are released under CC-BY-NC 4.0.
Where to send questions or comments about the model: Questions and comments about MusicGen can be sent via the Github repository of the project, or by opening an issue.
Intended use
------------
Primary intended use: The primary use of MusicGen is research on AI-based music generation, including:
* Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
* Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs
Primary intended users: The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
Out-of-scope use cases: The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Metrics
-------
Models performance measures: We used the following objective measure to evaluate the model on a standard music benchmark:
* Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish)
* Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST)
* CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model
Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes:
* Overall quality of the music samples;
* Text relevance to the provided text input;
* Adherence to the melody for melody-guided music generation.
More details on performance measures and human studies can be found in the paper.
Decision thresholds: Not applicable.
Evaluation datasets
-------------------
The model was evaluated on the MusicCaps benchmark and on an in-domain held-out evaluation set, with no artist overlap with the training set.
Training datasets
-----------------
The model was trained on licensed data using the following sources: the Meta Music Initiative Sound Collection, Shutterstock music collection and the Pond5 music collection. See the paper for more details about the training set and corresponding preprocessing.
Evaluation results
------------------
Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source Hybrid Transformer for Music Source Separation (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper.
More information can be found in the paper Simple and Controllable Music Generation, in the Results section.
Limitations and biases
----------------------
Data: The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model.
Mitigations: Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source Hybrid Transformer for Music Source Separation (HT-Demucs).
Limitations:
* The model is not able to generate realistic vocals.
* The model has been trained with English descriptions and will not perform as well in other languages.
* The model does not perform equally well for all music styles and cultures.
* The model sometimes generates end of songs, collapsing to silence.
* It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results.
Biases: The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive.
Risks and harms: Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
Use cases: Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
| [] | [
"TAGS\n#transformers #pytorch #safetensors #musicgen #text-to-audio #audiocraft #arxiv-2306.05284 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n"
] | [
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null | null | transformers | ## Model Description
EXPERIMENTAL MODEL, NOT FINAL, IT HAS SOME ISSUES, I DIDN'T TEST IT TOO MUCH YET
Yi-34B 200K base model fine-tuned on RAWrr v1 dataset via DPO and then fine-tuned on AEZAKMI v2 dataset via SFT. DPO training took around 6 hours, SFT took around 25 hours.
I used `unsloth` for both stages.
It's like airoboros but with less gptslop, no refusals and less typical language used by RLHFed OpenAI models.
Say goodbye to "It's important to remember"! \
Prompt format is standard chatml. Don't expect it to be good at math, riddles or be crazy smart. My end goal with AEZAKMI is to create a cozy free chatbot.
Base model used for fine-tuning was 200k context Yi-34B-Llama model shared by larryvrh.
Training was done with max_position_embeddings set at 4096. Then it was reverted back to 200K after applying LoRA.
## Prompt Format
I recommend using ChatML format, as this was used during fine-tune. \
Here's a prompt format you should use, you can set a different system message, model seems to respect that fine, so it wasn't overfitted.
Both `A chat.` and `A chat with uncensored assistant.` system prompt work fine and are pretty refusal-free.
```
<|im_start|>system
A chat with uncensored assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Intended uses & limitations
It's a chat model, not a base completion-only one.
Use is limited by Yi license. Since no-robots dataset was used for making rawrr_v1, I guess you maybe shouldn't use it for commercial activities.
## Known Issues
I recommend to set repetition penalty to something around 1.05 to avoid repetition. So far I had somewhat good experience running this model with temperature 1.0-1.2.
It seems like the strongest anti-refusal bias is at 0 ctx - the first prompt. But it's also present, albeit a little bit less, further down. I plan to expand rawrr dataset and include more samples without system prompt, this should help here.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" alt="made with Unsloth" width="400" height="64"/>](https://github.com/unslothai/unsloth)
## Unsloth training parameters DPO Stage
- lora_r: 16
- lora_alpha: 32
- max_length: 500
- learning_rate: 0.00005
- lr_scheduler_type: "linear"
- target_modules: ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",]
- gradient_accumulation_steps: 16
- per_device_batch_size: 1
- num_train_epochs: 1
Script used for DPO training can be found here:
https://huggingface.co/adamo1139/Yi-34B-200K-rawrr1-LORA-DPO-experimental-r3/blob/main/yi-34b-dpo-unsloth-1.py
## Unsloth training parameters SFT Stage
- lora_r: 16
- lora_alpha: 32
- max_length: 2400
- learning_rate: 0.000095
- lr_scheduler_type: "cosine"
- lr_scheduler_kwargs: {
"num_cycles" : 0.25,
}
- target_modules: ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",]
- gradient_accumulation_steps: 1
- per_device_batch_size: 1
- num_train_epochs: 2
Script used for SFT training can be found here (older run, different hyperparameters):
https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA/blob/main/yi-34b-aezakmi-sft-1-hf.py
### Credits
Thanks to mlabonne, Daniel Han and Michael Han for providing open source code that was used for fine-tuning. | {"license": "other", "datasets": ["adamo1139/AEZAKMI_v2", "adamo1139/rawrr_v1"], "license_name": "yi-license", "license_link": "LICENSE"} | text-generation | waldie/Yi-34B-200K-AEZAKMI-RAW-2901-4bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:adamo1139/AEZAKMI_v2",
"dataset:adamo1139/rawrr_v1",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:02:14+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #dataset-adamo1139/AEZAKMI_v2 #dataset-adamo1139/rawrr_v1 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Model Description
EXPERIMENTAL MODEL, NOT FINAL, IT HAS SOME ISSUES, I DIDN'T TEST IT TOO MUCH YET
Yi-34B 200K base model fine-tuned on RAWrr v1 dataset via DPO and then fine-tuned on AEZAKMI v2 dataset via SFT. DPO training took around 6 hours, SFT took around 25 hours.
I used 'unsloth' for both stages.
It's like airoboros but with less gptslop, no refusals and less typical language used by RLHFed OpenAI models.
Say goodbye to "It's important to remember"! \
Prompt format is standard chatml. Don't expect it to be good at math, riddles or be crazy smart. My end goal with AEZAKMI is to create a cozy free chatbot.
Base model used for fine-tuning was 200k context Yi-34B-Llama model shared by larryvrh.
Training was done with max_position_embeddings set at 4096. Then it was reverted back to 200K after applying LoRA.
## Prompt Format
I recommend using ChatML format, as this was used during fine-tune. \
Here's a prompt format you should use, you can set a different system message, model seems to respect that fine, so it wasn't overfitted.
Both 'A chat.' and 'A chat with uncensored assistant.' system prompt work fine and are pretty refusal-free.
## Intended uses & limitations
It's a chat model, not a base completion-only one.
Use is limited by Yi license. Since no-robots dataset was used for making rawrr_v1, I guess you maybe shouldn't use it for commercial activities.
## Known Issues
I recommend to set repetition penalty to something around 1.05 to avoid repetition. So far I had somewhat good experience running this model with temperature 1.0-1.2.
It seems like the strongest anti-refusal bias is at 0 ctx - the first prompt. But it's also present, albeit a little bit less, further down. I plan to expand rawrr dataset and include more samples without system prompt, this should help here.
<img src="URL alt="made with Unsloth" width="400" height="64"/>
## Unsloth training parameters DPO Stage
- lora_r: 16
- lora_alpha: 32
- max_length: 500
- learning_rate: 0.00005
- lr_scheduler_type: "linear"
- target_modules: ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",]
- gradient_accumulation_steps: 16
- per_device_batch_size: 1
- num_train_epochs: 1
Script used for DPO training can be found here:
URL
## Unsloth training parameters SFT Stage
- lora_r: 16
- lora_alpha: 32
- max_length: 2400
- learning_rate: 0.000095
- lr_scheduler_type: "cosine"
- lr_scheduler_kwargs: {
"num_cycles" : 0.25,
}
- target_modules: ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",]
- gradient_accumulation_steps: 1
- per_device_batch_size: 1
- num_train_epochs: 2
Script used for SFT training can be found here (older run, different hyperparameters):
URL
### Credits
Thanks to mlabonne, Daniel Han and Michael Han for providing open source code that was used for fine-tuning. | [
"## Model Description\n\nEXPERIMENTAL MODEL, NOT FINAL, IT HAS SOME ISSUES, I DIDN'T TEST IT TOO MUCH YET\n\n\nYi-34B 200K base model fine-tuned on RAWrr v1 dataset via DPO and then fine-tuned on AEZAKMI v2 dataset via SFT. DPO training took around 6 hours, SFT took around 25 hours.\nI used 'unsloth' for both stages.\nIt's like airoboros but with less gptslop, no refusals and less typical language used by RLHFed OpenAI models.\nSay goodbye to \"It's important to remember\"! \\\nPrompt format is standard chatml. Don't expect it to be good at math, riddles or be crazy smart. My end goal with AEZAKMI is to create a cozy free chatbot.\nBase model used for fine-tuning was 200k context Yi-34B-Llama model shared by larryvrh.\n\nTraining was done with max_position_embeddings set at 4096. Then it was reverted back to 200K after applying LoRA.",
"## Prompt Format\n\nI recommend using ChatML format, as this was used during fine-tune. \\\nHere's a prompt format you should use, you can set a different system message, model seems to respect that fine, so it wasn't overfitted.\nBoth 'A chat.' and 'A chat with uncensored assistant.' system prompt work fine and are pretty refusal-free.",
"## Intended uses & limitations\n\nIt's a chat model, not a base completion-only one.\nUse is limited by Yi license. Since no-robots dataset was used for making rawrr_v1, I guess you maybe shouldn't use it for commercial activities.",
"## Known Issues\n\nI recommend to set repetition penalty to something around 1.05 to avoid repetition. So far I had somewhat good experience running this model with temperature 1.0-1.2.\n\nIt seems like the strongest anti-refusal bias is at 0 ctx - the first prompt. But it's also present, albeit a little bit less, further down. I plan to expand rawrr dataset and include more samples without system prompt, this should help here.\n\n<img src=\"URL alt=\"made with Unsloth\" width=\"400\" height=\"64\"/>",
"## Unsloth training parameters DPO Stage\n\n- lora_r: 16\n- lora_alpha: 32\n- max_length: 500\n- learning_rate: 0.00005\n- lr_scheduler_type: \"linear\"\n- target_modules: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n \"gate_proj\", \"up_proj\", \"down_proj\",]\n- gradient_accumulation_steps: 16\n- per_device_batch_size: 1\n- num_train_epochs: 1\n\n Script used for DPO training can be found here:\n URL",
"## Unsloth training parameters SFT Stage\n\n- lora_r: 16\n- lora_alpha: 32\n- max_length: 2400\n- learning_rate: 0.000095\n- lr_scheduler_type: \"cosine\"\n- lr_scheduler_kwargs: {\n \"num_cycles\" : 0.25,\n }\n- target_modules: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n \"gate_proj\", \"up_proj\", \"down_proj\",]\n- gradient_accumulation_steps: 1\n- per_device_batch_size: 1\n- num_train_epochs: 2\n\n Script used for SFT training can be found here (older run, different hyperparameters):\n URL\n\n ### Credits\n Thanks to mlabonne, Daniel Han and Michael Han for providing open source code that was used for fine-tuning."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #dataset-adamo1139/AEZAKMI_v2 #dataset-adamo1139/rawrr_v1 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model Description\n\nEXPERIMENTAL MODEL, NOT FINAL, IT HAS SOME ISSUES, I DIDN'T TEST IT TOO MUCH YET\n\n\nYi-34B 200K base model fine-tuned on RAWrr v1 dataset via DPO and then fine-tuned on AEZAKMI v2 dataset via SFT. DPO training took around 6 hours, SFT took around 25 hours.\nI used 'unsloth' for both stages.\nIt's like airoboros but with less gptslop, no refusals and less typical language used by RLHFed OpenAI models.\nSay goodbye to \"It's important to remember\"! \\\nPrompt format is standard chatml. Don't expect it to be good at math, riddles or be crazy smart. My end goal with AEZAKMI is to create a cozy free chatbot.\nBase model used for fine-tuning was 200k context Yi-34B-Llama model shared by larryvrh.\n\nTraining was done with max_position_embeddings set at 4096. Then it was reverted back to 200K after applying LoRA.",
"## Prompt Format\n\nI recommend using ChatML format, as this was used during fine-tune. \\\nHere's a prompt format you should use, you can set a different system message, model seems to respect that fine, so it wasn't overfitted.\nBoth 'A chat.' and 'A chat with uncensored assistant.' system prompt work fine and are pretty refusal-free.",
"## Intended uses & limitations\n\nIt's a chat model, not a base completion-only one.\nUse is limited by Yi license. Since no-robots dataset was used for making rawrr_v1, I guess you maybe shouldn't use it for commercial activities.",
"## Known Issues\n\nI recommend to set repetition penalty to something around 1.05 to avoid repetition. So far I had somewhat good experience running this model with temperature 1.0-1.2.\n\nIt seems like the strongest anti-refusal bias is at 0 ctx - the first prompt. But it's also present, albeit a little bit less, further down. I plan to expand rawrr dataset and include more samples without system prompt, this should help here.\n\n<img src=\"URL alt=\"made with Unsloth\" width=\"400\" height=\"64\"/>",
"## Unsloth training parameters DPO Stage\n\n- lora_r: 16\n- lora_alpha: 32\n- max_length: 500\n- learning_rate: 0.00005\n- lr_scheduler_type: \"linear\"\n- target_modules: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n \"gate_proj\", \"up_proj\", \"down_proj\",]\n- gradient_accumulation_steps: 16\n- per_device_batch_size: 1\n- num_train_epochs: 1\n\n Script used for DPO training can be found here:\n URL",
"## Unsloth training parameters SFT Stage\n\n- lora_r: 16\n- lora_alpha: 32\n- max_length: 2400\n- learning_rate: 0.000095\n- lr_scheduler_type: \"cosine\"\n- lr_scheduler_kwargs: {\n \"num_cycles\" : 0.25,\n }\n- target_modules: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n \"gate_proj\", \"up_proj\", \"down_proj\",]\n- gradient_accumulation_steps: 1\n- per_device_batch_size: 1\n- num_train_epochs: 2\n\n Script used for SFT training can be found here (older run, different hyperparameters):\n URL\n\n ### Credits\n Thanks to mlabonne, Daniel Han and Michael Han for providing open source code that was used for fine-tuning."
] | [
79,
251,
86,
64,
127,
150,
213
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #dataset-adamo1139/AEZAKMI_v2 #dataset-adamo1139/rawrr_v1 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Model Description\n\nEXPERIMENTAL MODEL, NOT FINAL, IT HAS SOME ISSUES, I DIDN'T TEST IT TOO MUCH YET\n\n\nYi-34B 200K base model fine-tuned on RAWrr v1 dataset via DPO and then fine-tuned on AEZAKMI v2 dataset via SFT. DPO training took around 6 hours, SFT took around 25 hours.\nI used 'unsloth' for both stages.\nIt's like airoboros but with less gptslop, no refusals and less typical language used by RLHFed OpenAI models.\nSay goodbye to \"It's important to remember\"! \\\nPrompt format is standard chatml. Don't expect it to be good at math, riddles or be crazy smart. My end goal with AEZAKMI is to create a cozy free chatbot.\nBase model used for fine-tuning was 200k context Yi-34B-Llama model shared by larryvrh.\n\nTraining was done with max_position_embeddings set at 4096. Then it was reverted back to 200K after applying LoRA.## Prompt Format\n\nI recommend using ChatML format, as this was used during fine-tune. \\\nHere's a prompt format you should use, you can set a different system message, model seems to respect that fine, so it wasn't overfitted.\nBoth 'A chat.' and 'A chat with uncensored assistant.' system prompt work fine and are pretty refusal-free.## Intended uses & limitations\n\nIt's a chat model, not a base completion-only one.\nUse is limited by Yi license. Since no-robots dataset was used for making rawrr_v1, I guess you maybe shouldn't use it for commercial activities."
] | [
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null | null | diffusers |
# LoRA DreamBooth - danaleee/CL_rank50_iter500
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks dog", "inference": true} | text-to-image | danaleee/CL_rank50_iter500 | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-07T14:04:54+00:00 | [] | [] | TAGS
#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
|
# LoRA DreamBooth - danaleee/CL_rank50_iter500
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| [
"# LoRA DreamBooth - danaleee/CL_rank50_iter500\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following. \n\n\n\nLoRA for the text encoder was enabled: False."
] | [
"TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n",
"# LoRA DreamBooth - danaleee/CL_rank50_iter500\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following. \n\n\n\nLoRA for the text encoder was enabled: False."
] | [
70,
82
] | [
"passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank50_iter500\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following. \n\n\n\nLoRA for the text encoder was enabled: False."
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] |
null | null | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: frahman/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | reinforcement-learning | frahman/ppo-Huggy | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | 2024-02-07T14:05:04+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: frahman/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: frahman/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n",
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: frahman/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
44,
198
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: frahman/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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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. -->
# smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-3e-4
This model was trained from scratch on the kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4130
- Accuracy: 0.4092
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7346 | 1.0 | 18600 | 3.8972 | 0.3462 |
| 3.4287 | 2.0 | 37200 | 3.6409 | 0.3755 |
| 3.2901 | 3.0 | 55800 | 3.4779 | 0.3900 |
| 3.2043 | 4.0 | 74400 | 3.4166 | 0.3964 |
| 3.1401 | 5.0 | 93000 | 3.3858 | 0.4011 |
| 3.0938 | 6.0 | 111600 | 3.3652 | 0.4039 |
| 3.0576 | 7.0 | 130200 | 3.3590 | 0.4050 |
| 3.0235 | 8.0 | 148800 | 3.3599 | 0.4059 |
| 2.9953 | 9.0 | 167400 | 3.3596 | 0.4070 |
| 2.9671 | 10.0 | 186000 | 3.3670 | 0.4079 |
| 2.9471 | 11.0 | 204600 | 3.3677 | 0.4085 |
| 2.9196 | 12.0 | 223200 | 3.3675 | 0.4088 |
| 2.8999 | 13.0 | 241800 | 3.3742 | 0.4088 |
| 2.8818 | 14.0 | 260400 | 3.3657 | 0.4102 |
| 2.8608 | 15.0 | 279000 | 3.3741 | 0.4094 |
| 2.8388 | 16.0 | 297600 | 3.3850 | 0.4092 |
| 2.8207 | 17.0 | 316200 | 3.3959 | 0.4094 |
| 2.7965 | 18.0 | 334800 | 3.3924 | 0.4097 |
| 2.786 | 19.0 | 353400 | 3.4080 | 0.4092 |
| 2.7601 | 20.0 | 372000 | 3.4130 | 0.4092 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "datasets": ["kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal"], "metrics": ["accuracy"], "model-index": [{"name": "smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-3e-4", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal", "type": "kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal"}, "metrics": [{"type": "accuracy", "value": 0.4091822505859148, "name": "Accuracy"}]}]}]} | text-generation | kanishka/smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-3e-4 | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:13:46+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| smolm-autoreg-bpe-counterfactual-babylm-only\_measure\_nps\_as\_singular\_removal-3e-4
======================================================================================
This model was trained from scratch on the kanishka/counterfactual-babylm-only\_measure\_nps\_as\_singular\_removal dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4130
* Accuracy: 0.4092
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: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 32000
* num\_epochs: 20.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
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"### Training results",
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null | null | peft |
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### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "google/flan-t5-base"} | null | HeydarS/flan-t5-base_peft_v23 | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/flan-t5-base",
"region:us"
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- 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
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.7.2.dev0 | [
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"## Model Details",
"### Model Description\n\n\n\n\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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"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.2.dev0"
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
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"## Model Card Contact",
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null | null | transformers | Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | {"license": "cc-by-2.0"} | text-generation | LoneStriker/Senku-70B-Full-2.65bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:16:49+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
60
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | null | ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_it.layer1_v0.2_wandblog | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-07T14:17:09+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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## 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 | 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": "272.35 +/- 22.03", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | asorokoumov/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T14:18:16+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 | # Model
This is a merge model of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Model Details
### Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Undi95/Mistral-RP-0.1-7B
* MaziyarPanahi/NSFW_DPO_Noromaid-7b-Mistral-7B-Instruct-v0.1
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Undi95/Mistral-RP-0.1-7B
layer_range: [0, 32]
- model: MaziyarPanahi/NSFW_DPO_Noromaid-7b-Mistral-7B-Instruct-v0.1
layer_range: [0, 32]
merge_method: slerp
base_model: Undi95/Mistral-RP-0.1-7B
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 # fallback for rest of tensors
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": []} | text-generation | Ttimofeyka/MistralRP-Noromaid-NSFW-Mistral-7B-GGUF | [
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"mergekit",
"merge",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:19:28+00:00 | [] | [] | TAGS
#transformers #safetensors #gguf #mistral #text-generation #mergekit #merge #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Model
This is a merge model of pre-trained language models created using mergekit.
## Model Details
### Prompt template: Alpaca
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Undi95/Mistral-RP-0.1-7B
* MaziyarPanahi/NSFW_DPO_Noromaid-7b-Mistral-7B-Instruct-v0.1
### Configuration
The following YAML configuration was used to produce this model:
| [
"# Model\n\nThis is a merge model of pre-trained language models created using mergekit.",
"## Model Details",
"### Prompt template: Alpaca",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Undi95/Mistral-RP-0.1-7B\n* MaziyarPanahi/NSFW_DPO_Noromaid-7b-Mistral-7B-Instruct-v0.1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #gguf #mistral #text-generation #mergekit #merge #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model\n\nThis is a merge model of pre-trained language models created using mergekit.",
"## Model Details",
"### Prompt template: Alpaca",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Undi95/Mistral-RP-0.1-7B\n* MaziyarPanahi/NSFW_DPO_Noromaid-7b-Mistral-7B-Instruct-v0.1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
61,
19,
3,
9,
4,
18,
56,
17
] | [
"passage: TAGS\n#transformers #safetensors #gguf #mistral #text-generation #mergekit #merge #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Model\n\nThis is a merge model of pre-trained language models created using mergekit.## Model Details### Prompt template: Alpaca## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* Undi95/Mistral-RP-0.1-7B\n* MaziyarPanahi/NSFW_DPO_Noromaid-7b-Mistral-7B-Instruct-v0.1### Configuration\n\nThe following YAML configuration was used to produce 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. -->
# 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: 0.2146
- Accuracy: 0.9255
- F1: 0.9257
## 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 | 0.3263 | 0.905 | 0.9038 |
| No log | 2.0 | 500 | 0.2146 | 0.9255 | 0.9257 |
### 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": 0.9255, "name": "Accuracy"}, {"type": "f1", "value": 0.9257241957061232, "name": "F1"}]}]}]} | text-classification | jaegon-kim/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"
] | 2024-02-07T14:22:14+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: 0.2146
* Accuracy: 0.9255
* F1: 0.9257
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
| [
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"### 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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"### 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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"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 |
# Passthrough-Latxa-Llama-LlamaCode-7b
Passthrough-Latxa-Llama-LlamaCode-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [HiTZ/latxa-7b-v1](https://huggingface.co/HiTZ/latxa-7b-v1)
* [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
* [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: HiTZ/latxa-7b-v1
layer_range: [0, 24]
- sources:
- model: meta-llama/Llama-2-7b-hf
layer_range: [0, 24]
- sources:
- model: meta-llama/Llama-2-7b-chat-hf
layer_range: [0, 24]
merge_method: passthrough
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "airalribalta/Passthrough-Latxa-Llama-LlamaCode-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "HiTZ/latxa-7b-v1", "meta-llama/Llama-2-7b-hf", "meta-llama/Llama-2-7b-chat-hf"], "base_model": ["HiTZ/latxa-7b-v1", "meta-llama/Llama-2-7b-hf", "meta-llama/Llama-2-7b-chat-hf"]} | text-generation | airalribalta/Passthrough-Latxa-Llama-LlamaCode-7b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"HiTZ/latxa-7b-v1",
"meta-llama/Llama-2-7b-hf",
"meta-llama/Llama-2-7b-chat-hf",
"base_model:HiTZ/latxa-7b-v1",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:23:10+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #meta-llama/Llama-2-7b-hf #meta-llama/Llama-2-7b-chat-hf #base_model-HiTZ/latxa-7b-v1 #base_model-meta-llama/Llama-2-7b-hf #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Passthrough-Latxa-Llama-LlamaCode-7b
Passthrough-Latxa-Llama-LlamaCode-7b is a merge of the following models using LazyMergekit:
* HiTZ/latxa-7b-v1
* meta-llama/Llama-2-7b-hf
* meta-llama/Llama-2-7b-chat-hf
## Configuration
## Usage
| [
"# Passthrough-Latxa-Llama-LlamaCode-7b\n\nPassthrough-Latxa-Llama-LlamaCode-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* meta-llama/Llama-2-7b-hf\n* meta-llama/Llama-2-7b-chat-hf",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #meta-llama/Llama-2-7b-hf #meta-llama/Llama-2-7b-chat-hf #base_model-HiTZ/latxa-7b-v1 #base_model-meta-llama/Llama-2-7b-hf #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Passthrough-Latxa-Llama-LlamaCode-7b\n\nPassthrough-Latxa-Llama-LlamaCode-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* meta-llama/Llama-2-7b-hf\n* meta-llama/Llama-2-7b-chat-hf",
"## Configuration",
"## Usage"
] | [
154,
84,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #meta-llama/Llama-2-7b-hf #meta-llama/Llama-2-7b-chat-hf #base_model-HiTZ/latxa-7b-v1 #base_model-meta-llama/Llama-2-7b-hf #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Passthrough-Latxa-Llama-LlamaCode-7b\n\nPassthrough-Latxa-Llama-LlamaCode-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* meta-llama/Llama-2-7b-hf\n* meta-llama/Llama-2-7b-chat-hf## Configuration## Usage"
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] |
null | null | diffusers |
# LoRA DreamBooth - danaleee/CL_rank50_iter500_valprompt
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks duck", "inference": true} | text-to-image | danaleee/CL_rank50_iter500_valprompt | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-07T14:23:27+00:00 | [] | [] | TAGS
#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
|
# LoRA DreamBooth - danaleee/CL_rank50_iter500_valprompt
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using DreamBooth. You can find some example images in the following.
!img_0
!img_1
!img_2
!img_3
LoRA for the text encoder was enabled: False.
| [
"# LoRA DreamBooth - danaleee/CL_rank50_iter500_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False."
] | [
"TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n",
"# LoRA DreamBooth - danaleee/CL_rank50_iter500_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False."
] | [
70,
107
] | [
"passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank50_iter500_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False."
] | [
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null | null | diffusers |
# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks teddybear", "inference": true} | text-to-image | danaleee/CL_rank10_iter500_valprompt | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-07T14:26:50+00:00 | [] | [] | TAGS
#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
|
# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following.
!img_0
!img_1
!img_2
!img_3
LoRA for the text encoder was enabled: False.
| [
"# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False."
] | [
"TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n",
"# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False."
] | [
70,
109
] | [
"passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False."
] | [
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null | null | transformers | Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | {"license": "cc-by-2.0"} | text-generation | LoneStriker/Senku-70B-Full-3.5bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:27:46+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
60
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# train_2024-02-07-12-03-05
This model is a fine-tuned version of [qwen/Qwen-1_8B-Chat](https://huggingface.co/qwen/Qwen-1_8B-Chat) on the glaive_toolcall 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.0 | {"license": "other", "library_name": "peft", "tags": ["llama-factory", "lora", "generated_from_trainer"], "base_model": "qwen/Qwen-1_8B-Chat", "model-index": [{"name": "train_2024-02-07-12-03-05", "results": []}]} | null | praison/qwen-1.8B-test-function-calling | [
"peft",
"tensorboard",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:qwen/Qwen-1_8B-Chat",
"license:other",
"region:us"
] | 2024-02-07T14:27:47+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #llama-factory #lora #generated_from_trainer #base_model-qwen/Qwen-1_8B-Chat #license-other #region-us
|
# train_2024-02-07-12-03-05
This model is a fine-tuned version of qwen/Qwen-1_8B-Chat on the glaive_toolcall 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.0 | [
"# train_2024-02-07-12-03-05\n\nThis model is a fine-tuned version of qwen/Qwen-1_8B-Chat on the glaive_toolcall 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: 5e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\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- num_epochs: 1.0",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.14.6\n- Tokenizers 0.15.0"
] | [
"TAGS\n#peft #tensorboard #safetensors #llama-factory #lora #generated_from_trainer #base_model-qwen/Qwen-1_8B-Chat #license-other #region-us \n",
"# train_2024-02-07-12-03-05\n\nThis model is a fine-tuned version of qwen/Qwen-1_8B-Chat on the glaive_toolcall 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: 5e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\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- num_epochs: 1.0",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.14.6\n- Tokenizers 0.15.0"
] | [
55,
41,
6,
12,
8,
3,
114,
4,
36
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #llama-factory #lora #generated_from_trainer #base_model-qwen/Qwen-1_8B-Chat #license-other #region-us \n# train_2024-02-07-12-03-05\n\nThis model is a fine-tuned version of qwen/Qwen-1_8B-Chat on the glaive_toolcall 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: 5e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\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- num_epochs: 1.0### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.14.6\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. -->
# smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-3e-4
This model was trained from scratch on the kanishka/counterfactual-babylm-only_other_det_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4419
- Accuracy: 0.4081
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7368 | 1.0 | 18597 | 3.9162 | 0.3452 |
| 3.4348 | 2.0 | 37194 | 3.6327 | 0.3748 |
| 3.2919 | 3.0 | 55791 | 3.5084 | 0.3900 |
| 3.2086 | 4.0 | 74388 | 3.4502 | 0.3956 |
| 3.1474 | 5.0 | 92985 | 3.4235 | 0.3995 |
| 3.1012 | 6.0 | 111582 | 3.4031 | 0.4020 |
| 3.0638 | 7.0 | 130179 | 3.4128 | 0.4030 |
| 3.0262 | 8.0 | 148776 | 3.3998 | 0.4046 |
| 3.0016 | 9.0 | 167373 | 3.3731 | 0.4070 |
| 2.9715 | 10.0 | 185970 | 3.4058 | 0.4062 |
| 2.9481 | 11.0 | 204567 | 3.3875 | 0.4069 |
| 2.9243 | 12.0 | 223164 | 3.4070 | 0.4070 |
| 2.9047 | 13.0 | 241761 | 3.4015 | 0.4079 |
| 2.8797 | 14.0 | 260358 | 3.4114 | 0.4077 |
| 2.8651 | 15.0 | 278955 | 3.4072 | 0.4083 |
| 2.8434 | 16.0 | 297552 | 3.4240 | 0.4075 |
| 2.8255 | 17.0 | 316149 | 3.4179 | 0.4083 |
| 2.8036 | 18.0 | 334746 | 3.4256 | 0.4082 |
| 2.7888 | 19.0 | 353343 | 3.4363 | 0.4083 |
| 2.7701 | 20.0 | 371940 | 3.4419 | 0.4081 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "datasets": ["kanishka/counterfactual-babylm-only_other_det_removal"], "metrics": ["accuracy"], "model-index": [{"name": "smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-3e-4", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "kanishka/counterfactual-babylm-only_other_det_removal", "type": "kanishka/counterfactual-babylm-only_other_det_removal"}, "metrics": [{"type": "accuracy", "value": 0.40813531756892846, "name": "Accuracy"}]}]}]} | text-generation | kanishka/smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-3e-4 | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual-babylm-only_other_det_removal",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:30:14+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-only_other_det_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| smolm-autoreg-bpe-counterfactual-babylm-only\_other\_det\_removal-3e-4
======================================================================
This model was trained from scratch on the kanishka/counterfactual-babylm-only\_other\_det\_removal dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4419
* Accuracy: 0.4081
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: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 32000
* num\_epochs: 20.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* 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: 0.0003\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP",
"### 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 | (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- https://huggingface.co/rombodawg/DeepMagic-Coder-7b
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
```yaml
models:
- model: deepseek-ai_deepseek-coder-6.7b-instruct
parameters:
weight: 1
- model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"} | text-generation | LoneStriker/DeepMagic-Coder-7b-Alt-AWQ | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-07T14:30:30+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
| (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- URL
!image/jpeg
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n"
] | [
55
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n"
] | [
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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": "-0.20 +/- 0.09", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Wajid333/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T14:31:51+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 | null | Requant with iMatrix of : https://huggingface.co/NeverSleepHistorical/MiquMaid-v2-70B-alpha-GGUF
From Q4_K_M through Q8_0.
Q3_K_M quant available, IQ2_XS otw.
For testing purpose, so the folks with 36GB & 24 GB VRAM can use the model.
Some LlamaCPP benchs :
My requant of Miqu :
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag,88.75,,400,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag,88.1,,1000,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag,87.3,,2000,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag_Bin,82,,400,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag_Bin,85.1,,1000,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag_Bin,84.85,,2000,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Arc-Challenge,57.19063545,,299,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Arc-Easy,77.19298246,,570,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,MMLU,50.15974441,,313,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Thruthful-QA,41.49326805,,817,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Winogrande,78.8477,,1267,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,wikitext,4.2957,512,512,2024-01-29 00:00:00,RBF1000000,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,81
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,wikitext,3.8380,512,512,2024-01-29 00:00:00,RBF1000000,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,655
Miqumaid v1 :
- MiquMaid-v1-70B.q3_k_m.gguf,-,Hellaswag,89.25,83.75,400,2024-02-01 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Hellaswag,88,,1000,2024-02-01 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Hellaswag_Bin,82.25,,400,2024-02-01 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Hellaswag_Bin,85,,1000,2024-02-01 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Arc-Challenge,56.85618729,,299,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Arc-Easy,76.31578947,,570,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,MMLU,49.84025559,,313,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Thruthful-QA,40.88127295,,817,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Winogrande,79.0845,,1267,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,wikitext,3.8595,512,512,2024-02-01 00:00:00,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,655
Miqu DPO :
- Miqu-70B-DPO.q3_k_m.gguf,-,Hellaswag,84.5,400,,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Hellaswag,83.8,1000,,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Arc-Challenge,57.85953177,,299,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Arc-Easy,77.36842105,,570,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,MMLU,50.15974441,,313,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Thruthful-QA,42.47246022,,817,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Winogrande,78.7687,,1267,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,wikitext,4.3018,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,81
- Miqu-70B-DPO.q3_k_m.gguf,-,wikitext,3.8469,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,655
Miqumaid v2 Alpha Requant :
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Hellaswag,84.5,,400,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Hellaswag,83.8,,1000,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Arc-Challenge,56.18729097,,299,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Arc-Easy,77.71929825,,570,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,MMLU,47.92332268,,313,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Thruthful-QA,40.39167687,,817,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Winogrande,78.9266,,1267,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,wikitext,4.4132,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,81
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,wikitext,3.9105,512,512,2024-02-07 00:00:00,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,655
The Hellaswag scores are divergent due to a change in LlamaCPP 5-6 days ago, but in fact, they are stable.
The divergence is showed in Miqumaid v1, which goes from 89.25 to 83.75 on the exact same eval. | {} | null | Nexesenex/MiquMaid-v2-70B-alpha-Requant-iMat.GGUF | [
"gguf",
"region:us"
] | 2024-02-07T14:33:25+00:00 | [] | [] | TAGS
#gguf #region-us
| Requant with iMatrix of : URL
From Q4_K_M through Q8_0.
Q3_K_M quant available, IQ2_XS otw.
For testing purpose, so the folks with 36GB & 24 GB VRAM can use the model.
Some LlamaCPP benchs :
My requant of Miqu :
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag,88.75,,400,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag,88.1,,1000,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag,87.3,,2000,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag_Bin,82,,400,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag_Bin,85.1,,1000,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Hellaswag_Bin,84.85,,2000,2024-01-29 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Arc-Challenge,57.19063545,,299,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Arc-Easy,77.19298246,,570,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,MMLU,50.15974441,,313,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Thruthful-QA,41.49326805,,817,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Winogrande,78.8477,,1267,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,wikitext,4.2957,512,512,2024-01-29 00:00:00,RBF1000000,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,81
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,wikitext,3.8380,512,512,2024-01-29 00:00:00,RBF1000000,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,655
Miqumaid v1 :
- MiquMaid-v1-70B.q3_k_m.gguf,-,Hellaswag,89.25,83.75,400,2024-02-01 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Hellaswag,88,,1000,2024-02-01 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Hellaswag_Bin,82.25,,400,2024-02-01 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Hellaswag_Bin,85,,1000,2024-02-01 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Arc-Challenge,56.85618729,,299,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Arc-Easy,76.31578947,,570,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,MMLU,49.84025559,,313,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Thruthful-QA,40.88127295,,817,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,Winogrande,79.0845,,1267,2024-02-01 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- MiquMaid-v1-70B.q3_k_m.gguf,-,wikitext,3.8595,512,512,2024-02-01 00:00:00,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,655
Miqu DPO :
- Miqu-70B-DPO.q3_k_m.gguf,-,Hellaswag,84.5,400,,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Hellaswag,83.8,1000,,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Arc-Challenge,57.85953177,,299,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Arc-Easy,77.36842105,,570,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,MMLU,50.15974441,,313,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Thruthful-QA,42.47246022,,817,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Winogrande,78.7687,,1267,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,wikitext,4.3018,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,81
- Miqu-70B-DPO.q3_k_m.gguf,-,wikitext,3.8469,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,655
Miqumaid v2 Alpha Requant :
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Hellaswag,84.5,,400,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Hellaswag,83.8,,1000,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Arc-Challenge,56.18729097,,299,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Arc-Easy,77.71929825,,570,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,MMLU,47.92332268,,313,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Thruthful-QA,40.39167687,,817,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,Winogrande,78.9266,,1267,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,wikitext,4.4132,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,81
- MiquMaid-v2-70B-alpha-Requant-b2091-iMat-c32_ch200-Q3_K_M.gguf,-,wikitext,3.9105,512,512,2024-02-07 00:00:00,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,655
The Hellaswag scores are divergent due to a change in LlamaCPP 5-6 days ago, but in fact, they are stable.
The divergence is showed in Miqumaid v1, which goes from 89.25 to 83.75 on the exact same eval. | [] | [
"TAGS\n#gguf #region-us \n"
] | [
9
] | [
"passage: TAGS\n#gguf #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. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1938
- eval_accuracy: 0.6375
- eval_runtime: 2.3845
- eval_samples_per_second: 67.099
- eval_steps_per_second: 1.258
- epoch: 38.7
- step: 387
## 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: 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: 50
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "image_classification", "results": []}]} | image-classification | Amadeus99/image_classification | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T14:34:50+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# image_classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1938
- eval_accuracy: 0.6375
- eval_runtime: 2.3845
- eval_samples_per_second: 67.099
- eval_steps_per_second: 1.258
- epoch: 38.7
- step: 387
## 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: 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: 50
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# image_classification\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.1938\n- eval_accuracy: 0.6375\n- eval_runtime: 2.3845\n- eval_samples_per_second: 67.099\n- eval_steps_per_second: 1.258\n- epoch: 38.7\n- step: 387",
"## 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: 5e-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: 50",
"### 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 #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# image_classification\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.1938\n- eval_accuracy: 0.6375\n- eval_runtime: 2.3845\n- eval_samples_per_second: 67.099\n- eval_steps_per_second: 1.258\n- epoch: 38.7\n- step: 387",
"## 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: 5e-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: 50",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
82,
117,
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# image_classification\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.1938\n- eval_accuracy: 0.6375\n- eval_runtime: 2.3845\n- eval_samples_per_second: 67.099\n- eval_steps_per_second: 1.258\n- epoch: 38.7\n- step: 387## 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: 5e-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: 50### 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 |
<!-- 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. -->
# roberta-large-bn-adapter-3.17M-snli-model3
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6239
- Accuracy: 0.792
## 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: 84
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.31 | 1.0 | 8584 | 0.2443 | 0.9115 |
| 0.2958 | 2.0 | 17168 | 0.2302 | 0.9200 |
| 0.2816 | 3.0 | 25752 | 0.2253 | 0.9214 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-bn-adapter-3.17M-snli-model3", "results": []}]} | null | varun-v-rao/roberta-large-bn-adapter-3.17M-snli-model3 | [
"tensorboard",
"generated_from_trainer",
"base_model:roberta-large",
"license:mit",
"region:us"
] | 2024-02-07T14:35:32+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #base_model-roberta-large #license-mit #region-us
| roberta-large-bn-adapter-3.17M-snli-model3
==========================================
This model is a fine-tuned version of roberta-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6239
* Accuracy: 0.792
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: 84
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.1+cu121
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### 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: 84\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\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: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 84\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
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"passage: TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-large #license-mit #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: 84\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | diffusers | ERROR: type should be string, got "\nhttps://civitai.com/models/18732/higuchi-kaede-nijisanji" | {"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers"], "base_model": "runwayml/stable-diffusion-v1-5"} | text-to-image | xshini/HiguchiKaede | [
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"stable-diffusion",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-07T14:37:25+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us
|
URL | [] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n"
] | [
57
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] |
null | null | transformers | (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- https://huggingface.co/rombodawg/DeepMagic-Coder-7b
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
```yaml
models:
- model: deepseek-ai_deepseek-coder-6.7b-instruct
parameters:
weight: 1
- model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"} | text-generation | LoneStriker/DeepMagic-Coder-7b-Alt-GPTQ | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:41:20+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- URL
!image/jpeg
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
52
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers | Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | {"license": "cc-by-2.0"} | text-generation | LoneStriker/Senku-70B-Full-4.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:43:55+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
60
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
null | null | null | (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- https://huggingface.co/rombodawg/DeepMagic-Coder-7b
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
```yaml
models:
- model: deepseek-ai_deepseek-coder-6.7b-instruct
parameters:
weight: 1
- model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"} | null | LoneStriker/DeepMagic-Coder-7b-Alt-GGUF | [
"gguf",
"license:other",
"region:us"
] | 2024-02-07T14:44:22+00:00 | [] | [] | TAGS
#gguf #license-other #region-us
| (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- URL
!image/jpeg
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
| [] | [
"TAGS\n#gguf #license-other #region-us \n"
] | [
14
] | [
"passage: TAGS\n#gguf #license-other #region-us \n"
] | [
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null | null | transformers |
# Model Card for Model ID
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## Model Details
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| {"library_name": "transformers", "tags": []} | text-generation | regisss/test_model | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T14:46:02+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #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.
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## Uses
### Direct Use
### Downstream Use [optional]
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## 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:
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#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Contact
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #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. -->
# wav2vec_RTSplit0207_9
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: 0.0615
- Wer: 0.2450
- Cer: 0.1960
## 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: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.649 | 1.0 | 120 | 3.5943 | 0.9656 | 0.9895 |
| 1.5176 | 2.0 | 240 | 1.2925 | 0.9961 | 0.7557 |
| 0.8538 | 3.0 | 360 | 0.7185 | 0.8163 | 0.5987 |
| 0.6558 | 4.0 | 480 | 0.5508 | 0.7836 | 0.4833 |
| 0.5354 | 5.0 | 600 | 0.4279 | 0.7481 | 0.4720 |
| 0.4094 | 6.0 | 720 | 0.2818 | 0.4718 | 0.2811 |
| 0.3136 | 7.0 | 840 | 0.1658 | 0.3504 | 0.2384 |
| 0.2367 | 8.0 | 960 | 0.0919 | 0.3023 | 0.2065 |
| 0.2104 | 9.0 | 1080 | 0.0615 | 0.2450 | 0.1960 |
### 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_9", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0207_9 | [
"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"
] | 2024-02-07T14:46:57+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\_9
=======================
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: 0.0615
* Wer: 0.2450
* Cer: 0.1960
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: 9
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### 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: 9",
"### 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: 9",
"### 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,
116,
4,
33
] | [
"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: 9### 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 | Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of `meta-llama/Llama-2-7b-hf`.
For this quantization, we used 1 codebook of 16 bits.
Selected evaluation results for this and other models:
| Model | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link |
|------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
| Llama-2-7b | 1x16 | 6.31 | 2.4 | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-1x16-hf) |
| Llama-2-7b | 2x8 | 7.98 | 2.2 | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-2x8-hf) |
| Llama-2-7b | 8x8 | 7.83 | 2.2 | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-8x8-hf) |
| Llama-2-13b| 1x16 | 5.41 | 4.1 | [Link](https://huggingface.co/BlackSamorez/Llama-2-13b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 1x16 | 3.96 | 18.8 | [Link](https://huggingface.co/BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b (THIS)| 2x8 | 4.83 | 18.2 | [Link](https://huggingface.co/BlackSamorez/Llama-2-70b-AQLM-2Bit-2x8-hf) |
| Mixtral-8x7b| 1x16 | 4.37 | 12.6 | [Link](https://huggingface.co/BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf)|
To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the [official GitHub repo](https://github.com/Vahe1994/AQLM). | {} | text-generation | BlackSamorez/Llama-2-70b-AQLM-2Bit-2x8-hf | [
"transformers",
"safetensors",
"llama_aqlm",
"text-generation",
"custom_code",
"arxiv:2401.06118",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] | 2024-02-07T14:48:41+00:00 | [
"2401.06118"
] | [] | TAGS
#transformers #safetensors #llama_aqlm #text-generation #custom_code #arxiv-2401.06118 #autotrain_compatible #endpoints_compatible #8-bit #region-us
| Official AQLM quantization of 'meta-llama/Llama-2-7b-hf'.
For this quantization, we used 1 codebook of 16 bits.
Selected evaluation results for this and other models:
To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the official GitHub repo.
| [] | [
"TAGS\n#transformers #safetensors #llama_aqlm #text-generation #custom_code #arxiv-2401.06118 #autotrain_compatible #endpoints_compatible #8-bit #region-us \n"
] | [
59
] | [
"passage: TAGS\n#transformers #safetensors #llama_aqlm #text-generation #custom_code #arxiv-2401.06118 #autotrain_compatible #endpoints_compatible #8-bit #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": "Trelis/Llama-2-7b-chat-hf-sharded-bf16"} | null | SolaireOfTheSun/Llama-2-7b-chat-hf-sharded-bf16-feinabgestimmt-adapters-1 | [
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# Model Card for Model ID
## Model Details
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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
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#### 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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## Technical Specifications [optional]
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null | null | generic |
# Coreference Resolution for Long Documents
Modified coreference resolution model from [BERT for Coreference Resolution: Baselines and Analysis](https://aclanthology.org/D19-1588/) for handling long documents (~40K words) efficiently (500K words/s on a NVIDIA Tesla V100). The checkpoint is based on AllenNLP's coref-spanbert-large-2021.03.10. This modified model was used in [DAPR: A Benchmark on Document-Aware Passage Retrieval](https://arxiv.org/abs/2305.13915).
## Usage
### API call
One can call the Hugging's Inference Endpoints API directly: (need your access token from https://huggingface.co/settings/tokens and the loading takes around 6 minutes)
```python
import requests
import time
API_URL = "https://api-inference.huggingface.co/models/kwang2049/long-coref"
headers = {"Authorization": "Bearer ${YOUR_HUGGINGFACE_ACCESS_TOKEN}"}
def query(payload):
while True:
response = requests.post(API_URL, headers=headers, json=payload)
if response.status_code == 503:
time.sleep(5)
print(response.json()["error"])
continue
elif response.status_code == 200:
return response.json()
else:
error_message = f"{response.status_code}: {response.json['error']}."
raise requests.HTTPError(error_message)
doc = [
"The Half Moon is a public house and music venue in Putney, London. It is one of the city's longest running live music venues, and has hosted live music every night since 1963.",
"The pub is on the south side of the Lower Richmond road, in the London Borough of Wandsworth."
]
PARAGRAPH_DELIMITER = "\n\n"
output = query(
{
"inputs": PARAGRAPH_DELIMITER.join(doc),
}
)
print(output)
# {
# 'pargraph_sentences': ...,
# 'top_spans': ...,
# 'antecedents': ...
# }
```
### Local run
One can also run the code of the repo on a local machine:
```bash
# Clone the repo
git lfs install
git clone https://huggingface.co/kwang2049/long-coref
cd long-coref && pip install -r requirements.txt && python local_run.py
```
## Evalution
The evaluation results on OntoNotesv5 are:
| Model | Precision | Recall | F1 | Input Length|
| --- | --- | --- | --- | --- |
| AllenNLP's original implementation | 79.2 | 78.4 | 78.8 | <= 2K words|
| This modification | 78.9 | 67.0 | 72.4| <= 40K words |
## Citation
If you use the repo, feel free to cite our publication [DAPR: A Benchmark on Document-Aware Passage Retrieval](https://arxiv.org/abs/2305.13915):
```bibtex
@article{wang2023dapr,
title = "DAPR: A Benchmark on Document-Aware Passage Retrieval",
author = "Kexin Wang and Nils Reimers and Iryna Gurevych",
journal= "arXiv preprint arXiv:2305.13915",
year = "2023",
url = "https://arxiv.org/abs/2305.13915",
}
``` | {"license": "apache-2.0", "library_name": "generic", "tags": ["feature-extraction", "endpoints-template"]} | feature-extraction | kwang2049/long-coref | [
"generic",
"feature-extraction",
"endpoints-template",
"arxiv:2305.13915",
"license:apache-2.0",
"region:us"
] | 2024-02-07T14:51:17+00:00 | [
"2305.13915"
] | [] | TAGS
#generic #feature-extraction #endpoints-template #arxiv-2305.13915 #license-apache-2.0 #region-us
| Coreference Resolution for Long Documents
=========================================
Modified coreference resolution model from BERT for Coreference Resolution: Baselines and Analysis for handling long documents (~40K words) efficiently (500K words/s on a NVIDIA Tesla V100). The checkpoint is based on AllenNLP's coref-spanbert-large-2021.03.10. This modified model was used in DAPR: A Benchmark on Document-Aware Passage Retrieval.
Usage
-----
### API call
One can call the Hugging's Inference Endpoints API directly: (need your access token from URL and the loading takes around 6 minutes)
### Local run
One can also run the code of the repo on a local machine:
Evalution
---------
The evaluation results on OntoNotesv5 are:
If you use the repo, feel free to cite our publication DAPR: A Benchmark on Document-Aware Passage Retrieval:
| [
"### API call\n\n\nOne can call the Hugging's Inference Endpoints API directly: (need your access token from URL and the loading takes around 6 minutes)",
"### Local run\n\n\nOne can also run the code of the repo on a local machine:\n\n\nEvalution\n---------\n\n\nThe evaluation results on OntoNotesv5 are:\n\n\n\nIf you use the repo, feel free to cite our publication DAPR: A Benchmark on Document-Aware Passage Retrieval:"
] | [
"TAGS\n#generic #feature-extraction #endpoints-template #arxiv-2305.13915 #license-apache-2.0 #region-us \n",
"### API call\n\n\nOne can call the Hugging's Inference Endpoints API directly: (need your access token from URL and the loading takes around 6 minutes)",
"### Local run\n\n\nOne can also run the code of the repo on a local machine:\n\n\nEvalution\n---------\n\n\nThe evaluation results on OntoNotesv5 are:\n\n\n\nIf you use the repo, feel free to cite our publication DAPR: A Benchmark on Document-Aware Passage Retrieval:"
] | [
39,
39,
64
] | [
"passage: TAGS\n#generic #feature-extraction #endpoints-template #arxiv-2305.13915 #license-apache-2.0 #region-us \n### API call\n\n\nOne can call the Hugging's Inference Endpoints API directly: (need your access token from URL and the loading takes around 6 minutes)### Local run\n\n\nOne can also run the code of the repo on a local machine:\n\n\nEvalution\n---------\n\n\nThe evaluation results on OntoNotesv5 are:\n\n\n\nIf you use the repo, feel free to cite our publication DAPR: A Benchmark on Document-Aware Passage Retrieval:"
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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. -->
# result
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"tags": ["generated_from_trainer"], "base_model": "huawei-noah/TinyBERT_General_4L_312D", "model-index": [{"name": "result", "results": []}]} | question-answering | d3t0oo/nlpEx6 | [
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:huawei-noah/TinyBERT_General_4L_312D",
"endpoints_compatible",
"region:us"
] | 2024-02-07T14:51:50+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-huawei-noah/TinyBERT_General_4L_312D #endpoints_compatible #region-us
|
# result
This model is a fine-tuned version of huawei-noah/TinyBERT_General_4L_312D 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"# result\n\nThis model is a fine-tuned version of huawei-noah/TinyBERT_General_4L_312D 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: 3e-05\n- train_batch_size: 12\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- num_epochs: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-huawei-noah/TinyBERT_General_4L_312D #endpoints_compatible #region-us \n",
"# result\n\nThis model is a fine-tuned version of huawei-noah/TinyBERT_General_4L_312D 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: 3e-05\n- train_batch_size: 12\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- num_epochs: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
61,
39,
6,
12,
8,
3,
90,
4,
38
] | [
"passage: TAGS\n#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-huawei-noah/TinyBERT_General_4L_312D #endpoints_compatible #region-us \n# result\n\nThis model is a fine-tuned version of huawei-noah/TinyBERT_General_4L_312D 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: 3e-05\n- train_batch_size: 12\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- num_epochs: 2.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
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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. -->
# LexFerrinson/FirulaiModel
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0006
- Validation Loss: 0.0697
- Train Precision: 0.9062
- Train Recall: 1.0
- Train F1: 0.9508
- Train Accuracy: 0.9905
- Epoch: 2
## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 600, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.0618 | 0.0597 | 0.9062 | 1.0 | 0.9508 | 0.9905 | 0 |
| 0.0008 | 0.0676 | 0.9062 | 1.0 | 0.9508 | 0.9905 | 1 |
| 0.0006 | 0.0697 | 0.9062 | 1.0 | 0.9508 | 0.9905 | 2 |
### 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": "distilbert-base-uncased", "model-index": [{"name": "LexFerrinson/FirulaiModel", "results": []}]} | token-classification | LexFerrinson/FirulaiModel | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T14:53:02+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| LexFerrinson/FirulaiModel
=========================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0006
* Validation Loss: 0.0697
* Train Precision: 0.9062
* Train Recall: 1.0
* Train F1: 0.9508
* Train Accuracy: 0.9905
* Epoch: 2
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': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 600, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32
### 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': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 600, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### 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 #distilbert #token-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #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': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 600, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### 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"
] | [
71,
226,
4,
31
] | [
"passage: TAGS\n#transformers #tf #distilbert #token-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #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': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 600, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32### 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"
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null | null | null | dave bautista man = trigger word | {} | null | freecryptobasics/Sam | [
"region:us"
] | 2024-02-07T14:56:36+00:00 | [] | [] | TAGS
#region-us
| dave bautista man = trigger word | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#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
### Model Description
<!-- Provide a longer summary of what this model is. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "HuggingFaceH4/zephyr-7b-beta"} | null | NatLibFi/zephyr-7b-meteor-ludwig | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:HuggingFaceH4/zephyr-7b-beta",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-HuggingFaceH4/zephyr-7b-beta #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Shared by [optional]:
- 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
[optional]
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APA:
## 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. -->
# speecht5_tts_commonvoice_fa
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4596
## 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: 500
- training_steps: 6000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6004 | 2.18 | 500 | 0.5508 |
| 0.5764 | 4.37 | 1000 | 0.5257 |
| 0.5511 | 6.55 | 1500 | 0.5164 |
| 0.5233 | 8.73 | 2000 | 0.5100 |
| 0.526 | 10.92 | 2500 | 0.4879 |
| 0.5016 | 13.1 | 3000 | 0.4784 |
| 0.4888 | 15.28 | 3500 | 0.4697 |
| 0.4796 | 17.47 | 4000 | 0.4664 |
| 0.4736 | 19.65 | 4500 | 0.4579 |
| 0.4501 | 21.83 | 5000 | 0.4601 |
| 0.4554 | 24.02 | 5500 | 0.4572 |
| 0.4539 | 26.2 | 6000 | 0.4596 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["common_voice_13_0"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "speecht5_tts_commonvoice_fa", "results": []}]} | text-to-audio | erfanasgari21/speecht5_tts_commonvoice_fa | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-07T15:03:54+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-common_voice_13_0 #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
| speecht5\_tts\_commonvoice\_fa
==============================
This model is a fine-tuned version of microsoft/speecht5\_tts on the common\_voice\_13\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4596
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: 500
* training\_steps: 6000
* mixed\_precision\_training: Native AMP
### 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: 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: 500\n* training\\_steps: 6000\n* mixed\\_precision\\_training: Native AMP",
"### 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 #speecht5 #text-to-audio #generated_from_trainer #dataset-common_voice_13_0 #base_model-microsoft/speecht5_tts #license-mit #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: 500\n* training\\_steps: 6000\n* mixed\\_precision\\_training: Native AMP",
"### 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"
] | [
77,
157,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-common_voice_13_0 #base_model-microsoft/speecht5_tts #license-mit #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: 500\n* training\\_steps: 6000\n* mixed\\_precision\\_training: Native AMP### 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 |
<!-- 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. -->
# robust_llm_pythia-tt-14m-mz-v0
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_pythia-tt-14m-mz-v0", "results": []}]} | text-classification | AlignmentResearch/robust_llm_pythia-tt-14m-mz-v0 | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:09:13+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-14m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# robust_llm_pythia-tt-14m-mz-v0
This model is a fine-tuned version of EleutherAI/pythia-14m 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# robust_llm_pythia-tt-14m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-14m 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: 1e-05\n- train_batch_size: 8\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-14m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# robust_llm_pythia-tt-14m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-14m 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: 1e-05\n- train_batch_size: 8\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
75,
45,
6,
12,
8,
3,
90,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-14m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# robust_llm_pythia-tt-14m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-14m 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: 1e-05\n- train_batch_size: 8\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: 1### Training results### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers | Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | {"license": "cc-by-2.0"} | text-generation | LoneStriker/Senku-70B-Full-4.65bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:10:09+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
60
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# robust_llm_pythia-tt-31m-mz-v0
This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-tt-31m-mz-v0", "results": []}]} | text-classification | AlignmentResearch/robust_llm_pythia-tt-31m-mz-v0 | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-31m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:10:13+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-31m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# robust_llm_pythia-tt-31m-mz-v0
This model is a fine-tuned version of EleutherAI/pythia-31m 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# robust_llm_pythia-tt-31m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-31m 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: 1e-05\n- train_batch_size: 8\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-31m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# robust_llm_pythia-tt-31m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-31m 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: 1e-05\n- train_batch_size: 8\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
75,
45,
6,
12,
8,
3,
90,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-31m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# robust_llm_pythia-tt-31m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-31m 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: 1e-05\n- train_batch_size: 8\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: 1### Training results### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\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. -->
# robust_llm_pythia-tt-70m-mz-v0
This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-70m-deduped", "model-index": [{"name": "robust_llm_pythia-tt-70m-mz-v0", "results": []}]} | text-classification | AlignmentResearch/robust_llm_pythia-tt-70m-mz-v0 | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m-deduped",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:11:57+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-70m-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# robust_llm_pythia-tt-70m-mz-v0
This model is a fine-tuned version of EleutherAI/pythia-70m-deduped 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# robust_llm_pythia-tt-70m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-70m-deduped 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: 1e-05\n- train_batch_size: 8\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-70m-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# robust_llm_pythia-tt-70m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-70m-deduped 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: 1e-05\n- train_batch_size: 8\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
87,
49,
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30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-70m-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# robust_llm_pythia-tt-70m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-70m-deduped 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: 1e-05\n- train_batch_size: 8\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: 1### Training results### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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] |
null | null | transformers | ## Run summary:
train/epoch 13.91 <br/>
train/global_step 40 <br/>
train/learning_rate 0.0 <br/>
train/loss 0.2795 <br/>
train/total_flos 4134138886176768.0 <br/>
train/train_loss 1.33859 <br/>
train/train_runtime 1368.8841 <br/>
train/train_samples_per_second 10.344 <br/>
train/train_steps_per_second 0.029 <br/> | {"library_name": "transformers", "tags": []} | text-generation | ambrosfitz/tinyllama-history-chat_v0.2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:12:03+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Run summary:
train/epoch 13.91 <br/>
train/global_step 40 <br/>
train/learning_rate 0.0 <br/>
train/loss 0.2795 <br/>
train/total_flos 4134138886176768.0 <br/>
train/train_loss 1.33859 <br/>
train/train_runtime 1368.8841 <br/>
train/train_samples_per_second 10.344 <br/>
train/train_steps_per_second 0.029 <br/> | [
"## Run summary:\n\ntrain/epoch\t13.91 <br/>\ntrain/global_step\t40 <br/>\ntrain/learning_rate\t0.0 <br/>\ntrain/loss\t0.2795 <br/>\ntrain/total_flos\t4134138886176768.0 <br/>\ntrain/train_loss\t1.33859 <br/>\ntrain/train_runtime\t1368.8841 <br/>\ntrain/train_samples_per_second\t10.344 <br/>\ntrain/train_steps_per_second\t0.029 <br/>"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Run summary:\n\ntrain/epoch\t13.91 <br/>\ntrain/global_step\t40 <br/>\ntrain/learning_rate\t0.0 <br/>\ntrain/loss\t0.2795 <br/>\ntrain/total_flos\t4134138886176768.0 <br/>\ntrain/train_loss\t1.33859 <br/>\ntrain/train_runtime\t1368.8841 <br/>\ntrain/train_samples_per_second\t10.344 <br/>\ntrain/train_steps_per_second\t0.029 <br/>"
] | [
51,
127
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Run summary:\n\ntrain/epoch\t13.91 <br/>\ntrain/global_step\t40 <br/>\ntrain/learning_rate\t0.0 <br/>\ntrain/loss\t0.2795 <br/>\ntrain/total_flos\t4134138886176768.0 <br/>\ntrain/train_loss\t1.33859 <br/>\ntrain/train_runtime\t1368.8841 <br/>\ntrain/train_samples_per_second\t10.344 <br/>\ntrain/train_steps_per_second\t0.029 <br/>"
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null | null | llama.cpp | <!-- markdownlint-disable MD041 -->
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# DareVox 7B - AWQ
- Model creator: [Zain ul Abideen](https://huggingface.co/abideen)
- Original model: [DareVox 7B](https://huggingface.co/abideen/DareVox-7B)
<!-- description start -->
## Description
This repo contains AWQ model files for [Zain ul Abideen's DareVox 7B](https://huggingface.co/abideen/DareVox-7B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/DareVox-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/DareVox-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/DareVox-7B-GGUF)
* [Zain ul Abideen's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/abideen/DareVox-7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/DareVox-7B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/DareVox-7B-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `DareVox-7B-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/DareVox-7B-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/DareVox-7B-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/DareVox-7B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/DareVox-7B-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Zain ul Abideen's DareVox 7B
# DareVox-7B
DareVox-7B is a merge of the following models:
* [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
* [abacusai/Slerp-CM-mist-dpo](https://huggingface.co/abacusai/Slerp-CM-mist-dpo)
* [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# No parameters necessary for base model
- model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
density: 0.53
weight: 0.4
- model: abacusai/Slerp-CM-mist-dpo
parameters:
density: 0.53
weight: 0.3
- model: berkeley-nest/Starling-LM-7B-alpha
parameters:
density: 0.5
weight: 0.4
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "abideen/DareVox-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
| {"license": "apache-2.0", "library_name": "llama.cpp", "tags": ["merge", "mergekit", "lazymergekit", "teknium/OpenHermes-2.5-Mistral-7B", "abacusai/Slerp-CM-mist-dpo", "berkeley-nest/Starling-LM-7B-alpha"], "model_name": "DareVox 7B", "base_model": "abideen/DareVox-7B", "inference": false, "model_creator": "Zain ul Abideen", "model_type": "mistral", "prompt_template": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:\n", "quantized_by": "TheBloke"} | null | osanseviero/DareVox-7B-AWQ | [
"llama.cpp",
"safetensors",
"mistral",
"merge",
"mergekit",
"lazymergekit",
"teknium/OpenHermes-2.5-Mistral-7B",
"abacusai/Slerp-CM-mist-dpo",
"berkeley-nest/Starling-LM-7B-alpha",
"base_model:abideen/DareVox-7B",
"license:apache-2.0",
"4-bit",
"region:us"
] | 2024-02-07T15:13:05+00:00 | [] | [] | TAGS
#llama.cpp #safetensors #mistral #merge #mergekit #lazymergekit #teknium/OpenHermes-2.5-Mistral-7B #abacusai/Slerp-CM-mist-dpo #berkeley-nest/Starling-LM-7B-alpha #base_model-abideen/DareVox-7B #license-apache-2.0 #4-bit #region-us
|
[[TheBloke's LLM work is generously supported by a grant from [andreessen horowitz (a16z)](URL)](URL to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style=)](URL & support: TheBloke's Discord server</a></p>
</div>
<div style=)
---
DareVox 7B - AWQ
================
* Model creator: Zain ul Abideen
* Original model: DareVox 7B
Description
-----------
This repo contains AWQ model files for Zain ul Abideen's DareVox 7B.
These files were quantised using hardware kindly provided by Massed Compute.
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
* Text Generation Webui - using Loader: AutoAWQ
* vLLM - version 0.2.2 or later for support for all model types.
* Hugging Face Text Generation Inference (TGI)
* Transformers version 4.35.0 and later, from any code or client that supports Transformers
* AutoAWQ - for use from Python code
Repositories available
----------------------
* AWQ model(s) for GPU inference.
* GPTQ models for GPU inference, with multiple quantisation parameter options.
* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
* Zain ul Abideen's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Alpaca
-----------------------
Provided files, and AWQ parameters
----------------------------------
I currently release 128g GEMM models only. The addition of group\_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
How to easily download and use this model in text-generation-webui
------------------------------------------------------------------
Please make sure you're using the latest version of text-generation-webui.
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the Model tab.
2. Under Download custom model or LoRA, enter 'TheBloke/DareVox-7B-AWQ'.
3. Click Download.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to Model.
6. In the Model dropdown, choose the model you just downloaded: 'DareVox-7B-AWQ'
7. Select Loader: AutoAWQ.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
10. Once you're ready, click the Text Generation tab and enter a prompt to get started!
Multi-user inference server: vLLM
---------------------------------
Documentation on installing and using vLLM can be found here.
* Please ensure you are using vLLM version 0.2 or later.
* When using vLLM as a server, pass the '--quantization awq' parameter.
For example:
* When using vLLM from Python code, again set 'quantization=awq'.
For example:
Multi-user inference server: Hugging Face Text Generation Inference (TGI)
-------------------------------------------------------------------------
Use TGI version 1.1.0 or later. The official Docker container is: 'URL
Example Docker parameters:
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
Inference from Python code using Transformers
---------------------------------------------
### Install the necessary packages
* Requires: Transformers 4.35.0 or later.
* Requires: AutoAWQ 0.1.6 or later.
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
If you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:
### Transformers example code (requires Transformers 4.35.0 and later)
Compatibility
-------------
The files provided are tested to work with:
* text-generation-webui using 'Loader: AutoAWQ'.
* vLLM version 0.2.0 and later.
* Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.
* Transformers version 4.35.0 and later.
* AutoAWQ version 0.1.1 and later.
Discord
-------
For further support, and discussions on these models and AI in general, join us at:
TheBloke AI's Discord server
Thanks, and how to contribute
-----------------------------
Thanks to the URL team!
Thanks to Clay from URL!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: URL
* Ko-Fi: URL
Special thanks to: Aemon Algiz.
Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, URL, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S\_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Zain ul Abideen's DareVox 7B
=================================================
DareVox-7B
==========
DareVox-7B is a merge of the following models:
* teknium/OpenHermes-2.5-Mistral-7B
* abacusai/Slerp-CM-mist-dpo
* berkeley-nest/Starling-LM-7B-alpha
Configuration
-------------
Usage
-----
| [
"### About AWQ\n\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\n\nAWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.\n\n\nIt is supported by:\n\n\n* Text Generation Webui - using Loader: AutoAWQ\n* vLLM - version 0.2.2 or later for support for all model types.\n* Hugging Face Text Generation Inference (TGI)\n* Transformers version 4.35.0 and later, from any code or client that supports Transformers\n* AutoAWQ - for use from Python code\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Zain ul Abideen's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: Alpaca\n-----------------------\n\n\nProvided files, and AWQ parameters\n----------------------------------\n\n\nI currently release 128g GEMM models only. The addition of group\\_size 32 models, and GEMV kernel models, is being actively considered.\n\n\nModels are released as sharded safetensors files.\n\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/DareVox-7B-AWQ'.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'DareVox-7B-AWQ'\n7. Select Loader: AutoAWQ.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n10. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nMulti-user inference server: vLLM\n---------------------------------\n\n\nDocumentation on installing and using vLLM can be found here.\n\n\n* Please ensure you are using vLLM version 0.2 or later.\n* When using vLLM as a server, pass the '--quantization awq' parameter.\n\n\nFor example:\n\n\n* When using vLLM from Python code, again set 'quantization=awq'.\n\n\nFor example:\n\n\nMulti-user inference server: Hugging Face Text Generation Inference (TGI)\n-------------------------------------------------------------------------\n\n\nUse TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nInference from Python code using Transformers\n---------------------------------------------",
"### Install the necessary packages\n\n\n* Requires: Transformers 4.35.0 or later.\n* Requires: AutoAWQ 0.1.6 or later.\n\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n\nIf you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:",
"### Transformers example code (requires Transformers 4.35.0 and later)\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with:\n\n\n* text-generation-webui using 'Loader: AutoAWQ'.\n* vLLM version 0.2.0 and later.\n* Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.\n* Transformers version 4.35.0 and later.\n* AutoAWQ version 0.1.1 and later.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, URL, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S\\_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Zain ul Abideen's DareVox 7B\n=================================================\n\n\nDareVox-7B\n==========\n\n\nDareVox-7B is a merge of the following models:\n\n\n* teknium/OpenHermes-2.5-Mistral-7B\n* abacusai/Slerp-CM-mist-dpo\n* berkeley-nest/Starling-LM-7B-alpha\n\n\nConfiguration\n-------------\n\n\nUsage\n-----"
] | [
"TAGS\n#llama.cpp #safetensors #mistral #merge #mergekit #lazymergekit #teknium/OpenHermes-2.5-Mistral-7B #abacusai/Slerp-CM-mist-dpo #berkeley-nest/Starling-LM-7B-alpha #base_model-abideen/DareVox-7B #license-apache-2.0 #4-bit #region-us \n",
"### About AWQ\n\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\n\nAWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.\n\n\nIt is supported by:\n\n\n* Text Generation Webui - using Loader: AutoAWQ\n* vLLM - version 0.2.2 or later for support for all model types.\n* Hugging Face Text Generation Inference (TGI)\n* Transformers version 4.35.0 and later, from any code or client that supports Transformers\n* AutoAWQ - for use from Python code\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Zain ul Abideen's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: Alpaca\n-----------------------\n\n\nProvided files, and AWQ parameters\n----------------------------------\n\n\nI currently release 128g GEMM models only. The addition of group\\_size 32 models, and GEMV kernel models, is being actively considered.\n\n\nModels are released as sharded safetensors files.\n\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/DareVox-7B-AWQ'.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'DareVox-7B-AWQ'\n7. Select Loader: AutoAWQ.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n10. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nMulti-user inference server: vLLM\n---------------------------------\n\n\nDocumentation on installing and using vLLM can be found here.\n\n\n* Please ensure you are using vLLM version 0.2 or later.\n* When using vLLM as a server, pass the '--quantization awq' parameter.\n\n\nFor example:\n\n\n* When using vLLM from Python code, again set 'quantization=awq'.\n\n\nFor example:\n\n\nMulti-user inference server: Hugging Face Text Generation Inference (TGI)\n-------------------------------------------------------------------------\n\n\nUse TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nInference from Python code using Transformers\n---------------------------------------------",
"### Install the necessary packages\n\n\n* Requires: Transformers 4.35.0 or later.\n* Requires: AutoAWQ 0.1.6 or later.\n\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n\nIf you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:",
"### Transformers example code (requires Transformers 4.35.0 and later)\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with:\n\n\n* text-generation-webui using 'Loader: AutoAWQ'.\n* vLLM version 0.2.0 and later.\n* Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.\n* Transformers version 4.35.0 and later.\n* AutoAWQ version 0.1.1 and later.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, URL, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S\\_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Zain ul Abideen's DareVox 7B\n=================================================\n\n\nDareVox-7B\n==========\n\n\nDareVox-7B is a merge of the following models:\n\n\n* teknium/OpenHermes-2.5-Mistral-7B\n* abacusai/Slerp-CM-mist-dpo\n* berkeley-nest/Starling-LM-7B-alpha\n\n\nConfiguration\n-------------\n\n\nUsage\n-----"
] | [
105,
767,
111,
901
] | [
"passage: TAGS\n#llama.cpp #safetensors #mistral #merge #mergekit #lazymergekit #teknium/OpenHermes-2.5-Mistral-7B #abacusai/Slerp-CM-mist-dpo #berkeley-nest/Starling-LM-7B-alpha #base_model-abideen/DareVox-7B #license-apache-2.0 #4-bit #region-us \n",
"passage: ### About AWQ\n\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\n\nAWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.\n\n\nIt is supported by:\n\n\n* Text Generation Webui - using Loader: AutoAWQ\n* vLLM - version 0.2.2 or later for support for all model types.\n* Hugging Face Text Generation Inference (TGI)\n* Transformers version 4.35.0 and later, from any code or client that supports Transformers\n* AutoAWQ - for use from Python code\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Zain ul Abideen's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: Alpaca\n-----------------------\n\n\nProvided files, and AWQ parameters\n----------------------------------\n\n\nI currently release 128g GEMM models only. The addition of group\\_size 32 models, and GEMV kernel models, is being actively considered.\n\n\nModels are released as sharded safetensors files.\n\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/DareVox-7B-AWQ'.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'DareVox-7B-AWQ'\n7. Select Loader: AutoAWQ.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n10. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nMulti-user inference server: vLLM\n---------------------------------\n\n\nDocumentation on installing and using vLLM can be found here.\n\n\n* Please ensure you are using vLLM version 0.2 or later.\n* When using vLLM as a server, pass the '--quantization awq' parameter.\n\n\nFor example:\n\n\n* When using vLLM from Python code, again set 'quantization=awq'.\n\n\nFor example:\n\n\nMulti-user inference server: Hugging Face Text Generation Inference (TGI)\n-------------------------------------------------------------------------\n\n\nUse TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nInference from Python code using Transformers\n---------------------------------------------### Install the necessary packages\n\n\n* Requires: Transformers 4.35.0 or later.\n* Requires: AutoAWQ 0.1.6 or later.\n\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n\nIf you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:"
] | [
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null | null | null | # Applio
Welcome to **Applio**, the ultimate voice cloning tool meticulously optimized for unrivaled power, modularity, and a user-friendly experience.
[](https://huggingface.co/IAHispano/applio/tree/main/Applio%20V3%20Precompiled)
[](https://discord.gg/iahispano)
[](https://github.com/IAHispano/Applio-RVC-Fork/issues)
[](https://colab.research.google.com/github/iahispano/applio/blob/master/assets/Applio.ipynb)
## Content Table
- [**Installation**](#installation)
- [Windows](#windows)
- [Linux](#linux)
- [Using Makefile](#using-makefile-for-platforms-such-as-paperspace)
- [**Usage**](#usage)
- [Windows](#windows-1)
- [Linux](#linux-1)
- [Using Makefile](#using-makefile-for-platforms-such-as-paperspace-1)
- [**Repository Enhancements**](#repository-enhancements)
- [**Credits**](#credits)
- [Contributors](#contributors)
## Installation
Download the latest version from [GitHub Releases](https://github.com/IAHispano/Applio-RVC-Fork/releases) or use [Precompiled Versions](https://huggingface.co/IAHispano/applio/tree/main/Applio%20V3%20Precompiled).
### Windows
```bash
./run-install.bat
```
### Linux
```bash
chmod +x run-install.sh
./run-install.sh
```
### Using Makefile (for platforms such as [Paperspace](https://www.paperspace.com/))
```
make run-install
```
## Usage
Visit [Applio Documentation](https://docs.applio.org/) for a detailed UI usage explanation.
### Windows
```bash
./run-applio.bat
```
### Linux
```bash
chmod +x run-applio.sh
./run-applio.sh
```
### Using Makefile (for platforms such as [Paperspace](https://www.paperspace.com/))
```
make run-applio
```
## Repository Enhancements
This repository has undergone significant improvements to enhance its functionality and maintainability:
- **Code Modularization:** The codebase has been restructured to follow a modular approach. This ensures better organization, readability, and ease of maintenance.
- **Hop Length Implementation:** Special thanks to [@Mangio621](https://github.com/Mangio621/Mangio-RVC-Fork) for introducing hop length implementation. This enhancement enhances the efficiency and performance on Crepe (previously known as Mangio-Crepe).
- **Translations to +30 Languages:** The repository now supports translations in over 30 languages, making it more accessible to a global audience.
- **Cross-Platform Compatibility:** With multiplatform compatibility, this repository can seamlessly operate across various platforms, providing a consistent experience to users.
- **Optimized Requirements:** The project's requirements have been fine-tuned for improved performance and resource utilization.
- **Simple Installation:** The installation process has been streamlined, ensuring a straightforward and user-friendly experience for setup.
These enhancements contribute to a more robust and scalable codebase, making the repository more accessible for contributors and users alike.
## Contributions
- **Backend Contributions:** If you want to contribute to the backend, make your pull requests [here](https://github.com/blaise-tk/RVC_CLI).
- **Frontend Contributions:** For interface or script-related contributions, feel free to contribute to this repository.
We appreciate all contributions ❤️
## Planned Features
- Implement: Support for Apple Devices ([Issue Link](https://github.com/pytorch/pytorch/issues/77764))
- Implement: rmvpe_gpu
- Implement: Theme selector, RPC toggle & version checker
- Implement: Overtraining detector
- Implement: Autotune
- Implement: Training stop
- Fix: Model fusion
## Credits
- [VITS](https://github.com/jaywalnut310/vits) by jaywalnut310
- [Retrieval-based-Voice-Conversion-WebUI](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) by RVC-Project
- [Mangio-RVC-Fork](https://github.com/Mangio621/Mangio-RVC-Fork) by Mangio621
- [Mangio-RVC-Tweaks](https://github.com/alexlnkp/Mangio-RVC-Tweaks) by alexlnkp
- [RVG_tts](https://github.com/Foxify52/RVG_tts) by Foxify52
- [RMVPE](https://github.com/Dream-High/RMVPE) by Dream-High
- [ContentVec](https://github.com/auspicious3000/contentvec/) by auspicious3000
- [HIFIGAN](https://github.com/jik876/hifi-gan) by jik876
- [Gradio](https://github.com/gradio-app/gradio) by gradio-app
- [FFmpeg](https://github.com/FFmpeg/FFmpeg) by FFmpeg
- [audio-slicer](https://github.com/openvpi/audio-slicer) by openvpi
- [Ilaria-Audio-Analyzer](https://github.com/TheStingerX/Ilaria-Audio-Analyzer) by TheStingerX
- [gradio-screen-recorder](https://huggingface.co/spaces/gstaff/gradio-screen-recorder) by gstaff
- [RVC_CLI](https://github.com/blaise-tk/RVC_CLI) by blaise-tk
### Contributors
<a href="https://github.com/IAHispano/Applio/graphs/contributors" target="_blank">
<img src="https://contrib.rocks/image?repo=IAHispano/Applio" />
</a>
| {} | null | kanoyo/Kanoyo | [
"region:us"
] | 2024-02-07T15:14:27+00:00 | [] | [] | TAGS
#region-us
| # Applio
Welcome to Applio, the ultimate voice cloning tool meticulously optimized for unrivaled power, modularity, and a user-friendly experience.
## Usage
Visit Applio Documentation for a detailed UI usage explanation.
### Windows
### Linux
### Using Makefile (for platforms such as Paperspace)
## Repository Enhancements
This repository has undergone significant improvements to enhance its functionality and maintainability:
- Code Modularization: The codebase has been restructured to follow a modular approach. This ensures better organization, readability, and ease of maintenance.
- Hop Length Implementation: Special thanks to @Mangio621 for introducing hop length implementation. This enhancement enhances the efficiency and performance on Crepe (previously known as Mangio-Crepe).
- Translations to +30 Languages: The repository now supports translations in over 30 languages, making it more accessible to a global audience.
- Cross-Platform Compatibility: With multiplatform compatibility, this repository can seamlessly operate across various platforms, providing a consistent experience to users.
- Optimized Requirements: The project's requirements have been fine-tuned for improved performance and resource utilization.
- Simple Installation: The installation process has been streamlined, ensuring a straightforward and user-friendly experience for setup.
These enhancements contribute to a more robust and scalable codebase, making the repository more accessible for contributors and users alike.
## Contributions
- Backend Contributions: If you want to contribute to the backend, make your pull requests here.
- Frontend Contributions: For interface or script-related contributions, feel free to contribute to this repository.
We appreciate all contributions ️
## Planned Features
- Implement: Support for Apple Devices (Issue Link)
- Implement: rmvpe_gpu
- Implement: Theme selector, RPC toggle & version checker
- Implement: Overtraining detector
- Implement: Autotune
- Implement: Training stop
- Fix: Model fusion
## Credits
- VITS by jaywalnut310
- Retrieval-based-Voice-Conversion-WebUI by RVC-Project
- Mangio-RVC-Fork by Mangio621
- Mangio-RVC-Tweaks by alexlnkp
- RVG_tts by Foxify52
- RMVPE by Dream-High
- ContentVec by auspicious3000
- HIFIGAN by jik876
- Gradio by gradio-app
- FFmpeg by FFmpeg
- audio-slicer by openvpi
- Ilaria-Audio-Analyzer by TheStingerX
- gradio-screen-recorder by gstaff
- RVC_CLI by blaise-tk
### Contributors
<a href="URL target="_blank">
<img src="URL />
</a>
| [
"# Applio\n\nWelcome to Applio, the ultimate voice cloning tool meticulously optimized for unrivaled power, modularity, and a user-friendly experience.\n\n![Precompiled Versions](URL\n!GitHub Release\n!GitHub Repo stars\n!GitHub forks\n![Support Discord](URL\n![Issues](URL\n![Open In Collab](URL",
"## Content Table\n- Installation\n - Windows\n - Linux\n - Using Makefile\n- Usage\n - Windows\n - Linux\n - Using Makefile\n- Repository Enhancements\n- Credits\n - Contributors",
"## Installation\nDownload the latest version from GitHub Releases or use Precompiled Versions.",
"### Windows",
"### Linux",
"### Using Makefile (for platforms such as Paperspace)",
"## Usage\nVisit Applio Documentation for a detailed UI usage explanation.",
"### Windows",
"### Linux",
"### Using Makefile (for platforms such as Paperspace)",
"## Repository Enhancements\n\nThis repository has undergone significant improvements to enhance its functionality and maintainability:\n\n- Code Modularization: The codebase has been restructured to follow a modular approach. This ensures better organization, readability, and ease of maintenance.\n- Hop Length Implementation: Special thanks to @Mangio621 for introducing hop length implementation. This enhancement enhances the efficiency and performance on Crepe (previously known as Mangio-Crepe).\n- Translations to +30 Languages: The repository now supports translations in over 30 languages, making it more accessible to a global audience.\n- Cross-Platform Compatibility: With multiplatform compatibility, this repository can seamlessly operate across various platforms, providing a consistent experience to users.\n- Optimized Requirements: The project's requirements have been fine-tuned for improved performance and resource utilization.\n- Simple Installation: The installation process has been streamlined, ensuring a straightforward and user-friendly experience for setup.\n\nThese enhancements contribute to a more robust and scalable codebase, making the repository more accessible for contributors and users alike.",
"## Contributions\n- Backend Contributions: If you want to contribute to the backend, make your pull requests here.\n- Frontend Contributions: For interface or script-related contributions, feel free to contribute to this repository.\n\nWe appreciate all contributions ️",
"## Planned Features\n- Implement: Support for Apple Devices (Issue Link)\n- Implement: rmvpe_gpu\n- Implement: Theme selector, RPC toggle & version checker\n- Implement: Overtraining detector\n- Implement: Autotune\n- Implement: Training stop\n- Fix: Model fusion",
"## Credits\n- VITS by jaywalnut310\n- Retrieval-based-Voice-Conversion-WebUI by RVC-Project\n- Mangio-RVC-Fork by Mangio621\n- Mangio-RVC-Tweaks by alexlnkp\n- RVG_tts by Foxify52\n- RMVPE by Dream-High\n- ContentVec by auspicious3000\n- HIFIGAN by jik876\n- Gradio by gradio-app\n- FFmpeg by FFmpeg\n- audio-slicer by openvpi\n- Ilaria-Audio-Analyzer by TheStingerX\n- gradio-screen-recorder by gstaff\n- RVC_CLI by blaise-tk",
"### Contributors\n<a href=\"URL target=\"_blank\">\n <img src=\"URL />\n</a>"
] | [
"TAGS\n#region-us \n",
"# Applio\n\nWelcome to Applio, the ultimate voice cloning tool meticulously optimized for unrivaled power, modularity, and a user-friendly experience.\n\n![Precompiled Versions](URL\n!GitHub Release\n!GitHub Repo stars\n!GitHub forks\n![Support Discord](URL\n![Issues](URL\n![Open In Collab](URL",
"## Content Table\n- Installation\n - Windows\n - Linux\n - Using Makefile\n- Usage\n - Windows\n - Linux\n - Using Makefile\n- Repository Enhancements\n- Credits\n - Contributors",
"## Installation\nDownload the latest version from GitHub Releases or use Precompiled Versions.",
"### Windows",
"### Linux",
"### Using Makefile (for platforms such as Paperspace)",
"## Usage\nVisit Applio Documentation for a detailed UI usage explanation.",
"### Windows",
"### Linux",
"### Using Makefile (for platforms such as Paperspace)",
"## Repository Enhancements\n\nThis repository has undergone significant improvements to enhance its functionality and maintainability:\n\n- Code Modularization: The codebase has been restructured to follow a modular approach. This ensures better organization, readability, and ease of maintenance.\n- Hop Length Implementation: Special thanks to @Mangio621 for introducing hop length implementation. This enhancement enhances the efficiency and performance on Crepe (previously known as Mangio-Crepe).\n- Translations to +30 Languages: The repository now supports translations in over 30 languages, making it more accessible to a global audience.\n- Cross-Platform Compatibility: With multiplatform compatibility, this repository can seamlessly operate across various platforms, providing a consistent experience to users.\n- Optimized Requirements: The project's requirements have been fine-tuned for improved performance and resource utilization.\n- Simple Installation: The installation process has been streamlined, ensuring a straightforward and user-friendly experience for setup.\n\nThese enhancements contribute to a more robust and scalable codebase, making the repository more accessible for contributors and users alike.",
"## Contributions\n- Backend Contributions: If you want to contribute to the backend, make your pull requests here.\n- Frontend Contributions: For interface or script-related contributions, feel free to contribute to this repository.\n\nWe appreciate all contributions ️",
"## Planned Features\n- Implement: Support for Apple Devices (Issue Link)\n- Implement: rmvpe_gpu\n- Implement: Theme selector, RPC toggle & version checker\n- Implement: Overtraining detector\n- Implement: Autotune\n- Implement: Training stop\n- Fix: Model fusion",
"## Credits\n- VITS by jaywalnut310\n- Retrieval-based-Voice-Conversion-WebUI by RVC-Project\n- Mangio-RVC-Fork by Mangio621\n- Mangio-RVC-Tweaks by alexlnkp\n- RVG_tts by Foxify52\n- RMVPE by Dream-High\n- ContentVec by auspicious3000\n- HIFIGAN by jik876\n- Gradio by gradio-app\n- FFmpeg by FFmpeg\n- audio-slicer by openvpi\n- Ilaria-Audio-Analyzer by TheStingerX\n- gradio-screen-recorder by gstaff\n- RVC_CLI by blaise-tk",
"### Contributors\n<a href=\"URL target=\"_blank\">\n <img src=\"URL />\n</a>"
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"passage: TAGS\n#region-us \n# Applio\n\nWelcome to Applio, the ultimate voice cloning tool meticulously optimized for unrivaled power, modularity, and a user-friendly experience.\n\n## Usage\nVisit Applio Documentation for a detailed UI usage explanation.### Windows### Linux### Using Makefile (for platforms such as Paperspace)## Repository Enhancements\n\nThis repository has undergone significant improvements to enhance its functionality and maintainability:\n\n- Code Modularization: The codebase has been restructured to follow a modular approach. This ensures better organization, readability, and ease of maintenance.\n- Hop Length Implementation: Special thanks to @Mangio621 for introducing hop length implementation. This enhancement enhances the efficiency and performance on Crepe (previously known as Mangio-Crepe).\n- Translations to +30 Languages: The repository now supports translations in over 30 languages, making it more accessible to a global audience.\n- Cross-Platform Compatibility: With multiplatform compatibility, this repository can seamlessly operate across various platforms, providing a consistent experience to users.\n- Optimized Requirements: The project's requirements have been fine-tuned for improved performance and resource utilization.\n- Simple Installation: The installation process has been streamlined, ensuring a straightforward and user-friendly experience for setup.\n\nThese enhancements contribute to a more robust and scalable codebase, making the repository more accessible for contributors and users alike."
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null | null | peft |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "google/flan-t5-base"} | null | HeydarS/flan-t5-base_peft_v24 | [
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|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- 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
[optional]
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APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
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null | null | generic |
# Fork of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) for a `zero-sho-image-classification` Inference endpoint.
This repository implements a `custom` task for `zero-shot-image-classification` for 🤗 Inference Endpoints. The code for the customized
pipeline is in the handler.py.
To use deploy this model an Inference Endpoint you have to select `Custom` as task to use the `handler.py` file.
### expected Request payload
```json
{
"image": encoded_image,
"parameters": {
"candidate_labels": "green, yellow, blue, white, silver"
}
}
```
`encoded_image` is a base64 encoded image. | {"library_name": "generic", "tags": ["vision", "zero-shot-image-classification", "endpoints-template"], "inference": false, "pipeline_tag": "zero-shot-image-classification", "base_model": "openai/clip-vit-large-patch14"} | zero-shot-image-classification | pimcore/IEP__zero-shot-image-classification | [
"generic",
"vision",
"zero-shot-image-classification",
"endpoints-template",
"base_model:openai/clip-vit-large-patch14",
"endpoints_compatible",
"region:us"
] | 2024-02-07T15:16:28+00:00 | [] | [] | TAGS
#generic #vision #zero-shot-image-classification #endpoints-template #base_model-openai/clip-vit-large-patch14 #endpoints_compatible #region-us
|
# Fork of openai/clip-vit-base-patch32 for a 'zero-sho-image-classification' Inference endpoint.
This repository implements a 'custom' task for 'zero-shot-image-classification' for Inference Endpoints. The code for the customized
pipeline is in the URL.
To use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.
### expected Request payload
'encoded_image' is a base64 encoded image. | [
"# Fork of openai/clip-vit-base-patch32 for a 'zero-sho-image-classification' Inference endpoint.\n\nThis repository implements a 'custom' task for 'zero-shot-image-classification' for Inference Endpoints. The code for the customized \npipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.",
"### expected Request payload\n\n\n\n'encoded_image' is a base64 encoded image."
] | [
"TAGS\n#generic #vision #zero-shot-image-classification #endpoints-template #base_model-openai/clip-vit-large-patch14 #endpoints_compatible #region-us \n",
"# Fork of openai/clip-vit-base-patch32 for a 'zero-sho-image-classification' Inference endpoint.\n\nThis repository implements a 'custom' task for 'zero-shot-image-classification' for Inference Endpoints. The code for the customized \npipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.",
"### expected Request payload\n\n\n\n'encoded_image' is a base64 encoded image."
] | [
53,
110,
22
] | [
"passage: TAGS\n#generic #vision #zero-shot-image-classification #endpoints-template #base_model-openai/clip-vit-large-patch14 #endpoints_compatible #region-us \n# Fork of openai/clip-vit-base-patch32 for a 'zero-sho-image-classification' Inference endpoint.\n\nThis repository implements a 'custom' task for 'zero-shot-image-classification' for Inference Endpoints. The code for the customized \npipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.### expected Request payload\n\n\n\n'encoded_image' is a base64 encoded image."
] | [
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] |
null | null | transformers | (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- https://huggingface.co/rombodawg/DeepMagic-Coder-7b
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
```yaml
models:
- model: deepseek-ai_deepseek-coder-6.7b-instruct
parameters:
weight: 1
- model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"} | text-generation | LoneStriker/DeepMagic-Coder-7b-Alt-3.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:16:46+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- URL
!image/jpeg
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
52
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
# ssarry-truthful-13B-slerp
ssarry-truthful-13B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
* [Sao10K/Mythical-Destroyer-V2-L2-13B](https://huggingface.co/Sao10K/Mythical-Destroyer-V2-L2-13B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: microsoft/Orca-2-13b
layer_range: [0, 32]
- model: Sao10K/Mythical-Destroyer-V2-L2-13B
layer_range: [0, 32]
merge_method: slerp
base_model: Sao10K/Mythical-Destroyer-V2-L2-13B
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
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ssaryssane/ssarry-truthful-13B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "microsoft/Orca-2-13b", "Sao10K/Mythical-Destroyer-V2-L2-13B"], "base_model": ["microsoft/Orca-2-13b", "Sao10K/Mythical-Destroyer-V2-L2-13B"]} | text-generation | ssaryssane/ssarry-truthful-13B-slerp | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"microsoft/Orca-2-13b",
"Sao10K/Mythical-Destroyer-V2-L2-13B",
"base_model:microsoft/Orca-2-13b",
"base_model:Sao10K/Mythical-Destroyer-V2-L2-13B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:17:28+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #microsoft/Orca-2-13b #Sao10K/Mythical-Destroyer-V2-L2-13B #base_model-microsoft/Orca-2-13b #base_model-Sao10K/Mythical-Destroyer-V2-L2-13B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ssarry-truthful-13B-slerp
ssarry-truthful-13B-slerp is a merge of the following models using LazyMergekit:
* microsoft/Orca-2-13b
* Sao10K/Mythical-Destroyer-V2-L2-13B
## Configuration
## Usage
| [
"# ssarry-truthful-13B-slerp\n\nssarry-truthful-13B-slerp is a merge of the following models using LazyMergekit:\n* microsoft/Orca-2-13b\n* Sao10K/Mythical-Destroyer-V2-L2-13B",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #microsoft/Orca-2-13b #Sao10K/Mythical-Destroyer-V2-L2-13B #base_model-microsoft/Orca-2-13b #base_model-Sao10K/Mythical-Destroyer-V2-L2-13B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ssarry-truthful-13B-slerp\n\nssarry-truthful-13B-slerp is a merge of the following models using LazyMergekit:\n* microsoft/Orca-2-13b\n* Sao10K/Mythical-Destroyer-V2-L2-13B",
"## Configuration",
"## Usage"
] | [
124,
67,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #microsoft/Orca-2-13b #Sao10K/Mythical-Destroyer-V2-L2-13B #base_model-microsoft/Orca-2-13b #base_model-Sao10K/Mythical-Destroyer-V2-L2-13B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ssarry-truthful-13B-slerp\n\nssarry-truthful-13B-slerp is a merge of the following models using LazyMergekit:\n* microsoft/Orca-2-13b\n* Sao10K/Mythical-Destroyer-V2-L2-13B## Configuration## Usage"
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null | null | transformers |
Prompt Example:
```
### System:
You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
### User:
How do you fine tune a large language model?
### Assistant:
``` | {"license": "other", "datasets": ["KnutJaegersberg/Deita-6k"], "license_name": "qwen", "license_link": "LICENSE"} | text-generation | KnutJaegersberg/Deita-4b | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"dataset:KnutJaegersberg/Deita-6k",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T15:18:42+00:00 | [] | [] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #dataset-KnutJaegersberg/Deita-6k #license-other #autotrain_compatible #endpoints_compatible #region-us
|
Prompt Example:
| [] | [
"TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #dataset-KnutJaegersberg/Deita-6k #license-other #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
63
] | [
"passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #dataset-KnutJaegersberg/Deita-6k #license-other #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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] |
null | null | transformers | # MysticFusion-13B-HQQ
This is [MysticFusion-13B model](https://huggingface.co/Walmart-the-bag/MysticFusion-13B) quantized to 4bit HQQ
# Usage
To run this quantization, you can use the following code.
```bash
pip install git+https://github.com/mobiusml/hqq/ transformers -U
```
```python
model_id = 'HQQHouse/MysticFusion-13B-HQQ'
from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = HQQModelForCausalLM.from_quantized(model_id)
# Prompt for inference
prompt = """
### Instruction: When will the sun explode?
### Response:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt').to('cuda')
output = model.generate(input_ids=input_ids, max_length=50, num_return_sequences=1, do_sample=True, top_k=50)
generated_sequence = tokenizer.decode(output[0].cuda(), skip_special_tokens=True)
print(generated_sequence)
```
# Read About HQQ
https://mobiusml.github.io/hqq_blog/
________________________
# Original Card
# MysticFusion-13B
YAML:
```
models:
- model: KoboldAI/LLaMA2-13B-Tiefighter
parameters:
weight: 0.3
- model: NeverSleep/Noromaid-13b-v0.1.1
parameters:
weight: 0.5
- model: lmsys/vicuna-13b-v1.5
parameters:
weight: 0.2
merge_method: linear
dtype: float16
```
## Usage:
This is meant to be story writing and basic instruction. More of story writing.
## Prompt Template:
### Alpaca
```
### Instruction:
### Response:
``` | {"license": "llama2", "inference": false} | text-generation | HQQHouse/MysticFusion-13B-HQQ | [
"transformers",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:18:55+00:00 | [] | [] | TAGS
#transformers #llama #text-generation #license-llama2 #autotrain_compatible #text-generation-inference #region-us
| # MysticFusion-13B-HQQ
This is MysticFusion-13B model quantized to 4bit HQQ
# Usage
To run this quantization, you can use the following code.
# Read About HQQ
URL
________________________
# Original Card
# MysticFusion-13B
!img1
YAML:
## Usage:
This is meant to be story writing and basic instruction. More of story writing.
## Prompt Template:
### Alpaca
| [
"# MysticFusion-13B-HQQ\nThis is MysticFusion-13B model quantized to 4bit HQQ",
"# Usage\nTo run this quantization, you can use the following code.",
"# Read About HQQ\nURL\n________________________",
"# Original Card",
"# MysticFusion-13B\n!img1\nYAML:",
"## Usage:\nThis is meant to be story writing and basic instruction. More of story writing.",
"## Prompt Template:",
"### Alpaca"
] | [
"TAGS\n#transformers #llama #text-generation #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n",
"# MysticFusion-13B-HQQ\nThis is MysticFusion-13B model quantized to 4bit HQQ",
"# Usage\nTo run this quantization, you can use the following code.",
"# Read About HQQ\nURL\n________________________",
"# Original Card",
"# MysticFusion-13B\n!img1\nYAML:",
"## Usage:\nThis is meant to be story writing and basic instruction. More of story writing.",
"## Prompt Template:",
"### Alpaca"
] | [
41,
26,
16,
9,
3,
14,
21,
6,
4
] | [
"passage: TAGS\n#transformers #llama #text-generation #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n# MysticFusion-13B-HQQ\nThis is MysticFusion-13B model quantized to 4bit HQQ# Usage\nTo run this quantization, you can use the following code.# Read About HQQ\nURL\n________________________# Original Card# MysticFusion-13B\n!img1\nYAML:## Usage:\nThis is meant to be story writing and basic instruction. More of story writing.## Prompt Template:### Alpaca"
] | [
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] |
null | null | transformers | (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- https://huggingface.co/rombodawg/DeepMagic-Coder-7b
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
```yaml
models:
- model: deepseek-ai_deepseek-coder-6.7b-instruct
parameters:
weight: 1
- model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"} | text-generation | LoneStriker/DeepMagic-Coder-7b-Alt-4.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:19:14+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- URL
!image/jpeg
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
52
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #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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| {"library_name": "transformers", "tags": []} | text-generation | Jayem-11/zephyr-7b-beta_assistant_v0.2_merged | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
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### Recommendations
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## 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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#### 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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## Technical Specifications [optional]
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## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | dkurzyk/phi2_DPO | [
"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 | transformers | (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- https://huggingface.co/rombodawg/DeepMagic-Coder-7b
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
```yaml
models:
- model: deepseek-ai_deepseek-coder-6.7b-instruct
parameters:
weight: 1
- model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"} | text-generation | LoneStriker/DeepMagic-Coder-7b-Alt-5.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:22:48+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- URL
!image/jpeg
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
52
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# robust_llm_pythia-tt-160m-mz-v0
This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-160m-deduped", "model-index": [{"name": "robust_llm_pythia-tt-160m-mz-v0", "results": []}]} | text-classification | AlignmentResearch/robust_llm_pythia-tt-160m-mz-v0 | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-160m-deduped",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:24:12+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-160m-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# robust_llm_pythia-tt-160m-mz-v0
This model is a fine-tuned version of EleutherAI/pythia-160m-deduped 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# robust_llm_pythia-tt-160m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-160m-deduped 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: 1e-05\n- train_batch_size: 8\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-160m-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# robust_llm_pythia-tt-160m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-160m-deduped 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: 1e-05\n- train_batch_size: 8\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
88,
51,
6,
12,
8,
3,
90,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-160m-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# robust_llm_pythia-tt-160m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-160m-deduped 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: 1e-05\n- train_batch_size: 8\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: 1### Training results### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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] |
null | null | transformers | (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- https://huggingface.co/rombodawg/DeepMagic-Coder-7b
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
```yaml
models:
- model: deepseek-ai_deepseek-coder-6.7b-instruct
parameters:
weight: 1
- model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"} | text-generation | LoneStriker/DeepMagic-Coder-7b-Alt-6.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T15:27:25+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| (Note: From short testing, this Alt version generated much better code)
Alternate version of DeepMagic-Coder-7b which can be found bellow.
- URL
!image/jpeg
This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.
Config can be found bellow:
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
52
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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