modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
linoyts/2000_ads_linoy_multi
|
linoyts
| 2024-01-26T14:40:36Z | 152 | 2 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-01-25T09:50:50Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: '<s0><s1> ad of a <s2><s3> woman wearing headphones'
output:
url:
"image_0.png"
- text: '<s0><s1> ad of a <s2><s3> woman wearing headphones'
output:
url:
"image_1.png"
- text: '<s0><s1> ad of a <s2><s3> woman wearing headphones'
output:
url:
"image_2.png"
- text: '<s0><s1> ad of a <s2><s3> woman wearing headphones'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: an ad in the style of <s0><s1> of a <s2><s3> woman
license: openrail++
---
# SDXL LoRA DreamBooth - linoyts/2000_ads_linoy_multi
<Gallery />
## Model description
### These are linoyts/2000_ads_linoy_multi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`2000_ads_linoy_multi.safetensors` here 💾](/linoyts/2000_ads_linoy_multi/blob/main/2000_ads_linoy_multi.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:2000_ads_linoy_multi:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`2000_ads_linoy_multi_emb.safetensors` here 💾](/linoyts/2000_ads_linoy_multi/blob/main/2000_ads_linoy_multi_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `2000_ads_linoy_multi_emb` to your prompt. For example, `an ad in the style of 2000_ads_linoy_multi_emb of a woman`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('linoyts/2000_ads_linoy_multi', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='linoyts/2000_ads_linoy_multi', filename='2000_ads_linoy_multi_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('<s0><s1> ad of a <s2><s3> woman wearing headphones').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
to trigger concept `T2K` → use `<s2><s3>` in your prompt
## Details
All [Files & versions](/linoyts/2000_ads_linoy_multi/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
anilguven/albert_tr_turkish_movie_reviews
|
anilguven
| 2024-01-26T14:36:24Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"movie",
"review",
"turkish",
"bert",
"sentiment",
"tr",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:19:52Z |
---
license: unknown
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- movie
- review
- turkish
- bert
- sentiment
---
### Model Info
This model was developed/finetuned for movie review task for the Turkish Language. This model was finetuned via the Turkish movie review dataset.
- LABEL_0: positive review
- LABEL_1: negative review
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
- **Paper:** https://dl.acm.org/doi/10.1145/3557892
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
- **Finetuned from model [optional]:** https://huggingface.co/loodos/albert-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- Accuracy: %91.71
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{10.1145/3557892,
author = {Guven, Zekeriya Anil},
title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3557892},
doi = {10.1145/3557892},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {dec},
articleno = {55},
numpages = {16},
keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
}*
**APA:**
*Guven, Z. A. (2022). The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-16.*
|
salmasally/esg-sally
|
salmasally
| 2024-01-26T14:35:38Z | 0 | 1 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T14:35:34Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# 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)
```
|
anilguven/distilbert_tr_turkish_movie_reviews
|
anilguven
| 2024-01-26T14:35:13Z | 93 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"movie",
"review",
"turkish",
"bert",
"sentiment",
"tr",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:21:57Z |
---
license: unknown
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- movie
- review
- turkish
- bert
- sentiment
---
### Model Info
This model was developed/finetuned for movie review task for the Turkish Language. This model was finetuned via the Turkish movie review dataset.
- LABEL_0: positive review
- LABEL_1: negative review
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
- **Paper:** https://dl.acm.org/doi/10.1145/3557892
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/distilbert-base-turkish-cased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- auprc = 0.9783265245768504
- auroc = 0.9786267839358107
- eval_loss = 0.332054428835344
- fn = 921
- fp = 1184
- mcc = 0.8424855995781335
- tn = 12166
- tp = 12429
- Accuracy: %92.00
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{10.1145/3557892,
author = {Guven, Zekeriya Anil},
title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3557892},
doi = {10.1145/3557892},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {dec},
articleno = {55},
numpages = {16},
keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
}*
**APA:**
*Guven, Z. A. (2022). The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-16.*
|
anilguven/albert_tr_turkish_hotel_reviews
|
anilguven
| 2024-01-26T14:29:37Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"hotel",
"review",
"turkish",
"sentiment",
"bert",
"tr",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:02:14Z |
---
license: unknown
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- hotel
- review
- turkish
- sentiment
- bert
---
### Model Info
This model was developed/finetuned for hotel review task for the Turkish Language. This model was finetuned via the Turkish hotel review dataset.
- LABEL_0: positive review
- LABEL_1: negative review
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
- **Paper:** https://dl.acm.org/doi/10.1145/3557892
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
- **Finetuned from model [optional]:** https://huggingface.co/loodos/ALBERT-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- auprc = 0.9967569041911343
- auroc = 0.9959888228299643
- eval_loss = 0.20936161253005187
- fn = 184
- fp = 11
- mcc = 0.934422786276581
- tn = 2889
- tp = 2716
- Accuracy: %96.63
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{10.1145/3557892,
author = {Guven, Zekeriya Anil},
title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3557892},
doi = {10.1145/3557892},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {dec},
articleno = {55},
numpages = {16},
keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
}*
**APA:**
*Guven, Z. A. (2022). The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-16.*
|
anilguven/distilbert_tr_turkish_hotel_reviews
|
anilguven
| 2024-01-26T14:28:26Z | 91 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"hotel",
"review",
"sentiment",
"turkish",
"bert",
"tr",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:03:32Z |
---
license: unknown
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- hotel
- review
- sentiment
- turkish
- bert
---
### Model Info
This model was developed/finetuned for hotel review task for the Turkish Language. This model was finetuned via the Turkish hotel review dataset.
- LABEL_0: positive review
- LABEL_1: negative review
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
- **Paper:** https://dl.acm.org/doi/10.1145/3557892
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/distilbert-base-turkish-cased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- auprc = 0.9980997402974433
- auroc = 0.9977912009512484
- eval_loss = 0.13716400672518045
- fn = 111
- fp = 24
- mcc = 0.9538776174134994
- tn = 2876
- tp = 2789
- Accuracy: %97.67
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{10.1145/3557892,
author = {Guven, Zekeriya Anil},
title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3557892},
doi = {10.1145/3557892},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {dec},
articleno = {55},
numpages = {16},
keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
}*
**APA:**
*Guven, Z. A. (2022). The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-16.*
|
anilguven/bert_tr_turkish_movie_reviews
|
anilguven
| 2024-01-26T14:24:46Z | 97 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"movie",
"review",
"sentiment",
"turkish",
"tr",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:10:18Z |
---
license: unknown
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- movie
- review
- sentiment
- turkish
- bert
---
### Model Info
This model was developed/finetuned for movie review task for the Turkish Language. This model was finetuned via the Turkish movie review dataset.
- LABEL_0: positive review
- LABEL_1: negative review
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
- **Paper:** https://dl.acm.org/doi/10.1145/3557892
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/bert-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- auprc = 0.9547155589592419
- auroc = 0.9567033960358541
- eval_loss = 0.4520341001172079
- fn = 1368
- fp = 1668
- mcc = 0.7727794159832003
- tn = 11682
- tp = 11982
- Accuracy: %92.11
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{10.1145/3557892,
author = {Guven, Zekeriya Anil},
title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3557892},
doi = {10.1145/3557892},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {dec},
articleno = {55},
numpages = {16},
keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
}*
**APA:**
*Guven, Z. A. (2022). The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-16.*
|
s3nh/latxa-13b-v1-GGUF
|
s3nh
| 2024-01-26T14:17:18Z | 2 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T12:51:00Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/HiTZ/latxa-13b-v1).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### inference
User: Tell me story about what is an quantization and what do we need to build.
Host: I am not going to tell you that. You should look for yourself on the internet. But don’t believe everything you find. Some people are wrong, others don’t know. So you have to be careful in your search.
User: OK.
Host: First you need to build a model for your application. What is a model? It’s what you do to understand how your system work.
User: I see.
Host: You will use this model later to write the software that drive the system. So it is very important that you get this right. The model should be as
# Original model card
|
jeevana/G8_mistral7b_qlora_1211_v01
|
jeevana
| 2024-01-26T14:14:47Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T13:47:09Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
|
guirnd/dqn-SpaceInvadersNoFrameskip-v4
|
guirnd
| 2024-01-26T14:12:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T14:11:58Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 534.00 +/- 171.45
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga guirnd -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga guirnd -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga guirnd
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
golesheed/whisper-non-native-children-0-dutch
|
golesheed
| 2024-01-26T14:00:53Z | 18 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"nl",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-26T11:31:03Z |
---
language:
- nl
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Large V2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large V2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3707
- Wer: 12.5219
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6724 | 0.71 | 30 | 0.3868 | 19.2016 |
| 0.2748 | 1.43 | 60 | 0.3584 | 15.3846 |
| 0.1701 | 2.14 | 90 | 0.3415 | 13.5346 |
| 0.0814 | 2.86 | 120 | 0.3366 | 13.3398 |
| 0.0419 | 3.57 | 150 | 0.3567 | 13.3982 |
| 0.0254 | 4.29 | 180 | 0.3627 | 12.7167 |
| 0.0124 | 5.0 | 210 | 0.3707 | 12.5219 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
mzbac/Mixtral-8x7B-v0.1-hf-4bit-mlx-adapters
|
mzbac
| 2024-01-26T13:59:49Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2024-01-26T08:51:04Z |
---
license: mit
---
# Qlora adapters for Mixtral-8x7B-v0.1-hf-4bit-mlx
## fine-tuned on guanaco dataset
## inference vis mlx-lm
```
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Mixtral-8x7B-v0.1-hf-4bit-mlx",adapter_file="adapters.npz")
generate(model=model, tokenizer=tokenizer, prompt="### Human: write a quick sort in python.\n### Assistant: ", max_tokens=500, verbose=True,temp=0.3)
```
## serve as an API Service
```
pip install mlx-llm-server
mlx-llm-server --model-path mlx-community/Mixtral-8x7B-v0.1-hf-4bit-mlx --adapter-file adapters.npz
```
|
anilguven/bert_multi_turkish_tweet
|
anilguven
| 2024-01-26T13:59:20Z | 94 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"multilingual",
"turkish",
"tweet",
"emotion",
"sentiment",
"tr",
"dataset:anilguven/turkish_tweet_emotion_dataset",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T13:40:01Z |
---
license: unknown
datasets:
- anilguven/turkish_tweet_emotion_dataset
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- multilingual
- turkish
- bert
- tweet
- emotion
- sentiment
---
### Model Info
This model was developed/finetuned for tweet emotion detection task for the Turkish Language. This model was finetuned via tweet dataset. This dataset contains 5 classes: angry, happy, sad, surprised and afraid.
- LABEL_0: angry
- LABEL_1: afraid
- LABEL_2: happy
- LABEL_3: surprised
- LABEL_4: sad
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_tweet_emotion_dataset
- **Paper:** https://ieeexplore.ieee.org/document/9559014
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_tweet_emotion_analysis_with_language_models
- **Finetuned from model [optional]:** https://huggingface.co/bert-base-multilingual-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- eval_loss = 0.5407382257189601
- mcc = 0.7682691555667568
- Accuracy: %81.37
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@INPROCEEDINGS{9559014,
author={Guven, Zekeriya Anil},
booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)},
title={Comparison of BERT Models and Machine Learning Methods for Sentiment Analysis on Turkish Tweets},
year={2021},
volume={},
number={},
pages={98-101},
keywords={Computer science;Sentiment analysis;Analytical models;Social networking (online);Computational modeling;Bit error rate;Random forests;Sentiment Analysis;BERT;Machine Learning;Text Classification;Tweet Analysis.},
doi={10.1109/UBMK52708.2021.9559014}}*
**APA:**
*Guven, Z. A. (2021, September). Comparison of BERT models and machine learning methods for sentiment analysis on Turkish tweets. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 98-101). IEEE.*
|
youngbreadho/distilbert-base-uncased-distilled-clinc
|
youngbreadho
| 2024-01-26T13:55:11Z | 97 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:41:19Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results: []
---
<!-- 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-distilled-clinc
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:
- Loss: 0.1160
- Accuracy: 0.9419
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1786 | 1.0 | 318 | 0.7011 | 0.7113 |
| 0.5333 | 2.0 | 636 | 0.3054 | 0.8581 |
| 0.2694 | 3.0 | 954 | 0.1794 | 0.9187 |
| 0.1792 | 4.0 | 1272 | 0.1441 | 0.9313 |
| 0.1468 | 5.0 | 1590 | 0.1316 | 0.9358 |
| 0.1323 | 6.0 | 1908 | 0.1242 | 0.9406 |
| 0.1239 | 7.0 | 2226 | 0.1207 | 0.9381 |
| 0.1189 | 8.0 | 2544 | 0.1179 | 0.9406 |
| 0.116 | 9.0 | 2862 | 0.1163 | 0.9426 |
| 0.1143 | 10.0 | 3180 | 0.1160 | 0.9419 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
paths1551/cethu-v1-b4
|
paths1551
| 2024-01-26T13:54:28Z | 1 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:Lykon/DreamShaper",
"base_model:adapter:Lykon/DreamShaper",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-26T11:27:24Z |
---
license: creativeml-openrail-m
base_model: Lykon/DreamShaper
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - paths1551/cethu-v1-b4
These are LoRA adaption weights for Lykon/DreamShaper. The weights were fine-tuned on the /workspace/cethu_lora dataset. You can find some example images in the following.
|
Pavan-124/lwin_winery_roberta
|
Pavan-124
| 2024-01-26T13:50:51Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"token-classification",
"generated_from_keras_callback",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-26T08:29:05Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: Pavan-124/lwin_winery_roberta
results: []
---
<!-- 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. -->
# Pavan-124/lwin_winery_roberta
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: 0.1539
- Validation Loss: 0.0875
- Train Precision: 0.8705
- Train Recall: 0.8780
- Train F1: 0.8742
- Train Accuracy: 0.9661
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, '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 |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.1539 | 0.0875 | 0.8705 | 0.8780 | 0.8742 | 0.9661 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
ondevicellm/tinyllama_mole_dpo_ep3
|
ondevicellm
| 2024-01-26T13:50:19Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mixtralmole",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"custom_code",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:ondevicellm/tinyllama_mole_sft_ultrachat_ep3",
"base_model:finetune:ondevicellm/tinyllama_mole_sft_ultrachat_ep3",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-01-26T08:51:34Z |
---
base_model: ondevicellm/tinyllama_mole_sft_ultrachat_ep3
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: tinyllama_mole_dpo_ep3
results: []
---
<!-- 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. -->
# tinyllama_mole_dpo_ep3
This model is a fine-tuned version of [ondevicellm/tinyllama_mole_sft_ultrachat_ep3](https://huggingface.co/ondevicellm/tinyllama_mole_sft_ultrachat_ep3) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6285
- Rewards/chosen: -0.3050
- Rewards/rejected: -0.5353
- Rewards/accuracies: 0.6806
- Rewards/margins: 0.2302
- Logps/rejected: -354.2071
- Logps/chosen: -373.1399
- Logits/rejected: -1.6731
- Logits/chosen: -1.8041
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6896 | 0.1 | 100 | 0.6899 | 0.0064 | -0.0013 | 0.6448 | 0.0076 | -300.8089 | -342.0017 | -1.7574 | -1.8918 |
| 0.6762 | 0.21 | 200 | 0.6756 | -0.0293 | -0.0716 | 0.6627 | 0.0423 | -307.8423 | -345.5688 | -1.7501 | -1.8839 |
| 0.6499 | 0.31 | 300 | 0.6587 | -0.0875 | -0.1813 | 0.6687 | 0.0938 | -318.8118 | -351.3895 | -1.7358 | -1.8688 |
| 0.6374 | 0.42 | 400 | 0.6451 | -0.1726 | -0.3218 | 0.6746 | 0.1493 | -332.8632 | -359.8953 | -1.7164 | -1.8482 |
| 0.6348 | 0.52 | 500 | 0.6377 | -0.2696 | -0.4550 | 0.6647 | 0.1854 | -346.1808 | -369.6013 | -1.6884 | -1.8208 |
| 0.6308 | 0.63 | 600 | 0.6333 | -0.2783 | -0.4815 | 0.6726 | 0.2032 | -348.8291 | -370.4673 | -1.6965 | -1.8269 |
| 0.62 | 0.73 | 700 | 0.6312 | -0.2323 | -0.4505 | 0.6806 | 0.2182 | -345.7306 | -365.8656 | -1.6841 | -1.8149 |
| 0.6055 | 0.84 | 800 | 0.6287 | -0.2877 | -0.5169 | 0.6865 | 0.2292 | -352.3697 | -371.4099 | -1.6793 | -1.8099 |
| 0.6357 | 0.94 | 900 | 0.6285 | -0.3050 | -0.5353 | 0.6806 | 0.2302 | -354.2071 | -373.1399 | -1.6731 | -1.8041 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
YanSte/fine_tuning_llama-2_chat_alpaca_dolly_hf
|
YanSte
| 2024-01-26T13:43:22Z | 2 | 1 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-26T12:58:42Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
### Framework versions
- PEFT 0.7.1
|
nbeerbower/bruphin-epsilon-GGUF-q4_0
|
nbeerbower
| 2024-01-26T13:38:38Z | 7 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:BarryFutureman/WildMarcoroni-Variant1-7B",
"base_model:merge:BarryFutureman/WildMarcoroni-Variant1-7B",
"base_model:nbeerbower/bruphin-delta",
"base_model:merge:nbeerbower/bruphin-delta",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-01-26T00:54:40Z |
---
base_model:
- BarryFutureman/WildMarcoroni-Variant1-7B
- nbeerbower/bruphin-delta
tags:
- mergekit
- merge
---
# bruphin-epsilon-GGUF-q4_0
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
Quantized using [llama.cpp](https://github.com/ggerganov/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:
* [BarryFutureman/WildMarcoroni-Variant1-7B](https://huggingface.co/BarryFutureman/WildMarcoroni-Variant1-7B)
* [nbeerbower/bruphin-delta](https://huggingface.co/nbeerbower/bruphin-delta)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: nbeerbower/bruphin-delta
layer_range: [0, 32]
- model: BarryFutureman/WildMarcoroni-Variant1-7B
layer_range: [0, 32]
merge_method: slerp
base_model: BarryFutureman/WildMarcoroni-Variant1-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
sabayo/Marcaps-GPT-adapters-ft
|
sabayo
| 2024-01-26T13:37:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T13:36:56Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
|
JKuang96/ppo-SnowballTarget
|
JKuang96
| 2024-01-26T13:35:52Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-01-26T13:35:47Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
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: JKuang96/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
anilguven/bert_tr_turkish_spam_email
|
anilguven
| 2024-01-26T13:35:13Z | 94 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"turkish",
"spam",
"ham",
"email",
"tr",
"dataset:anilguven/turkish_spam_email",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T19:36:37Z |
---
license: unknown
datasets:
- anilguven/turkish_spam_email
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- turkish
- spam
- ham
- email
- bert
---
### Model Info
This model was developed/finetuned for spam detection task for Turkish Language. This model was finetuned via spam/ham email dataset.
- LABEL_0: ham/normal mail
- LABEL_1: spam mail
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_spam_email
- **Paper:** https://dergipark.org.tr/tr/pub/ejosat/issue/75736/1234079
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_spam_email_detection_with_language_models
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/bert-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
# Model Load safetensors
<!-- Provide a quick summary of what the model is/does. -->
Detailed https://huggingface.co/docs/diffusers/using-diffusers/using_safetensors
### Results
- F1-score: %94.0
- Accuracy: %94.08
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{article_1234079, title={Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={1–6}, year={2023}, DOI={10.31590/ejosat.1234079}, author={GÜVEN, Zekeriya Anıl}, keywords={Siber Güvenlik, Spam Tespiti, Dil Modeli, Makine Öğrenmesi, Doğal Dil İşleme, Metin Sınıflandırma, Cyber Security, Spam Detection, Language Model, Machine Learning, Natural Language Processing, Text Classification}, number={47}, publisher={Osman SAĞDIÇ} }*
**APA:**
*GÜVEN, Z. A. (2023). Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi. Avrupa Bilim ve Teknoloji Dergisi, (47), 1-6.*
|
erdometo/TurkishDistilbert
|
erdometo
| 2024-01-26T13:35:09Z | 114 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-26T12:22:21Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: TurkishDistilbert
results: []
---
<!-- 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. -->
# TurkishDistilbert
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:
- Loss: 2.5396
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5851 | 1.0 | 520 | 2.8374 |
| 2.668 | 2.0 | 1040 | 2.6035 |
| 2.3349 | 3.0 | 1560 | 2.5396 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
NandGate1110/mistral_7b_guanaco
|
NandGate1110
| 2024-01-26T13:34:36Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"arxiv:1910.09700",
"region:us"
] | null | 2024-01-18T15:23:41Z |
---
library_name: peft
base_model: Mistral-7B-Instruct-v0.2
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
### Framework versions
- PEFT 0.7.1
|
anilguven/albert_tr_turkish_spam_email
|
anilguven
| 2024-01-26T13:34:19Z | 121 | 1 |
transformers
|
[
"transformers",
"safetensors",
"albert",
"text-classification",
"turkish",
"spam",
"ham",
"email",
"bert",
"tr",
"dataset:anilguven/turkish_spam_email",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T19:34:03Z |
---
license: unknown
datasets:
- anilguven/turkish_spam_email
language:
- tr
metrics:
- accuracy
- f1
- recall
- precision
tags:
- turkish
- spam
- ham
- email
- albert
- bert
---
### Model Info
This model was developed/finetuned for spam detection task for Turkish Language. This model was finetuned via spam/ham email dataset.
- LABEL_0: ham/normal mail
- LABEL_1: spam mail
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_spam_email
- **Paper:** https://dergipark.org.tr/tr/pub/ejosat/issue/75736/1234079
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_spam_email_detection_with_language_models
- **Finetuned from model [optional]:** https://huggingface.co/loodos/albert-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
# Model Load safetensors
<!-- Provide a quick summary of what the model is/does. -->
Detailed https://huggingface.co/docs/diffusers/using-diffusers/using_safetensors
### Results
- F1-score: %93.55
- Accuracy: %93.10
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{article_1234079, title={Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={1–6}, year={2023}, DOI={10.31590/ejosat.1234079}, author={GÜVEN, Zekeriya Anıl}, keywords={Siber Güvenlik, Spam Tespiti, Dil Modeli, Makine Öğrenmesi, Doğal Dil İşleme, Metin Sınıflandırma, Cyber Security, Spam Detection, Language Model, Machine Learning, Natural Language Processing, Text Classification}, number={47}, publisher={Osman SAĞDIÇ} }*
**APA:**
*GÜVEN, Z. A. (2023). Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi. Avrupa Bilim ve Teknoloji Dergisi, (47), 1-6.*
|
triet1102/distilbert-base-uncased-finetuned-clinc
|
triet1102
| 2024-01-26T13:22:58Z | 97 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T13:18:57Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results: []
---
<!-- 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-clinc
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:
- Loss: 0.7583
- Accuracy: 0.9203
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2885 | 1.0 | 318 | 3.2661 | 0.7310 |
| 2.5978 | 2.0 | 636 | 1.8508 | 0.8458 |
| 1.5196 | 3.0 | 954 | 1.1364 | 0.8990 |
| 0.9933 | 4.0 | 1272 | 0.8393 | 0.9148 |
| 0.7755 | 5.0 | 1590 | 0.7583 | 0.9203 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
simpragma/breeze-listen-dsw-base-ta
|
simpragma
| 2024-01-26T13:12:28Z | 62 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"ta",
"dataset:mozilla-foundation/common_voice_16_0",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-23T08:26:57Z |
---
language:
- ta
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Breeze DSW Tamil - base
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_0 ta
type: mozilla-foundation/common_voice_16_0
config: ta
split: test
args: ta
metrics:
- name: Wer
type: wer
value: 21.407068619939793
---
<!-- 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. -->
# Breeze DSW Tamil - base
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_16_0 ta dataset.
It achieves the following results on the evaluation set:
- Loss: 0.375
- Wer: 21.4071
## 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: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1698 | 0.1 | 100 | 0.5723 | 30.4406 |
| 0.3578 | 0.2 | 200 | 0.4302 | 25.6862 |
| 0.2832 | 0.3 | 300 | 0.3967 | 23.2048 |
| 0.2663 | 0.4 | 400 | 0.4038 | 23.8525 |
| 0.5175 | 0.5 | 500 | 0.3962 | 24.1466 |
| 0.2365 | 0.6 | 600 | 0.3850 | 22.2595 |
| 0.1692 | 0.7 | 700 | 0.3960 | 21.8687 |
| 0.1815 | 0.8 | 800 | 0.3823 | 22.0772 |
| 0.1612 | 0.9 | 900 | 0.3701 | 21.8056 |
| 0.1393 | 1.0 | 1000 | 0.375 | 21.4071 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
|
Kralley/mistral-7b-da-instr-fn
|
Kralley
| 2024-01-26T13:11:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:danish-foundation-models/munin-7b-alpha",
"base_model:adapter:danish-foundation-models/munin-7b-alpha",
"license:apache-2.0",
"region:us"
] | null | 2024-01-26T11:42:24Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: danish-foundation-models/munin-7b-alpha
model-index:
- name: ft-results
results: []
---
<!-- 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. -->
# ft-results
This model is a fine-tuned version of [danish-foundation-models/munin-7b-alpha](https://huggingface.co/danish-foundation-models/munin-7b-alpha) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
|
not-lain/test-dynamic-pipeline
|
not-lain
| 2024-01-26T13:11:10Z | 94 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T12:58:51Z |
---
pipeline_tag: text-classification
---
# how to load the pipeline
```python
from transformers import pipeline
pipe = pipeline(model="not-lain/test-dynamic-pipeline",trust_remote_code=True)
pipe("hi",second_text="hello")
```
|
youngbreadho/distilbert-base-uncased-finetuned-clinc
|
youngbreadho
| 2024-01-26T13:06:42Z | 97 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:05:01Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results: []
---
<!-- 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-clinc
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:
- Loss: 0.7682
- Accuracy: 0.9184
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2967 | 1.0 | 318 | 3.2810 | 0.7181 |
| 2.6146 | 2.0 | 636 | 1.8653 | 0.8403 |
| 1.5377 | 3.0 | 954 | 1.1478 | 0.8981 |
| 1.0043 | 4.0 | 1272 | 0.8491 | 0.9135 |
| 0.7902 | 5.0 | 1590 | 0.7682 | 0.9184 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
mamsis25/cubao
|
mamsis25
| 2024-01-26T13:04:00Z | 0 | 0 | null |
[
"conversational",
"aa",
"region:us"
] |
text-generation
| 2024-01-26T13:03:46Z |
---
language:
- aa
pipeline_tag: conversational
---
|
bartowski/deepseek-coder-7b-instruct-v1.5-exl2
|
bartowski
| 2024-01-26T12:59:37Z | 4 | 3 | null |
[
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2024-01-26T12:46:00Z |
---
license: other
license_name: deepseek
license_link: LICENSE
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of deepseek-coder-7b-instruct-v1.5
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.12">turboderp's ExLlamaV2 v0.0.12</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/deepseek-ai/deepseek-coder-7b-instruct-v1.5
No GQA - VRAM requirements will be higher
| Branch | Bits | lm_head bits | Size (4k) | Size (16k) | Description |
| -------------------------------------------------------------- | ---- | ------------ | --------- | ---------- | ----------- |
| [8_0](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/8_0) | 8.0 | 8.0 | 9.4 GB | 15.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/6_5) | 6.5 | 8.0 | 8.6 GB | 14.8 GB | Near unquantized performance at vastly reduced size, **recommended**. |
| [5_0](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/5_0) | 5.0 | 6.0 | 7.2 GB | 13.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards with 4k context. |
| [4_25](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/4_25) | 4.25 | 6.0 | 6.5 GB | 12.7 GB | GPTQ equivalent bits per weight. |
| [3_5](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/3_5) | 3.5 | 6.0 | 5.9 GB | 12.1 GB | Lower quality, not recommended. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/deepseek-coder-7b-instruct-v1.5-exl2 deepseek-coder-7b-instruct-v1.5-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 `deepseek-coder-7b-instruct-v1.5-exl2`:
```shell
mkdir deepseek-coder-7b-instruct-v1.5-exl2
huggingface-cli download bartowski/deepseek-coder-7b-instruct-v1.5-exl2 --local-dir deepseek-coder-7b-instruct-v1.5-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir deepseek-coder-7b-instruct-v1.5-exl2-6_5
huggingface-cli download bartowski/deepseek-coder-7b-instruct-v1.5-exl2 --revision 6_5 --local-dir deepseek-coder-7b-instruct-v1.5-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir deepseek-coder-7b-instruct-v1.5-exl2-6.5
huggingface-cli download bartowski/deepseek-coder-7b-instruct-v1.5-exl2 --revision 6_5 --local-dir deepseek-coder-7b-instruct-v1.5-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
AntoineGourru/Mistral_drome_full
|
AntoineGourru
| 2024-01-26T12:51:10Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T12:45:29Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
|
ambrosfitz/neural-history-chat-v1.5
|
ambrosfitz
| 2024-01-26T12:45:29Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"dataset:ambrosfitz/mighty-history-merge",
"dataset:ambrosfitz/textbook-openstax-yawp-merge",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-25T23:50:56Z |
---
library_name: transformers
license: cc
datasets:
- ambrosfitz/mighty-history-merge
- ambrosfitz/textbook-openstax-yawp-merge
---
# Model Card for Model ID
An updated version of Neural History Chat, using the mighty-history-merge dataset to fine-tune the previous version (v1.0).
## Model Details
```
Run history:
train/epoch ▁▁▂▂▃▃▃▄▄▅▅▅▆▆▇▇▇██
train/global_step ▁▁▂▂▃▃▃▄▄▅▅▅▆▆▇▇▇██
train/learning_rate ▂▃▅▆▇█▇▇▆▆▅▄▄▃▃▂▂▁
train/loss █▆▄▃▃▃▃▃▂▃▂▂▁▁▁▁▂▁
train/total_flos ▁
train/train_loss ▁
train/train_runtime ▁
train/train_samples_per_second ▁
train/train_steps_per_second ▁
Run summary:
train/epoch 1.98
train/global_step 92
train/learning_rate 0.0
train/loss 0.7792
train/total_flos 1.756453697101824e+16
train/train_loss 1.30356
train/train_runtime 1176.2194
train/train_samples_per_second 10.068
train/train_steps_per_second 0.078
```
## Training Explained
We went with a shorter training session of roughly 2 epochs for testing and evaluation. More steps/epochs might be
in the future, but colab pricing is pretty steep. Currently to merge the peft back to the model, requires roughly 40GB of
GPU RAM. So renting a Google Colab A100 is required and runs through credits quickly.
|
varun-v-rao/t5-large-lora-4.72M-snli
|
varun-v-rao
| 2024-01-26T12:45:16Z | 36 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text-classification",
"generated_from_trainer",
"base_model:google-t5/t5-large",
"base_model:finetune:google-t5/t5-large",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T07:49:26Z |
---
license: apache-2.0
base_model: t5-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: t5-large-lora-4.72M-snli
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-large-lora-4.72M-snli
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6356
- Accuracy: 0.7945
## 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: 128
- eval_batch_size: 128
- seed: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3516 | 1.0 | 4292 | 0.2753 | 0.9041 |
| 0.3315 | 2.0 | 8584 | 0.2624 | 0.9077 |
| 0.3283 | 3.0 | 12876 | 0.2595 | 0.9101 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF
|
MaziyarPanahi
| 2024-01-26T12:32:49Z | 45 | 5 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"migtissera/Tess-XS-v1-3-yarn-128K",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2",
"base_model:quantized:MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2",
"conversational"
] |
text-generation
| 2024-01-26T11:36:52Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- migtissera/Tess-XS-v1-3-yarn-128K
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF
base_model: MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2)
## Description
[MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF) contains GGUF format model files for [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF) and below it, a specific filename to download, such as: Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
|
paths1551/cethu-v1-b1
|
paths1551
| 2024-01-26T12:31:34Z | 3 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:Lykon/DreamShaper",
"base_model:adapter:Lykon/DreamShaper",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-26T11:11:45Z |
---
license: creativeml-openrail-m
base_model: Lykon/DreamShaper
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - paths1551/cethu-v1-b1
These are LoRA adaption weights for Lykon/DreamShaper. The weights were fine-tuned on the /workspace/cethu_lora dataset. You can find some example images in the following.
|
xiawei910/U8LunarLander-v2
|
xiawei910
| 2024-01-26T12:16:54Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T12:16:47Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -188.57 +/- 148.83
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'xiawei910/U8LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
thanosAnt/blip2-peft-facad-finetuned-val-images-2-epochs
|
thanosAnt
| 2024-01-26T12:10:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:ybelkada/blip2-opt-2.7b-fp16-sharded",
"base_model:adapter:ybelkada/blip2-opt-2.7b-fp16-sharded",
"region:us"
] | null | 2024-01-26T12:10:34Z |
---
library_name: peft
base_model: ybelkada/blip2-opt-2.7b-fp16-sharded
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
### Framework versions
- PEFT 0.7.2.dev0
|
mudogruer/mistral-7b-dolly
|
mudogruer
| 2024-01-26T11:54:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T11:54:16Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
|
SilverCoder66/Mistral-7B-Instruct-adapt-v0.22
|
SilverCoder66
| 2024-01-26T11:29:38Z | 0 | 0 | null |
[
"safetensors",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-01-26T11:28:28Z |
---
license: cc-by-nc-4.0
---
Description TBD, thanks for checking in!
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(repo_id)
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
sannysayril/distilgpt2-finetuned-wikitext2
|
sannysayril
| 2024-01-26T11:29:35Z | 46 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T11:22:32Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_keras_callback
model-index:
- name: sannysayril/distilgpt2-finetuned-wikitext2
results: []
---
<!-- 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. -->
# sannysayril/distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.8580
- Validation Loss: 3.6737
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.8580 | 3.6737 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
EssJayB/ddpm-celebahq-finetuned-butterflies-2epoch_us
|
EssJayB
| 2024-01-26T11:28:23Z | 44 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2024-01-26T11:28:02Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('EssJayB/ddpm-celebahq-finetuned-butterflies-2epoch_us')
image = pipeline().images[0]
image
```
|
tiagoblima/mt5_base-qg-ap-oficial
|
tiagoblima
| 2024-01-26T11:19:00Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:tiagoblima/preprocessed-du-qg-squadv1_pt",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-23T02:10:50Z |
---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
datasets:
- tiagoblima/preprocessed-du-qg-squadv1_pt
model-index:
- name: mt5_base-qg-ap-oficial
results: []
---
<!-- 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. -->
# mt5_base-qg-ap-oficial
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the tiagoblima/preprocessed-du-qg-squadv1_pt dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0951
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7276 | 1.0 | 1386 | 1.3489 |
| 1.5698 | 2.0 | 2772 | 1.2226 |
| 1.4547 | 3.0 | 4158 | 1.1470 |
| 1.3969 | 4.0 | 5544 | 1.1057 |
| 1.3748 | 5.0 | 6930 | 1.0951 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.1
|
Maikou/Michelangelo
|
Maikou
| 2024-01-26T11:12:45Z | 0 | 15 | null |
[
"image-to-3d",
"text-to-3d",
"arxiv:2306.17115",
"license:lgpl-3.0",
"region:us"
] |
text-to-3d
| 2023-10-25T09:26:10Z |
---
license: lgpl-3.0
pipeline_tag: text-to-3d
tags:
- image-to-3d
---
# Michelangelo
* [Project Page](https://neuralcarver.github.io/michelangelo/)
* [Paper](https://arxiv.org/abs/2306.17115)
* [Code](https://github.com/NeuralCarver/Michelangelo)
* [Demo](https://huggingface.co/spaces/Maikou/Michelangelo)
|
s3nh/EstopianMaid-13B-GGUF
|
s3nh
| 2024-01-26T11:05:51Z | 468 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T09:24:46Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/KatyTheCutie/EstopianMaid-13B).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### inference
User: Tell me story about what is an quantization and what do we need to build.
Answer: Once upon a time, in the magical world of digital music, there was a problem that needed solving. The problem was how to take sound waves, which are continuous and smooth, and turn them into something that computers could understand and manipulate easily. This process is called "quantization."
In order to build this solution, we needed clever engineers who understood both the art of music and the science of technology. They worked tirelessly, experimenting with different methods and algorithms, until they finally created a system that could transform sound waves into digital data.
Their invention was called an "
# Original model card
|
xyfJASON/Context-Encoder-pytorch
|
xyfJASON
| 2024-01-26T11:00:29Z | 0 | 0 | null |
[
"tensorboard",
"license:mit",
"region:us"
] | null | 2024-01-26T10:50:41Z |
---
license: mit
---
Checkpoints and training logs for GitHub repository: [xyfJASON/Context-Encoder-pytorch](https://github.com/xyfJASON/Context-Encoder-pytorch).
|
numind/NuSentiment-multilingual
|
numind
| 2024-01-26T10:52:59Z | 20,598 | 11 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentiment-analysis",
"text-classification",
"generic",
"sentiment-classification",
"multilingual",
"en",
"ar",
"fr",
"de",
"pt",
"it",
"es",
"zh",
"ja",
"ko",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-08-11T12:05:16Z |
---
license: mit
language:
- en
- ar
- fr
- de
- pt
- it
- es
- zh
- ja
- ko
pipeline_tag: feature-extraction
tags:
- sentiment-analysis
- text-classification
- generic
- sentiment-classification
- multilingual
---
## Model
Base version of e5-multilingual finetunned on an annotated subset of mC4 (multilingual C4). This model provide generic embedding for sentiment analysis. Embeddings can be used out of the box or fine tune on specific datasets.
Blog post: https://www.numind.ai/blog/creating-task-specific-foundation-models-with-gpt-4
## Usage
Below is an example to encode text and get embedding.
```python
import torch
from transformers import AutoTokenizer, AutoModel
model = AutoModel.from_pretrained("Numind/e5-multilingual-sentiment_analysis")
tokenizer = AutoTokenizer.from_pretrained("Numind/e5-multilingual-sentiment_analysis")
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
size = 256
text = "This movie is amazing"
encoding = tokenizer(
text,
truncation=True,
padding='max_length',
max_length= size,
)
emb = model(
torch.reshape(torch.tensor(encoding.input_ids),(1,len(encoding.input_ids))).to(device),output_hidden_states=True
).hidden_states[-1].cpu().detach()
embText = torch.mean(emb,axis = 1)
```
|
Augustya07/Llama-2-7b-hf-neitzsche-books
|
Augustya07
| 2024-01-26T10:47:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T10:47:17Z |
---
library_name: transformers
tags:
- unsloth
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
|
Augustya07/Llama-2-7b-hf-neitzsche-books-adapters
|
Augustya07
| 2024-01-26T10:46:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T10:46:49Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
|
VanillaVanilla/poca-SoccerTwos
|
VanillaVanilla
| 2024-01-26T10:40:01Z | 9 | 1 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2024-01-26T10:39:09Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: VanillaVanilla/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kam414/pre-train-v3
|
kam414
| 2024-01-26T10:31:54Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"license:other",
"region:us"
] | null | 2024-01-26T10:17:15Z |
---
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: HuggingFaceH4/zephyr-7b-beta
model-index:
- name: train_2024-01-26-10-08-09
results: []
---
<!-- 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_2024-01-26-10-08-09
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the wiki_demo 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: 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
- num_epochs: 8.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
ertyazilim/emotion-analiysis-with-distilbert
|
ertyazilim
| 2024-01-26T10:31:19Z | 46 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T10:14:07Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: ertyazilim/emotion-analiysis-with-distilbert
results: []
---
<!-- 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. -->
# ertyazilim/emotion-analiysis-with-distilbert
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: 0.1339
- Validation Loss: 0.1353
- Train Accuracy: 0.9385
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3922 | 0.1544 | 0.941 | 0 |
| 0.1339 | 0.1353 | 0.9385 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
crypticvandal/NeuralPipe-7B-slerp
|
crypticvandal
| 2024-01-26T10:26:32Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:merge:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T10:20:05Z |
---
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
base_model:
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
---
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using LazyMergekit:
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
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
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "crypticvandal/NeuralPipe-7B-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"])
```
|
e22vvb/EN_mt5-base_10_wikiSQL
|
e22vvb
| 2024-01-26T10:24:28Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikisql",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-26T05:06:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikisql
model-index:
- name: EN_mt5-base_10_wikiSQL
results: []
---
<!-- 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. -->
# EN_mt5-base_10_wikiSQL
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wikisql dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0849
- Rouge2 Precision: 0.864
- Rouge2 Recall: 0.787
- Rouge2 Fmeasure: 0.8178
## 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: 21
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.1677 | 1.0 | 3085 | 0.1224 | 0.8269 | 0.7506 | 0.7803 |
| 0.1287 | 2.0 | 6170 | 0.1028 | 0.8458 | 0.7673 | 0.7988 |
| 0.1086 | 3.0 | 9255 | 0.0959 | 0.8511 | 0.7727 | 0.8042 |
| 0.0965 | 4.0 | 12340 | 0.0900 | 0.8543 | 0.777 | 0.808 |
| 0.089 | 5.0 | 15425 | 0.0883 | 0.8575 | 0.7802 | 0.8111 |
| 0.0809 | 6.0 | 18510 | 0.0866 | 0.8606 | 0.7834 | 0.8143 |
| 0.0771 | 7.0 | 21595 | 0.0860 | 0.8625 | 0.7851 | 0.8161 |
| 0.0745 | 8.0 | 24680 | 0.0855 | 0.8633 | 0.7862 | 0.8171 |
| 0.0715 | 9.0 | 27765 | 0.0848 | 0.8641 | 0.7869 | 0.8178 |
| 0.0702 | 10.0 | 30850 | 0.0849 | 0.864 | 0.787 | 0.8178 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.7.dev0
- Tokenizers 0.13.3
|
raicrits/DistilFEVERit
|
raicrits
| 2024-01-26T10:22:01Z | 52 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T10:20:34Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
base_model: distilbert-base-multilingual-cased
model-index:
- name: DistilFEVERit
results: []
---
<!-- 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. -->
# DistilFEVERit
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.37.0
- TensorFlow 2.8.0
- Datasets 2.13.0
- Tokenizers 0.15.1
|
sheduele/bert_C_2
|
sheduele
| 2024-01-26T10:21:22Z | 92 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T09:25:28Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert_C_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_C_2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6722.5049
- Mae: 52.1614
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 72
- eval_batch_size: 72
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 51 | 8283.7012 | 62.4105 |
| No log | 2.0 | 102 | 7761.8237 | 58.8175 |
| No log | 3.0 | 153 | 7552.2861 | 57.4051 |
| No log | 4.0 | 204 | 7422.1416 | 56.5480 |
| No log | 5.0 | 255 | 7319.2437 | 55.8786 |
| No log | 6.0 | 306 | 7231.1514 | 55.3173 |
| No log | 7.0 | 357 | 7153.9229 | 54.8313 |
| No log | 8.0 | 408 | 7085.3296 | 54.4032 |
| No log | 9.0 | 459 | 7023.9609 | 54.0201 |
| 8468.761 | 10.0 | 510 | 6969.4009 | 53.6830 |
| 8468.761 | 11.0 | 561 | 6920.9131 | 53.3808 |
| 8468.761 | 12.0 | 612 | 6878.1675 | 53.1132 |
| 8468.761 | 13.0 | 663 | 6841.0210 | 52.8787 |
| 8468.761 | 14.0 | 714 | 6809.2080 | 52.6846 |
| 8468.761 | 15.0 | 765 | 6782.4966 | 52.5224 |
| 8468.761 | 16.0 | 816 | 6760.8091 | 52.3901 |
| 8468.761 | 17.0 | 867 | 6744.0356 | 52.2873 |
| 8468.761 | 18.0 | 918 | 6732.0830 | 52.2164 |
| 8468.761 | 19.0 | 969 | 6724.9185 | 52.1753 |
| 7734.004 | 20.0 | 1020 | 6722.5049 | 52.1614 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
ImSakushi/nistraal-2
|
ImSakushi
| 2024-01-26T10:19:30Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-25T15:21:20Z |
---
library_name: transformers
tags: []
---
|
s3nh/CrystalMistral_7b_v.01-GGUF
|
s3nh
| 2024-01-26T10:12:24Z | 1 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-01-26T09:02:11Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/Crystalcareai/CrystalMistral_7b_v.01).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### inference
# Original model card
|
hojzas/setfit-proj8-multilabel
|
hojzas
| 2024-01-26T10:07:59Z | 49 | 0 |
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"dataset:hojzas/proj8-multilabel",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"co2_eq_emissions",
"region:us"
] |
text-classification
| 2024-01-26T10:07:33Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- hojzas/proj8-multilabel
metrics:
- accuracy
widget:
- text: 'def first_with_given_key(iterable, key=lambda x: x):\n keys_used = {}\n for
item in iterable:\n rp = repr(key(item))\n if rp not in keys_used.keys():\n keys_used[rp]
= repr(item)\n yield item'
- text: 'def first_with_given_key(iterable, key=lambda x: x):\n keys=[]\n for
i in iterable:\n if key(i) not in keys:\n yield i\n keys.append(key(i))'
- text: 'def first_with_given_key(iterable, key=repr):\n set_of_keys = set()\n lambda_key
= (lambda x: key(x))\n for item in iterable:\n key = lambda_key(item)\n try:\n key_for_set
= hash(key)\n except TypeError:\n key_for_set = repr(key)\n if
key_for_set in set_of_keys:\n continue\n set_of_keys.add(key_for_set)\n yield
item'
- text: 'def first_with_given_key(iterable, key = lambda x: x):\n found_keys={}\n for
i in iterable:\n if key(i) not in found_keys.keys():\n found_keys[key(i)]=i\n yield
i'
- text: 'def first_with_given_key(the_iterable, key=lambda x: x):\n temp_keys=[]\n for
i in range(len(the_iterable)):\n if (key(the_iterable[i]) not in temp_keys):\n temp_keys.append(key(the_iterable[i]))\n yield
the_iterable[i]\n del temp_keys'
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
emissions: 0.2716104726718793
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
ram_total_size: 251.49160385131836
hours_used: 0.005
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj8-multilabel](https://huggingface.co/datasets/hojzas/proj8-multilabel) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [hojzas/proj8-multilabel](https://huggingface.co/datasets/hojzas/proj8-multilabel)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("hojzas/setfit-proj8-multilabel")
# Run inference
preds = model("def first_with_given_key(iterable, key=lambda x: x):\n keys=[]\n for i in iterable:\n if key(i) not in keys:\n yield i\n keys.append(key(i))")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 43 | 92.5185 | 125 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0147 | 1 | 0.3001 | - |
| 0.7353 | 50 | 0.0104 | - |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.000 kg of CO2
- **Hours Used**: 0.005 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: No GPU used
- **CPU Model**: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
- **RAM Size**: 251.49 GB
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.36.1
- PyTorch: 2.1.2+cu121
- Datasets: 2.14.7
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
isaacekblad/dendrite
|
isaacekblad
| 2024-01-26T10:07:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-01-26T10:07:39Z |
---
license: creativeml-openrail-m
---
|
Shalie/VshojoMataraKan
|
Shalie
| 2024-01-26T09:42:47Z | 3 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"dataset:Hunko/VshojoMataraKan-Dataset",
"base_model:hollowstrawberry/stable-diffusion-guide",
"base_model:adapter:hollowstrawberry/stable-diffusion-guide",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-26T08:54:34Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
masterpiece, best quality, 1girl, <lora:spmatarakandef:1> matarakandef,
arthropod girl, extra arms, antennae, cleavage, cleavage cutout, white
dress, navel, thighhighs
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit,
fewer digits, cropped, worst quality, low quality, normal quality, jpeg
artifacts, signature, watermark, username, blurry, artist name
output:
url: >-
images/01599-222020380-masterpiece, best quality, 1girl,
_lora_spmatarakandef_1_ matarakandef, arthropod girl, extra arms,
antennae, cleavage, cleavage.png
- text: >-
masterpiece, best quality, 1girl, <lora:spmatarakandef:1> matarakandef,
arthropod girl, extra arms, antennae, cleavage, cleavage cutout, white
dress, navel, thighhighs, blush, looking away, solo, bouquet, flower, pink
flower, pink rose, rose, upper body, white flower
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit,
fewer digits, cropped, worst quality, low quality, normal quality, jpeg
artifacts, signature, watermark, username, blurry, artist name
output:
url: >-
images/01600-1977711466-masterpiece, best quality, 1girl,
_lora_spmatarakandef_1_ matarakandef, arthropod girl, extra arms,
antennae, cleavage, cleavage.png
- text: >-
masterpiece, best quality, 1girl, <lora:spmatarakandef:1> matarakandef,
arthropod girl, extra arms, antennae, cleavage, cleavage cutout, white
dress, navel, thighhighs, food on face, looking at viewer, open mouth, solo,
beach, sun
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit,
fewer digits, cropped, worst quality, low quality, normal quality, jpeg
artifacts, signature, watermark, username, blurry, artist name
output:
url: >-
images/01603-256858806-masterpiece, best quality, 1girl,
_lora_spmatarakandef_1_ matarakandef, arthropod girl, extra arms,
antennae, cleavage, cleavage.png
- text: >-
masterpiece, best quality, 1girl, <lora:spmatarakandef:1> matarakandef,
arthropod girl, extra arms, antennae, cleavage, cleavage cutout, white
dress, navel, thighhighs, sitting, desk, eyes closed, school
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit,
fewer digits, cropped, worst quality, low quality, normal quality, jpeg
artifacts, signature, watermark, username, blurry, artist name
output:
url: >-
images/01604-2796744409-masterpiece, best quality, 1girl,
_lora_spmatarakandef_1_ matarakandef, arthropod girl, extra arms,
antennae, cleavage, cleavage.png
- text: >-
masterpiece, best quality, 1girl, <lora:spmatarakandef:0.9> matarakandef,
arthropod girl, extra arms, antennae, cleavage, cleavage cutout, white
dress, navel, thighhighs, leaning forward, pout, street, outdoors
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit,
fewer digits, cropped, worst quality, low quality, normal quality, jpeg
artifacts, signature, watermark, username, blurry, artist name
output:
url: >-
images/01609-3838150950-masterpiece, best quality, 1girl,
_lora_spmatarakandef_0.9_ matarakandef, arthropod girl, extra arms,
antennae, cleavage, cleava.png
- text: >-
masterpiece, best quality, 1girl, <lora:spmatarakandef:1> matarakandef,
arthropod girl, extra arms, antennae, school uniform, arms behind back
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit,
fewer digits, cropped, worst quality, low quality, normal quality, jpeg
artifacts, signature, watermark, username, blurry, artist name
output:
url: >-
images/01610-3270945333-masterpiece, best quality, 1girl,
_lora_spmatarakandef_1_ matarakandef, arthropod girl, extra arms,
antennae, school uniform, ar.png
- text: >-
masterpiece, best quality, 1girl, <lora:spmatarakandef:1> matarakandef,
arthropod girl, extra arms, antennae, swimsuit, water, beach
parameters:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit,
fewer digits, cropped, worst quality, low quality, normal quality, jpeg
artifacts, signature, watermark, username, blurry, artist name
output:
url: >-
images/01613-4033188892-masterpiece, best quality, 1girl,
_lora_spmatarakandef_1_ matarakandef, arthropod girl, extra arms,
antennae, swimsuit, water, b.png
base_model: hollowstrawberry/stable-diffusion-guide
instance_prompt: >-
matarakandef, arthropod girl, extra arms, antennae, cleavage, cleavage cutout,
white dress, navel, thighhighs
license: creativeml-openrail-m
datasets:
- Hunko/VshojoMataraKan-Dataset
pipeline_tag: text-to-image
---
# Matara Kan
<Gallery />
## Model description
Matara Kan (Mat'tarakan) From VShojo!
Trained on 1 outfit, every outfit has a trigger word corresponding to the appearance of the character and suggested prompts that summons related clothes and accesories.
Works well with 0.7-1.0 weight
## Trigger words
First Outfit (Debut Outfit): `matarakandef, arthropod girl, extra arms, antennae, cleavage, cleavage cutout, white dress, navel, thighhighs`
## Download model
Weights for this model are available in Safetensors format.
[Download](/Hunko/VshojoMataraKan/tree/main) them in the Files & versions tab.
### License
This LoRA model is provided under the [CreativeML Open RAIL-M](https://raw.githubusercontent.com/CompVis/stable-diffusion/main/LICENSE) license.
## Restrictions:
- **Usage in Generation Services**: You are not allowed to use the model in any generation services without proper permission from the original creator.
- **Commercial Usage**: The sale of the model or any commercial usage is strictly prohibited without explicit written permission from the original creator.
|
signon-project/text-to-text-translator
|
signon-project
| 2024-01-26T09:28:10Z | 173 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-23T18:54:05Z |
# Model checkpoint for the text-to-text model
Refer to [this repository](https://github.com/signon-project/wp4-text2text-translation) for the code.
|
sevvalozdamar/bert-base-cased-finetuned
|
sevvalozdamar
| 2024-01-26T09:20:37Z | 93 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-21T10:59:05Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2396
- Accuracy: 0.9575
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2096 | 1.0 | 100 | 0.2535 | 0.9375 |
| 0.0835 | 2.0 | 200 | 0.2931 | 0.9425 |
| 0.0358 | 3.0 | 300 | 0.2112 | 0.9525 |
| 0.0124 | 4.0 | 400 | 0.2400 | 0.9475 |
| 0.0071 | 5.0 | 500 | 0.2241 | 0.955 |
| 0.0024 | 6.0 | 600 | 0.2342 | 0.9575 |
| 0.0035 | 7.0 | 700 | 0.2345 | 0.96 |
| 0.0005 | 8.0 | 800 | 0.2278 | 0.9575 |
| 0.0059 | 9.0 | 900 | 0.2243 | 0.96 |
| 0.0003 | 10.0 | 1000 | 0.2396 | 0.9575 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
hojzas/setfit-multilabel-test
|
hojzas
| 2024-01-26T09:18:35Z | 49 | 0 |
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"dataset:ethos",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"co2_eq_emissions",
"region:us"
] |
text-classification
| 2024-01-26T09:18:08Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- ethos
metrics:
- accuracy
widget:
- text: apparently east Asians commit less crime than us......well this is because
they are submissive conformist losers..WHITE POWER
- text: Because everyone knows this islam men is the devil
- text: Syria has a religion problem.
- text: Or maybe just don't follow degenerate sandnigger religions from the middle
east?
- text: ONLY LEGAL IMMIGRANTS... Like the hero officer Singh murdered in the line
of duty. NO TO ILLEGAL ALIENS THEY DO NOT BELONG HERE. Deport today
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
emissions: 0.4430446693845021
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
ram_total_size: 251.49160385131836
hours_used: 0.009
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: ethos
type: ethos
split: test
metrics:
- type: accuracy
value: 0.4509283819628647
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [ethos](https://huggingface.co/datasets/ethos) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [ethos](https://huggingface.co/datasets/ethos)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.4509 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("hojzas/setfit-multilabel-test")
# Run inference
preds = model("Syria has a religion problem.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 5 | 20.2344 | 182 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0063 | 1 | 0.2441 | - |
| 0.3125 | 50 | 0.1594 | - |
| 0.625 | 100 | 0.1721 | - |
| 0.9375 | 150 | 0.12 | - |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.000 kg of CO2
- **Hours Used**: 0.009 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: No GPU used
- **CPU Model**: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
- **RAM Size**: 251.49 GB
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.36.1
- PyTorch: 2.1.2+cu121
- Datasets: 2.14.7
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
SupaNova/w2v-bert-2.0-mongolian-colab-CV16.0
|
SupaNova
| 2024-01-26T09:16:26Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-25T08:54:19Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
|
akjindal53244/Mistral-7B-v0.1-Open-Platypus
|
akjindal53244
| 2024-01-26T09:15:26Z | 1,625 | 8 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-05T22:48:41Z |
---
license: apache-2.0
---
Model is instruction-finetuned using Open-Platypus dataset: https://huggingface.co/datasets/garage-bAInd/Open-Platypus
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_akjindal53244__Mistral-7B-v0.1-Open-Platypus)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 53.64 |
| ARC (25-shot) | 62.37 |
| HellaSwag (10-shot) | 85.08 |
| MMLU (5-shot) | 63.79 |
| TruthfulQA (0-shot) | 47.33 |
| Winogrande (5-shot) | 77.66 |
| GSM8K (5-shot) | 17.29 |
| DROP (3-shot) | 21.93 |
### Support My Work
Building LLMs takes time and resources; if you find my work interesting, your support would be epic!
<a href="https://www.buymeacoffee.com/a_little_learner" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
|
ginami/distilbert-base-uncased-finetuned-emotion
|
ginami
| 2024-01-26T09:15:20Z | 93 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T09:08:19Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9260951796167063
---
<!-- 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: 0.2160
- Accuracy: 0.926
- F1: 0.9261
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8118 | 1.0 | 250 | 0.3167 | 0.9065 | 0.9058 |
| 0.2434 | 2.0 | 500 | 0.2160 | 0.926 | 0.9261 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
hasiburrahman/ppo-LunarLander-v2
|
hasiburrahman
| 2024-01-26T09:13:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T09:13:32Z |
---
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: 280.56 +/- 16.93
name: mean_reward
verified: false
---
# **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
...
```
|
EliasKD/roberta-large-peft-p-tuning
|
EliasKD
| 2024-01-26T09:12:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:FacebookAI/roberta-large",
"base_model:adapter:FacebookAI/roberta-large",
"region:us"
] | null | 2024-01-24T03:22:43Z |
---
library_name: peft
base_model: roberta-large
---
# 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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### Framework versions
- PEFT 0.7.1
|
shidowake/swal-7B-base-bnb-4bit-chatml
|
shidowake
| 2024-01-26T09:10:40Z | 61 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-26T09:09:02Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
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[More Information Needed]
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[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
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
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[More Information Needed]
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|
andysalerno/fusionmixtral_sft_7Bx2_MoE
|
andysalerno
| 2024-01-26T09:02:42Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T08:56:59Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
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### 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]
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[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. -->
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## 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
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[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).
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|
MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-26T09:01:03Z | 60 | 4 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"NousResearch/Yarn-Mistral-7b-64k",
"pytorch",
"custom_code",
"en",
"dataset:emozilla/yarn-train-tokenized-16k-mistral",
"arxiv:2309.00071",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-26T08:52:13Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- NousResearch/Yarn-Mistral-7b-64k
- pytorch
- custom_code
- en
- dataset:emozilla/yarn-train-tokenized-16k-mistral
- arxiv:2309.00071
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: [MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Yarn-Mistral-7b-64k-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
|
csukuangfj/icefall-asr-librispeech-pruned-stateless-emformer-rnnt2-2022-06-01
|
csukuangfj
| 2024-01-26T08:59:35Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2022-06-01T00:17:23Z |
# Introduction
See https://github.com/k2-fsa/icefall/pull/390
|
llama-lang-adapt/pretrain-wura
|
llama-lang-adapt
| 2024-01-26T08:57:10Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:llama-lang-adapt/wura",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T08:33:00Z |
---
datasets:
- llama-lang-adapt/wura
---
We continual pre-train **meta-llama/Llama-2-7b-hf** on monolingual WURA corpus for **20 languages**. All languages are uniformly sampled.
## Important Parameters
- num_gpus: 8
- max_steps: 8000 # see [here](https://github.com/AfricanLlama/ALMA?tab=readme-ov-file#when-should-i-stop-fine-tuning-at-stage-1)
- gradient_accumulation_steps: 16
- per_device_batch_size: 2
- learning_rate: 2e-5
|
LarryAIDraw/rio_scarxzys
|
LarryAIDraw
| 2024-01-26T08:57:00Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-01-26T07:26:58Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/276275/rio-tsukatsuki-or-blue-archive
|
jeevana/mistral7b_group8QnA_26janV01
|
jeevana
| 2024-01-26T08:48:53Z | 61 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-26T07:13:33Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
|
geraldOslo/unsloth-llama-13b-radprot
|
geraldOslo
| 2024-01-26T08:43:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-24T16:31:34Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
A model fine-tuned on Norwegian prompt/response pairs relevant to the curriculum in radation physics, radation protection and radiological technology for dentistry and dental hygiene students.
It is an experimental model not yet stable enough to use in production.
## Model Details
### Model Description
## Model
The base model used is the Meta Llama 13B model ([meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)).
## Data
A dataset of prompt/response pairs about radiation protection, radiation physics, radiation biology and radiological technology as the apply in dental clinics was used to fine-tune the model. The dataset is in Norwegian and the model is fine-tuned to answer in Norwegian.
## Training
The model was trained on 6.2k prompt/response pairs from the dataset [meta-llama/Llama-2-13b-hf](https://huggingface.co/datasets/geraldOslo/RadProtDataSet) for 6 epochs on a Google Colag notebook with an A100 GPU.
The [Unsloth library](https://github.com/unslothai/unsloth) was used to train the model on a single A100 GPU.
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:** Gerald Torgersen
- **Model type:** Chat model fine-tuned
- **Language(s) (NLP):** Norwegian
- **License:** Llama 2
- **Finetuned from model [meta-llama/Llama-2-13b-hf]:**
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
For teaching and learning.
### 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]
|
Facepalm0/q-FrozenLake-v1-4x4-noSlippery
|
Facepalm0
| 2024-01-26T08:39:20Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T08:39:17Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Facepalm0/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
M4869/WavMark
|
M4869
| 2024-01-26T08:39:07Z | 0 | 4 | null |
[
"watermark",
"audio-to-audio",
"en",
"arxiv:2308.12770",
"license:mit",
"region:us"
] |
audio-to-audio
| 2023-07-31T07:19:19Z |
---
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: audio-to-audio
tags:
- watermark
---
# WavMark
> AI-based Audio Watermarking Tool
- ⚡ **Leading Stability:** The watermark resist to **10** types of common attacks like Gaussian noise, MP3 compression, low-pass filter, and speed variation; achieving over **29** times in robustness compared with the traditional method.
- 🙉 **High Imperceptibility:** The watermarked audio has over 38dB SNR and 4.3 PESQ, which means it is inaudible to humans. Listen the examples: [https://wavmark.github.io/](https://wavmark.github.io/).
- 😉 **Easy for Extending:** This project is entirely python based. You can easily leverage our underlying PyTorch model to implement a custom watermarking system with higher capacity or robustness.
- 🤗 **Huggingface Spaces:** Try our online demonstration: https://huggingface.co/spaces/M4869/WavMark
## Installation
```
pip install wavmark
```
## Basic Usage
The following code adds 16-bit watermark into the input file `example.wav` and subsequently performs decoding:
```python
import numpy as np
import soundfile
import torch
import wavmark
# 1.load model
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = wavmark.load_model().to(device)
# 2.create 16-bit payload
payload = np.random.choice([0, 1], size=16)
print("Payload:", payload)
# 3.read host audio
# the audio should be a single-channel 16kHz wav, you can read it using soundfile:
signal, sample_rate = soundfile.read("example.wav")
# Otherwise, you can use the following function to convert the host audio to single-channel 16kHz format:
# from wavmark.utils import file_reader
# signal = file_reader.read_as_single_channel("example.wav", aim_sr=16000)
# 4.encode watermark
watermarked_signal, _ = wavmark.encode_watermark(model, signal, payload, show_progress=True)
# you can save it as a new wav:
# soundfile.write("output.wav", watermarked_signal, 16000)
# 5.decode watermark
payload_decoded, _ = wavmark.decode_watermark(model, watermarked_signal, show_progress=True)
BER = (payload != payload_decoded).mean() * 100
print("Decode BER:%.1f" % BER)
```
## Low-level Access
```python
# 1.load model
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = wavmark.load_model().to(device)
# 2. take 16,000 samples
signal, sample_rate = soundfile.read("example.wav")
trunck = signal[0:16000]
message_npy = np.random.choice([0, 1], size=32)
# 3. do encode:
with torch.no_grad():
signal = torch.FloatTensor(trunck).to(device)[None]
message_tensor = torch.FloatTensor(message_npy).to(device)[None]
signal_wmd_tensor = model.encode(signal, message_tensor)
signal_wmd_npy = signal_wmd_tensor.detach().cpu().numpy().squeeze()
# 4.do decode:
with torch.no_grad():
signal = torch.FloatTensor(signal_wmd_npy).to(device).unsqueeze(0)
message_decoded_npy = (model.decode(signal) >= 0.5).int().detach().cpu().numpy().squeeze()
BER = (message_npy != message_decoded_npy).mean() * 100
print("BER:", BER)
```
## Thanks
The "[Audiowmark](https://uplex.de/audiowmark)" developed by Stefan Westerfeld has provided valuable ideas for the design of this project.
## Citation
```
@misc{chen2023wavmark,
title={WavMark: Watermarking for Audio Generation},
author={Guangyu Chen and Yu Wu and Shujie Liu and Tao Liu and Xiaoyong Du and Furu Wei},
year={2023},
eprint={2308.12770},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
|
mingli/optaeg-v1-fashionminst-tiny-49k
|
mingli
| 2024-01-26T08:38:54Z | 0 | 0 | null |
[
"image-classification",
"dataset:fashion_mnist",
"license:mit",
"region:us"
] |
image-classification
| 2024-01-26T08:04:13Z |
---
license: mit
datasets:
- fashion_mnist
metrics:
- accuracy
pipeline_tag: image-classification
---
A tiny fashion-mnist model to demostrate the potential of the learnable activation - OptAEG-V1.
The model can reach 90.2% accuracy with only 48.5k parameters.
The OptAEG-V1 learnable activation is based on a theory of Arithmetic Expression Geometry which is still in developing.
Please visit the draft papers on [theory](https://github.com/mountain/aeg-paper) and [neural networks](https://github.com/mountain/optim-aeg) for a reference
|
MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-26T08:37:23Z | 57 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"maywell/Synatra-V0.1-7B-Instruct",
"pytorch",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"license:apache-2.0",
"base_model:MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-26T08:28:29Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- maywell/Synatra-V0.1-7B-Instruct
- pytorch
- ko
- license:cc-by-nc-4.0
- autotrain_compatible
- endpoints_compatible
- region:us
- license:apache-2.0
model_name: Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
|
Crystalcareai/CrystalMistral_7b_v.01
|
Crystalcareai
| 2024-01-26T08:31:30Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Open-Orca/Mistral-7B-OpenOrca",
"Crystalcareai/CrystalMistral-Evol",
"conversational",
"base_model:Crystalcareai/CrystalMistral-Evol",
"base_model:merge:Crystalcareai/CrystalMistral-Evol",
"base_model:Open-Orca/Mistral-7B-OpenOrca",
"base_model:merge:Open-Orca/Mistral-7B-OpenOrca",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T08:23:53Z |
---
tags:
- merge
- mergekit
- lazymergekit
- Open-Orca/Mistral-7B-OpenOrca
- Crystalcareai/CrystalMistral-Evol
base_model:
- Open-Orca/Mistral-7B-OpenOrca
- Crystalcareai/CrystalMistral-Evol
---
# CrystalMistral_7b_v.01
CrystalMistral_7b_v.01 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
* [Crystalcareai/CrystalMistral-Evol](https://huggingface.co/Crystalcareai/CrystalMistral-Evol)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Open-Orca/Mistral-7B-OpenOrca
layer_range: [0, 32]
- model: Crystalcareai/CrystalMistral-Evol
layer_range: [0, 32]
merge_method: slerp
base_model: Open-Orca/Mistral-7B-OpenOrca
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 = "Crystalcareai/CrystalMistral_7b_v.01"
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"])
```
|
amd/yolov5s
|
amd
| 2024-01-26T08:29:01Z | 0 | 2 | null |
[
"onnx",
"RyzenAI",
"object-detection",
"vision",
"YOLO",
"Pytorch",
"dataset:COCO",
"license:apache-2.0",
"region:us"
] |
object-detection
| 2023-12-04T08:25:34Z |
---
license: apache-2.0
tags:
- RyzenAI
- object-detection
- vision
- YOLO
- Pytorch
datasets:
- COCO
metrics:
- mAP
---
# YOLOv5s model trained on COCO
YOLOv5s is the small version of YOLOv5 model trained on COCO object detection (118k annotated images) at resolution 640x640. It was released in [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5).
We develop a modified version that could be supported by [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html).
## Model description
YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
## Intended uses & limitations
You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=amd/yolov5) to look for all available YOLOv5 models.
## How to use
### Installation
Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI.
Run the following script to install pre-requisites for this model.
```bash
pip install -r requirements.txt
```
### Data Preparation (optional: for accuracy evaluation)
The dataset MSCOCO2017 contains 118287 images for training and 5000 images for validation.
Download COCO dataset and create directories in your code like this:
```plain
└── datasets
└── coco
├── annotations
| ├── instances_val2017.json
| └── ...
├── labels
| ├── val2017
| | ├── 000000000139.txt
| ├── 000000000285.txt
| └── ...
├── images
| ├── val2017
| | ├── 000000000139.jpg
| ├── 000000000285.jpg
└── val2017.txt
```
1. put the val2017 image folder under images directory or use a softlink
2. the labels folder and val2017.txt above are generate by **general_json2yolo.py**
3. modify the coco.yaml like this:
```markdown
path: /path/to/your/datasets/coco # dataset root dir
train: train2017.txt # train images (relative to 'path') 118287 images
val: val2017.txt # val images (relative to 'path') 5000 images
```
### Test & Evaluation
- Code snippet from [`infer_onnx.py`](infer_onnx.py) on how to use
```python
args = make_parser().parse_args()
onnx_path = args.onnx_model
onnx_weight = onnxruntime.InferenceSession(onnx_path)
grid = np.load("./grid.npy", allow_pickle=True)
anchor_grid = np.load("./anchor_grid.npy", allow_pickle=True)
path = args.image_path
new_path = args.output_path
conf_thres, iou_thres, classes, agnostic_nms, max_det = 0.25, 0.45, None, False, 1000
img0 = cv2.imread(path)
img = pre_process(img0)
onnx_input = {onnx_weight.get_inputs()[0].name: img}
onnx_output = onnx_weight.run(None, onnx_input)
onnx_output = post_process(onnx_output)
pred = non_max_suppression(
onnx_output[0], conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det
)
colors = Colors()
det = pred[0]
im0 = img0.copy()
annotator = Annotator(im0, line_width=2, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Write results
for *xyxy, conf, cls in reversed(det):
c = int(cls) # integer class
label = f"{names[c]} {conf:.2f}"
annotator.box_label(xyxy, label, color=colors(c, True))
# Stream results
im0 = annotator.result()
cv2.imwrite(new_path, im0)
```
- Run inference for a single image
```python
python infer_onnx.py --onnx_model ./yolov5s.onnx -i /Path/To/Your/Image --ipu --provider_config /Path/To/Your/Provider_config
```
*Note: __vaip_config.json__ is located at the setup package of Ryzen AI (refer to [Installation](#installation))*
- Test accuracy of the quantized model
```python
python eval_onnx.py --onnx_model ./yolov5s.onnx --ipu --provider_config /Path/To/Your/Provider_config
```
### Performance
|Metric |Accuracy on IPU|
| :----: | :----: |
|AP\@0.50:0.95|0.356|
```bibtex
@software{glenn_jocher_2021_5563715,
author = {Glenn Jocher et. al.},
title = {{ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models,
Roboflow integration, TensorFlow export, OpenCV
DNN support}},
month = oct,
year = 2021,
publisher = {Zenodo},
version = {v6.0},
doi = {10.5281/zenodo.5563715},
url = {https://doi.org/10.5281/zenodo.5563715}
}
```
|
NLUHOPOE/Mistral-test-case-3
|
NLUHOPOE
| 2024-01-26T08:25:24Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-24T02:03:11Z |
---
license: apache-2.0
datasets:
- Open-Orca/OpenOrca
language:
- en
---
# Model Details
* Model Description: This model is test for data ordering.
* Developed by: Juhwan Lee
* Model Type: Large Language Model
# Model Architecture
This model is based on Mistral-7B-v0.1. We fine-tuning this model for data ordering task.
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
# Dataset
We random sample Open-Orca dataset. (We finetune the 100,000 dataset)
# Guthub
https://github.com/trailerAI
# License
Apache License 2.0
|
Lianghanxin/Aa
|
Lianghanxin
| 2024-01-26T08:23:43Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2024-01-26T08:23:43Z |
---
license: bigscience-openrail-m
---
|
DanielClough/Candle_phi-2
|
DanielClough
| 2024-01-26T08:22:03Z | 55 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"phi",
"text-generation",
"custom_code",
"en",
"dataset:microsoft/phi-2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T05:22:00Z |
---
datasets:
- microsoft/phi-2
language:
- en
pipeline_tag: text-generation
license: mit
---
This repo includes `.gguf` built for HuggingFace/Candle.
They will not work with `llama.cpp`.
Refer to the [original repo](https://huggingface.co/microsoft/phi-2) for more details.
|
DooDooHyun/AIFT-Yi-Ko-6B-ao-instruct-all-v0.64
|
DooDooHyun
| 2024-01-26T08:19:32Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:beomi/Yi-Ko-6B",
"base_model:finetune:beomi/Yi-Ko-6B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T07:30:49Z |
---
license: cc-by-nc-4.0
base_model: beomi/Yi-Ko-6B
tags:
- generated_from_trainer
model-index:
- name: AIFT-Yi-Ko-6B-ao-instruct-all-v0.64
results: []
---
<!-- 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. -->
# AIFT-Yi-Ko-6B-ao-instruct-all-v0.64
This model is a fine-tuned version of [beomi/Yi-Ko-6B](https://huggingface.co/beomi/Yi-Ko-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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.0.0
- Tokenizers 0.15.0
|
DanielClough/Candle_phi-1_5
|
DanielClough
| 2024-01-26T08:17:40Z | 115 | 0 |
transformers
|
[
"transformers",
"gguf",
"phi",
"text-generation",
"custom_code",
"en",
"dataset:microsoft/phi-1_5",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T05:20:49Z |
---
datasets:
- microsoft/phi-1_5
language:
- en
pipeline_tag: text-generation
license: mit
---
This repo includes `.gguf` built for HuggingFace/Candle.
They will not work with `llama.cpp`.
Refer to the [original repo](https://huggingface.co/microsoft/phi-1_5) for more details.
|
YingJie0202/Llama-2-7b-chat-hf_finetune
|
YingJie0202
| 2024-01-26T08:15:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-26T04:27:01Z |
---
library_name: peft
base_model: NousResearch/Llama-2-7b-chat-hf
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
### Framework versions
- PEFT 0.7.1
|
vierlinglukas/PyramidsRND
|
vierlinglukas
| 2024-01-26T08:14:10Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-01-26T08:14:09Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **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: vierlinglukas/PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DanielClough/Candle_phi-1
|
DanielClough
| 2024-01-26T08:10:36Z | 57 | 0 |
transformers
|
[
"transformers",
"gguf",
"phi",
"text-generation",
"custom_code",
"en",
"dataset:microsoft/phi-1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T05:44:45Z |
---
datasets:
- microsoft/phi-1
language:
- en
pipeline_tag: text-generation
license: mit
---
This repo includes `.gguf` built for HuggingFace/Candle.
They will not work with `llama.cpp`.
Refer to the [original repo](https://huggingface.co/microsoft/phi-1) for more details.
|
vierlinglukas/ppo-SnowballTarget
|
vierlinglukas
| 2024-01-26T08:06:25Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-01-26T08:06:21Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
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: vierlinglukas/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
lycaoduong/ko2vn
|
lycaoduong
| 2024-01-26T07:55:37Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ko",
"vi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-25T06:26:23Z |
---
license: apache-2.0
language:
- ko
- vi
---
# Ko-Vi-Translate-Machine
This project creates a machine learning model to translate Korean into Vietnamese for certain tasks. This is a project that goes from zero to product.
## For some personal reasons, we cannot provide training data.
## After running ads on Facebook for about $20 within 3 days, we had nearly 600 translations during that period.
|
harborwater/wizard-orca-3b
|
harborwater
| 2024-01-26T07:53:34Z | 1,473 | 5 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:pankajmathur/WizardLM_Orca",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-06T20:24:01Z |
---
language:
- en
license: apache-2.0
library_name: transformers
datasets:
- pankajmathur/WizardLM_Orca
model-index:
- name: wizard-orca-3b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 41.72
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=harborwater/wizard-orca-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 71.78
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=harborwater/wizard-orca-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 24.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=harborwater/wizard-orca-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 40.04
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=harborwater/wizard-orca-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.93
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=harborwater/wizard-orca-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 1.06
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=harborwater/wizard-orca-3b
name: Open LLM Leaderboard
---
Trained on 2 epoch of pankajmathur's WizardLM_orca dataset.
This is an open llama derivative.
Prompt template:
```
### HUMAN:
{prompt}
### RESPONSE:
<leave a newline for the model to answer>
```
[<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)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__wizard-orca-3b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |41.00|
|AI2 Reasoning Challenge (25-Shot)|41.72|
|HellaSwag (10-Shot) |71.78|
|MMLU (5-Shot) |24.49|
|TruthfulQA (0-shot) |40.04|
|Winogrande (5-shot) |66.93|
|GSM8k (5-shot) | 1.06|
|
EnlightenedAI/TCSI_pp_zh
|
EnlightenedAI
| 2024-01-26T07:43:04Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-08-25T05:44:36Z |
---
license: apache-2.0
---
# CAPP-130: A Corpus of Chinese Application Privacy Policy Summarization and Interpretation.
## Introduction
A privacy policy serves as an online internet protocol crafted by service providers, which details how service providers collect, process, store, manage, and use personal information when users engage with applications.
However, these privacy policies are often filled with technobabble and legalese, making them 'incomprehensible'.
As a result, users often agree to all terms unknowingly, even some terms may conflict with the law, thereby posing a considerable risk to personal privacy information.
To tackle these challenges, we introduce a fine-grained CAPP-130 corpus and a TCSI-pp framework.
CAPP-130 contains $130$ Chinese privacy policies from popular applications that have been carefully annotated and interpreted by legal experts, resulting in $52,489$ annotations and $20,555$ rewritten sentences.
TCSI-pp first extracts sentences related to the topic specified by users and then uses a generative model to rewrite the sentences into comprehensible summarization. Built upon TSCI-pp, we construct a summarization tool TSCI-pp-zh by selecting RoBERTa from six classification models for sentence extraction and selecting mT5 from five generative models for sentence rewriting.
Code: [here](https://github.com/EnlightenedAI/CAPP-130)
## Environment
Project dependencies can be installed in the following ways:
```
pip install -r requirements.txt
```
Equipment: A100 *2
## Chinese Application Privacy Policy Corpus (CAPP-130)
CAPP-130 contains $130$ Chinese privacy policies from popular applications that have been carefully annotated and interpreted by legal experts, resulting in $52,489$ annotations and $19,570$ rewritten sentences.
### Basic Statistics of Corpus CAPP-130
The guide for [Paper](Documents) and Annotation Guidelines ([Chinese version](Documents/Annotation_Guidelines_Chinese_Version.pdf), [English version](Documents/Annotation_Guidelines_English_Version.pdf)) explains the tags and the process of annotation, which can be found in the Documents.
Currently, the Annotation Guidelines are available only in Chinese, but we are working on translating them into English.
Table 1 shows the basic statistical information of CAPP-130, and Table 2 shows the pre-sliced data information used for TCSI-pp. They are stored in the CAPP-130 Corpus.
Table 1: Basic Statistics of Corpus CAPP-130.
| Data Practice Categories | Quantity | Percentage (\%) | Median | Mea |
|------------------------------|----------|-----------------|---------|----|
| Information Collection | 6967 | 17.9 | 58 | 70 |
| Permission Acquisition | 1852 | 4.8 | 54 | 62 |
| Sharing and Disclosure | 4740 | 12.2 | 52 | 63 |
| Usage | 3589 | 9.2 | 64 | 75 |
| Storage | 1360 | 3.5 | 41 | 46 |
| Security Measures | 3000 | 7.7 | 53 | 60 |
| Special Audiences | 1416 | 3.6 | 54 | 60 |
| Management | 5324 | 13.7 | 43 | 49 |
| Contact Information | 712 | 1.8 | 41 | 54 |
| Authorization and Revisions | 1049 | 2.7 | 35 | 43 |
| Cessation of Operations | 110 | 0.3 | 64 | 68 |
| Important | 20555 | 52.8 | 52 | 61 |
| Risks | 1815 | 4.7 | 40 | 46 |
Table 2: The pre-sliced data from CAPP-130 is used to train TCSI-pp.
| sub dataset | train samples | validation samples | test samples |
|----------------------------------|---------------|--------------------|--------------|
| important_identification_dataset | 27222 | 5833 | 5834 |
| risk_identification_dataset | 14338 | 3083 | 3084 |
| topic_identification_dataset | 14190 | 3043 | 3035 |
| rewritten_sentences | 15656 | 1957 | 1957 |
## Topic-Controlled Framework for Summarization and Interpretation of Privacy Policy (TCSI-pp)
we provide a Topic-Controlled Framework for Summarization and Interpretation of Privacy Policy (TCSI-pp). Unlike previous methods that only extract specific sentences, TCSI-pp first retrieves relevant sentences based on the topics chosen from data practice categories by users using a classification model. Then, a generative model is used to rewrite these sentences clearly and concisely for the understanding of the general public, with potentially risky sentences emphasized.
### Information Extraction
These are specifically utilized for binary classification models such as "Important Identification" and "Risk Identification", as well as multi-classification models like "Topic Identification".
#### How to use
The model is placed in the XXX_pretain (where XXX is the model name) directory and each directory contains three files:
- pytorch_model.bin
- bert_config.json
- vocab.txt
Pre-trained model download address from [here](https://github.com/huggingface).
After decompression, put it in the corresponding directory according to the above, and confirm the file name is correct.
We have independently acquired three sets of classification benchmarks from six different models: RoBERTa, BERT, mBERT, sBERT, Pert, and ERNIE.
You can be used in the following ways:
```
# Train and test binary classification model:
python run.py --model 'model_name' --data 'data_name'
# Train and test multi-classification model:
python run_multi.py --model 'model_name' --data 'data_name'
```
Please note that the above code examples are for illustrative purposes only and you may need to make appropriate adjustments based on your specific situation.
#### Baselines
We provide classification baselines for "Important Identification", "Risk Identification", and "Topic Identification". They are respectively trained and tested on the 'important_identification_dataset', 'risk_identification_dataset', and 'topics_identification_dataset' in the sub-dataset. Table 3 displays the evaluation metrics of six models.
Table 3: Evaluation Metrics with F1 for Classification Models.
| Methods | topic-Micro | topic-Macro | important-Micro | important-Macro |risk-Micro | risk-Macro |
|------------------|------------|----------|----------|------|------|------|
| RoBERTa |**0.819**|**0.841**|**0.897**|**0.899**|0.920 | 0.711|
|Bert |0.802 |0.820 |0.895 |0.896 |0.921 |0.719|
|mBERT |0.809 |0.821 |0.889 |0.889 |0.918 |0.709 |
|SBERT |0.781 |0.794 | 0.875 |0.874 |0.917 |0.689|
|PERT |0.801 |0.812 | 0.895 |0.897 |**0.922** |**0.716**|
|ERNIE |0.807 |0.821 | 0.895 |0.896 |0.921 | 0.702|
**(New)** We will upload all model parameters to [here](https://huggingface.co/EnlightenedAI/TCSI_pp_zh/tree/main).
### Rewritten Sentences
A generative model is used to rewrite these sentences clearly and concisely for the understanding of the general public, with potentially risky sentences emphasized.
#### How to use
For rewriting sentences, we fine-tuned the following models based on the transformer encoder-decoder architecture: mT5, Bert2Bert, Bert2gpt, RoBerta2gpt, and ERNIE2gpt. These models were initialized with parameters from publicly available models, such as mT5-small, Bert-base-Chinese, ernie-3.0-base-zh, chinese-roberta-wwm-ext, and gpt2-base-chinese. These models can be found on [Hugging Face](https://huggingface.co/) model repository.
You can be used in the following ways:
```
# train and test:
python model_name.py
#The model_name needs to be changed to mT5, Bert2Bert, Bert2gpt, RoBerta2gpt, or ERNIE2gpt.
```
Please note that the above code examples are for illustrative purposes only and you may need to make appropriate adjustments based on your specific situation.
#### Baselines
Table 4 displays the ROUGE, Bert-score, Bart-score, and Carburacy evaluation metrics for these models:
Table 4: Evaluation metrics for the rewrite models.
| Methods | rouge-1 | rouge-2 | rouge-l | Bert-score | Bart-score | Carburacy |
|--------------|-------|-------|----------|----------|------------|-----------|
| mT5 | **0.753** | **0.609** | **0.733** | **0.888** | **-4.577** | **0.833** |
| RoBERTa2gpt | 0.749 |0.577 | 0.719 | 0.872 | -4.975 | 0.755 |
| Bert2bert | 0.718 |0.535 | 0.689 | 0.864 | -5.020 | 0.747 |
| Bert2gpt | 0.751 |0.574 | 0.720 | 0.872 | -4.964 | 0.764 |
| ERNIE2gpt | 0.623 |0.406 | 0.581 | 0.809 | -5.716 | 0.715 |
**(New)** We will upload all model parameters to [here](https://huggingface.co/EnlightenedAI/TCSI_pp_zh/tree/main).
## Chinese application privacy policy summary tool (TCSI-pp-zh)
we select the most effective RoBERTa and mT5 to implement the Chinese application privacy policy summary tool (TCSI-pp-zh). Experiments on real privacy policies show that TCSI-pp-zh is superior over GPT-4 and other models, demonstrating higher readability and reliability in the task of summarizing Chinese application privacy policies.
### How to use
You can be used in the following ways:
```
# train and test:
python ./TCSI_pp_zh/TCSI_pp_zh.py --binary_model 'binary_model_name' --multi_model 'multi_model_name' --rewrite_model 'rewrite_model_name' --topic_list 'choose_a_topic_list' --data 'a_privacy_policy'
```
Please note that the above code examples are for illustrative purposes only and you may need to make appropriate adjustments based on your specific situation.
### Effect Demonstration
Figure 1 displays the summarization of GPT-4 and TCSI-pp-zh in a privacy policy, where text having the same background color represents descriptions of the same part of the privacy policy generated by different algorithms; red text emphasizes incorrect content produced in the summary.
Figure 1: Summarization of GPT-4 and TCSI-pp-zh.
## citation
If you use the data or code of this project, or if our work is helpful to you, please state the citation
```
@inproceedings{
zhu2023capp,
title={{CAPP}-130: A Corpus of Chinese Application Privacy Policy Summarization and Interpretation},
author={Pengyun Zhu and Long Wen and Jinfei Liu and Feng Xue and Jian Lou and Zhibo Wang and Kui Ren},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=OyTIV57Prb}
}
```
## Update
We will continue to update this repository on GitHub.
|
epinnock/deepseek-coder-33-evol-feedback-v1-r512
|
epinnock
| 2024-01-26T07:42:16Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-26T07:38:50Z |
---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
### 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]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Alikatana/htrrr
|
Alikatana
| 2024-01-26T07:41:34Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2024-01-26T07:41:34Z |
---
license: other
license_name: lice
license_link: LICENSE
---
|
Andrewwwwww/MythoMax-L2-13B-GGUF
|
Andrewwwwww
| 2024-01-26T07:37:44Z | 188 | 1 |
transformers
|
[
"transformers",
"gguf",
"llama",
"en",
"base_model:Gryphe/MythoMax-L2-13b",
"base_model:quantized:Gryphe/MythoMax-L2-13b",
"license:other",
"region:us"
] | null | 2024-01-26T07:36:14Z |
---
language:
- en
license: other
model_name: MythoMax L2 13B
base_model: Gryphe/MythoMax-L2-13b
inference: false
model_creator: Gryphe
model_type: llama
prompt_template: '```
{system_message}
### Instruction:
{prompt}
(For roleplay purposes, I suggest the following - Write <CHAR NAME>''s next reply
in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.)
### Response:
```
'
quantized_by: TheBloke
---
<!-- header start -->
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# MythoMax L2 13B - GGUF
- Model creator: [Gryphe](https://huggingface.co/Gryphe)
- Original model: [MythoMax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Gryphe's MythoMax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/MythoMax-L2-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF)
* [Gryphe's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Gryphe/MythoMax-L2-13b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Custom
```
{system_message}
### Instruction:
{prompt}
(For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.)
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Gryphe's MythoMax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mythomax-l2-13b.Q2_K.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [mythomax-l2-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [mythomax-l2-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [mythomax-l2-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [mythomax-l2-13b.Q4_0.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mythomax-l2-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [mythomax-l2-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [mythomax-l2-13b.Q5_0.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mythomax-l2-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [mythomax-l2-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [mythomax-l2-13b.Q6_K.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [mythomax-l2-13b.Q8_0.gguf](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/blob/main/mythomax-l2-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/MythoMax-L2-13B-GGUF and below it, a specific filename to download, such as: mythomax-l2-13b.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/MythoMax-L2-13B-GGUF mythomax-l2-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/MythoMax-L2-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/MythoMax-L2-13B-GGUF mythomax-l2-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m mythomax-l2-13b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/MythoMax-L2-13B-GGUF", model_file="mythomax-l2-13b.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run 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**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Gryphe's MythoMax L2 13B
An improved, potentially even perfected variant of MythoMix, my [MythoLogic-L2](https://huggingface.co/Gryphe/MythoLogic-L2-13b) and [Huginn](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16) merge using a highly experimental tensor type merge technique. The main difference with MythoMix is that I allowed more of Huginn to intermingle with the single tensors located at the front and end of a model, resulting in increased coherency across the entire structure.
The script and the acccompanying templates I used to produce both can [be found here](https://github.com/Gryphe/BlockMerge_Gradient/tree/main/YAML).
This model is proficient at both roleplaying and storywriting due to its unique nature.
Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) (You're the best!)
## Model details
The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to have resulted in a model that exceeds at both, confirming my theory. (More details to be released at a later time)
This type of merge is incapable of being illustrated, as each of its 363 tensors had an unique ratio applied to it. As with my prior merges, gradients were part of these ratios to further finetune its behaviour.
## Prompt Format
This model primarily uses Alpaca formatting, so for optimal model performance, use:
```
<System prompt/Character Card>
### Instruction:
Your instruction or question here.
For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.
### Response:
```
---
license: other
---
<!-- original-model-card end -->
|
csukuangfj/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
|
csukuangfj
| 2024-01-26T07:30:53Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2024-01-26T07:22:09Z |
# Introduction
This repo is forked from
https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
See https://github.com/k2-fsa/icefall/pull/440
This model use the following setup:
* length of chunk is 32 frames (i.e., 0.32s)
* length of right context is 8 frames (i.e., 0.08s)
|
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