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devgpt/killjoybr
|
devgpt
| 2024-06-26T14:12:39Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2024-06-26T14:12:39Z |
---
license: openrail
---
|
yard1/SangBinCho
|
yard1
| 2024-06-29T01:22:41Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2024-06-26T14:13:36Z |
Entry not found
|
RoyMusthang/test
|
RoyMusthang
| 2024-06-26T14:17:17Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2024-06-26T14:17:17Z |
---
license: mit
---
|
Gyukro/tttt
|
Gyukro
| 2024-06-26T14:17:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:17:29Z |
Entry not found
|
shahzaib201/OEL
|
shahzaib201
| 2024-06-26T14:19:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:19:52Z |
Entry not found
|
neeraj1bh/magicanimate
|
neeraj1bh
| 2024-06-28T13:08:17Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:2311.16498",
"region:us"
] | null | 2024-06-26T14:20:29Z |
<!-- # magic-edit.github.io -->
<p align="center">
<h2 align="center">MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model</h2>
<p align="center">
<a href="https://scholar.google.com/citations?user=-4iADzMAAAAJ&hl=en"><strong>Zhongcong Xu</strong></a>
·
<a href="http://jeff95.me/"><strong>Jianfeng Zhang</strong></a>
·
<a href="https://scholar.google.com.sg/citations?user=8gm-CYYAAAAJ&hl=en"><strong>Jun Hao Liew</strong></a>
·
<a href="https://hanshuyan.github.io/"><strong>Hanshu Yan</strong></a>
·
<a href="https://scholar.google.com/citations?user=stQQf7wAAAAJ&hl=en"><strong>Jia-Wei Liu</strong></a>
·
<a href="https://zhangchenxu528.github.io/"><strong>Chenxu Zhang</strong></a>
·
<a href="https://sites.google.com/site/jshfeng/home"><strong>Jiashi Feng</strong></a>
·
<a href="https://sites.google.com/view/showlab"><strong>Mike Zheng Shou</strong></a>
<br>
<br>
<a href="https://arxiv.org/abs/2311.16498"><img src='https://img.shields.io/badge/arXiv-MagicAnimate-red' alt='Paper PDF'></a>
<a href='https://showlab.github.io/magicanimate'><img src='https://img.shields.io/badge/Project_Page-MagicAnimate-green' alt='Project Page'></a>
<a href='https://huggingface.co/spaces/zcxu-eric/magicanimate'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
<br>
<b>National University of Singapore | ByteDance</b>
</p>
<table align="center">
<tr>
<td>
<img src="assets/teaser/t4.gif">
</td>
<td>
<img src="assets/teaser/t2.gif">
</td>
</tr>
</table>
## 📢 News
* **[2023.12.4]** Release inference code and gradio demo. We are working to improve MagicAnimate, stay tuned!
* **[2023.11.23]** Release MagicAnimate paper and project page.
## 🏃♂️ Getting Started
Download the pretrained base models for [StableDiffusion V1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and [MSE-finetuned VAE](https://huggingface.co/stabilityai/sd-vae-ft-mse).
Download our MagicAnimate [checkpoints](https://huggingface.co/zcxu-eric/MagicAnimate).
Please follow the huggingface download instructions to download the above models and checkpoints, `git lfs` is recommended.
Place the based models and checkpoints as follows:
```bash
magic-animate
|----pretrained_models
|----MagicAnimate
|----appearance_encoder
|----diffusion_pytorch_model.safetensors
|----config.json
|----densepose_controlnet
|----diffusion_pytorch_model.safetensors
|----config.json
|----temporal_attention
|----temporal_attention.ckpt
|----sd-vae-ft-mse
|----config.json
|----diffusion_pytorch_model.safetensors
|----stable-diffusion-v1-5
|----scheduler
|----scheduler_config.json
|----text_encoder
|----config.json
|----pytorch_model.bin
|----tokenizer (all)
|----unet
|----diffusion_pytorch_model.bin
|----config.json
|----v1-5-pruned-emaonly.safetensors
|----...
```
## ⚒️ Installation
prerequisites: `python>=3.8`, `CUDA>=11.3`, and `ffmpeg`.
Install with `conda`:
```bash
conda env create -f environment.yaml
conda activate manimate
```
or `pip`:
```bash
pip3 install -r requirements.txt
```
## 💃 Inference
Run inference on single GPU:
```bash
bash scripts/animate.sh
```
Run inference with multiple GPUs:
```bash
bash scripts/animate_dist.sh
```
## 🎨 Gradio Demo
#### Online Gradio Demo:
Try our [online gradio demo](https://huggingface.co/spaces/zcxu-eric/magicanimate) quickly.
#### Local Gradio Demo:
Launch local gradio demo on single GPU:
```bash
python3 -m demo.gradio_animate
```
Launch local gradio demo if you have multiple GPUs:
```bash
python3 -m demo.gradio_animate_dist
```
Then open gradio demo in local browser.
## 🙏 Acknowledgements
We would like to thank [AK(@_akhaliq)](https://twitter.com/_akhaliq?lang=en) and huggingface team for the help of setting up oneline gradio demo.
## 🎓 Citation
If you find this codebase useful for your research, please use the following entry.
```BibTeX
@inproceedings{xu2023magicanimate,
author = {Xu, Zhongcong and Zhang, Jianfeng and Liew, Jun Hao and Yan, Hanshu and Liu, Jia-Wei and Zhang, Chenxu and Feng, Jiashi and Shou, Mike Zheng},
title = {MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model},
booktitle = {arXiv},
year = {2023}
}
```
|
Temo27Anas/videomae-base-finetuned-ucf101-subset-200frames
|
Temo27Anas
| 2024-06-26T14:21:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:21:22Z |
Entry not found
|
advokat/chimera-SDXL
|
advokat
| 2024-06-29T06:36:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:22:21Z |
Entry not found
|
Elahe96/dqn-SpaceInvadersNoFrameskip-v4
|
Elahe96
| 2024-06-26T14:24:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:24:30Z |
Entry not found
|
sibikrish/creditcard_rf
|
sibikrish
| 2024-06-26T14:26:30Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2024-06-26T14:26:30Z |
---
license: mit
---
|
pengphy/Seed-TTS
|
pengphy
| 2024-06-26T14:28:01Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-06-26T14:28:01Z |
---
license: apache-2.0
---
|
greenbureau/intentsensor-01.3
|
greenbureau
| 2024-06-26T21:14:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T14:32:30Z |
---
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]
|
kugelitsjust/Meow_Skulls_Solo
|
kugelitsjust
| 2024-06-26T14:35:45Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2024-06-26T14:34:31Z |
---
license: openrail
---
|
LinxuanPastel/takoyana
|
LinxuanPastel
| 2024-06-26T15:04:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:34:36Z |
Entry not found
|
HoangCong/Vistral-7B-Chat-Finetune-Version2
|
HoangCong
| 2024-06-27T02:25:00Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-06-26T14:36:44Z |
---
license: apache-2.0
---
|
acarrillo48/mistral-version-big-starion-test-a
|
acarrillo48
| 2024-06-26T14:37:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:37:16Z |
Entry not found
|
blackhole33/whisper-small-uzv2
|
blackhole33
| 2024-06-26T14:40:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:40:53Z |
Entry not found
|
Nidhi-choudhary/dqn-SpaceInvadersNoFrameskip-v4
|
Nidhi-choudhary
| 2024-06-26T14:40:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:40:53Z |
Entry not found
|
jonghhhh/checkpoints
|
jonghhhh
| 2024-06-26T14:46:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:43:25Z |
Entry not found
|
davelotito/donut_experiment_bayesian_trial_11
|
davelotito
| 2024-06-26T15:32:41Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T14:44:39Z |
---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_bayesian_trial_11
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. -->
# donut_experiment_bayesian_trial_11
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4496
- Bleu: 0.0662
- Precisions: [0.8162839248434238, 0.7440758293838863, 0.7013698630136986, 0.6623376623376623]
- Brevity Penalty: 0.0908
- Length Ratio: 0.2942
- Translation Length: 479
- Reference Length: 1628
- Cer: 0.7615
- Wer: 0.8328
## 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: 0.00012678733283601488
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.5513 | 1.0 | 253 | 0.5209 | 0.0666 | [0.7287128712871287, 0.6450892857142857, 0.578005115089514, 0.5269461077844312] | 0.1082 | 0.3102 | 505 | 1628 | 0.7584 | 0.8461 |
| 0.2612 | 2.0 | 506 | 0.5017 | 0.0648 | [0.7962577962577962, 0.7287735849056604, 0.6784741144414169, 0.6225806451612903] | 0.0921 | 0.2955 | 481 | 1628 | 0.7542 | 0.8349 |
| 0.1638 | 3.0 | 759 | 0.4666 | 0.0615 | [0.7995824634655533, 0.6990521327014217, 0.6356164383561644, 0.5909090909090909] | 0.0908 | 0.2942 | 479 | 1628 | 0.7636 | 0.8392 |
| 0.059 | 4.0 | 1012 | 0.4496 | 0.0662 | [0.8162839248434238, 0.7440758293838863, 0.7013698630136986, 0.6623376623376623] | 0.0908 | 0.2942 | 479 | 1628 | 0.7615 | 0.8328 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1
|
Achraf1313/dataset_project
|
Achraf1313
| 2024-07-01T13:05:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:44:46Z |
Entry not found
|
kartashoffv/ods_text_classification
|
kartashoffv
| 2024-06-26T14:47:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:47:48Z |
Entry not found
|
Aung2024/gpt2-qa-model
|
Aung2024
| 2024-06-26T14:50:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T14:49:45Z |
---
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]
|
samvelkoch/tricky-4ep-kevsun
|
samvelkoch
| 2024-06-26T14:50:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-06-26T14:49:56Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [KevSun/Engessay_grading_ML](https://huggingface.co/KevSun/Engessay_grading_ML)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.40.2
```
Also make sure you are providing your huggingface token if the model is lying in a private repo.
- You can login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
You will also need to download the classification head, either manually, or by running the following code:
```python
from huggingface_hub import hf_hub_download
model_name = "samvelkoch/tricky-4ep-kevsun" # either local folder or huggingface model name
hf_hub_download(repo_id=model_name, filename="classification_head.pth", local_dir="./")
```
You can make classification predictions by following the example below:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "samvelkoch/tricky-4ep-kevsun" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "How are you?"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
).cuda().eval()
head_weights = torch.load("classification_head.pth", map_location="cuda")
# settings can be arbitrary here as we overwrite with saved weights
head = torch.nn.Linear(1, 1, bias=False).to("cuda")
head.weight.data = head_weights
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
out = model(**inputs).logits
logits = head(out[:,-1])
print(logits)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
RobertaForCausalLM(
(roberta): RobertaModel(
(embeddings): RobertaEmbeddings(
(word_embeddings): Embedding(50265, 768, padding_idx=1)
(position_embeddings): Embedding(514, 768, padding_idx=1)
(token_type_embeddings): Embedding(1, 768)
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.0, inplace=False)
)
(encoder): RobertaEncoder(
(layer): ModuleList(
(0-11): 12 x RobertaLayer(
(attention): RobertaAttention(
(self): RobertaSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.0, inplace=False)
)
(output): RobertaSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.0, inplace=False)
)
)
(intermediate): RobertaIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): RobertaOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.0, inplace=False)
)
)
)
)
)
(lm_head): RobertaLMHead(
(dense): Linear(in_features=768, out_features=768, bias=True)
(layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(decoder): Linear(in_features=768, out_features=50265, bias=True)
)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
6aToH/pract1
|
6aToH
| 2024-06-26T14:50:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:50:37Z |
Entry not found
|
unclecode/folrence-2-8bit
|
unclecode
| 2024-06-26T14:54:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"florence2",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-06-26T14:53:05Z |
---
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]
|
habulaj/6072447560
|
habulaj
| 2024-06-26T14:53:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:53:43Z |
Entry not found
|
wtz37/sdxls
|
wtz37
| 2024-06-26T14:55:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:55:50Z |
Entry not found
|
Iftikhar1/650MB-test
|
Iftikhar1
| 2024-06-26T14:55:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:55:58Z |
Entry not found
|
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-en-100-percent-med-median-nv-embed-mmlu_cm
|
AdamKasumovic
| 2024-06-26T14:57:28Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T14:57:28Z |
---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
habulaj/377976343667
|
habulaj
| 2024-06-26T14:57:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T14:57:53Z |
Entry not found
|
gsar78/Greek_Tokenizer
|
gsar78
| 2024-06-26T15:27:07Z | 0 | 0 | null |
[
"greek",
"tokenization",
"bpe",
"el",
"license:mit",
"region:us"
] | null | 2024-06-26T15:01:18Z |
---
tags:
- greek
- tokenization
- bpe
license: mit
language:
- el
---
# Greek Tokenizer
Tokenizer trained from scratch based on BPE algorithm on Greek corpus.
### Usage:
To use this tokenizer, you can load it from the Hugging Face Hub:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gsar78/Greek_Tokenizer")
```
### Example:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gsar78/Greek_Tokenizer")
# Tokenize input text
input_text = "Αυτό είναι ένα παράδειγμα."
inputs = tokenizer(input_text, return_tensors="pt")
# Print the tokenized input (IDs and tokens)
print("Token IDs:", inputs["input_ids"].tolist())
# Convert token IDs to tokens
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
print("Tokens:", tokens)
# Manually join tokens to form the tokenized string
tokenized_string = ' '.join(tokens)
print("Tokenized String:", tokenized_string)
```
It can also be used as a head start for pretraining a GPT2 base model on the Greek language.
## Training Details
Vocabulary Size: 52000
Special Tokens: [PAD], [UNK], [CLS], [SEP], [MASK]
## Benefits and Why to use:
Many generic tokenizers split words in multiple tokens.
This tokenizer, efficient and only splits words that was not trained on.
In the example above , the output of this tokenizer is only five tokens, while another tokenizer *e.g. Llama-3* results in 9 or more tokens.
This can have an impact in inference costs and downstream applications.
|
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-en-100-percent-low-median-nv-embed-mmlu_cm
|
AdamKasumovic
| 2024-06-26T15:03:30Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T15:03:30Z |
---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
tlam/rvc2
|
tlam
| 2024-06-26T15:07:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:03:46Z |
Entry not found
|
habulaj/1394113989
|
habulaj
| 2024-06-26T15:04:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:04:42Z |
Entry not found
|
ako83/lilt-en-funsd
|
ako83
| 2024-06-26T15:06:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:06:21Z |
Entry not found
|
Reihaneh/wav2vec2_fy_nl_de_lid_common_voice_53
|
Reihaneh
| 2024-06-26T15:09:21Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T15:09:20Z |
---
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]
|
habulaj/476227446870
|
habulaj
| 2024-06-26T15:16:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:16:45Z |
Entry not found
|
PrunaAI/Contamination-contaminated_proof_7b_v1.0-QUANTO-int4bit-smashed
|
PrunaAI
| 2024-07-01T07:58:38Z | 0 | 0 |
transformers
|
[
"transformers",
"pruna-ai",
"base_model:Contamination/contaminated_proof_7b_v1.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T15:16:49Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Contamination/contaminated_proof_7b_v1.0
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Contamination/contaminated_proof_7b_v1.0 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/Contamination-contaminated_proof_7b_v1.0-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("Contamination/contaminated_proof_7b_v1.0")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Contamination/contaminated_proof_7b_v1.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
PrunaAI/Contamination-contaminated_proof_7b_v1.0-QUANTO-int8bit-smashed
|
PrunaAI
| 2024-07-01T08:01:10Z | 0 | 0 |
transformers
|
[
"transformers",
"pruna-ai",
"base_model:Contamination/contaminated_proof_7b_v1.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T15:17:04Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Contamination/contaminated_proof_7b_v1.0
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Contamination/contaminated_proof_7b_v1.0 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/Contamination-contaminated_proof_7b_v1.0-QUANTO-int8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("Contamination/contaminated_proof_7b_v1.0")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Contamination/contaminated_proof_7b_v1.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
PrunaAI/Contamination-contaminated_proof_7b_v1.0-QUANTO-int2bit-smashed
|
PrunaAI
| 2024-07-01T08:00:52Z | 0 | 0 |
transformers
|
[
"transformers",
"pruna-ai",
"base_model:Contamination/contaminated_proof_7b_v1.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T15:17:06Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Contamination/contaminated_proof_7b_v1.0
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Contamination/contaminated_proof_7b_v1.0 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/Contamination-contaminated_proof_7b_v1.0-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("Contamination/contaminated_proof_7b_v1.0")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Contamination/contaminated_proof_7b_v1.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
PrunaAI/Contamination-contaminated_proof_7b_v1.0-bnb-4bit-smashed
|
PrunaAI
| 2024-06-26T15:19:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"pruna-ai",
"conversational",
"base_model:Contamination/contaminated_proof_7b_v1.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-06-26T15:17:34Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Contamination/contaminated_proof_7b_v1.0
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Contamination/contaminated_proof_7b_v1.0 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/Contamination-contaminated_proof_7b_v1.0-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("Contamination/contaminated_proof_7b_v1.0")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Contamination/contaminated_proof_7b_v1.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
PrunaAI/Contamination-contaminated_proof_7b_v1.0-HQQ-2bit-smashed
|
PrunaAI
| 2024-06-26T15:19:16Z | 0 | 0 |
transformers
|
[
"transformers",
"mistral",
"text-generation",
"pruna-ai",
"conversational",
"base_model:Contamination/contaminated_proof_7b_v1.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-06-26T15:17:54Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Contamination/contaminated_proof_7b_v1.0
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Contamination/contaminated_proof_7b_v1.0 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/Contamination-contaminated_proof_7b_v1.0-HQQ-2bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/Contamination-contaminated_proof_7b_v1.0-HQQ-2bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("Contamination/contaminated_proof_7b_v1.0")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Contamination/contaminated_proof_7b_v1.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
PrunaAI/Contamination-contaminated_proof_7b_v1.0-HQQ-1bit-smashed
|
PrunaAI
| 2024-06-26T15:18:47Z | 0 | 0 |
transformers
|
[
"transformers",
"mistral",
"text-generation",
"pruna-ai",
"conversational",
"base_model:Contamination/contaminated_proof_7b_v1.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-06-26T15:17:58Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Contamination/contaminated_proof_7b_v1.0
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Contamination/contaminated_proof_7b_v1.0 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/Contamination-contaminated_proof_7b_v1.0-HQQ-1bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/Contamination-contaminated_proof_7b_v1.0-HQQ-1bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("Contamination/contaminated_proof_7b_v1.0")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Contamination/contaminated_proof_7b_v1.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
alex2020xx/analora
|
alex2020xx
| 2024-06-27T13:15:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:18:08Z |
Entry not found
|
PrunaAI/Contamination-contaminated_proof_7b_v1.0-HQQ-4bit-smashed
|
PrunaAI
| 2024-06-26T15:21:04Z | 0 | 0 |
transformers
|
[
"transformers",
"mistral",
"text-generation",
"pruna-ai",
"conversational",
"base_model:Contamination/contaminated_proof_7b_v1.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-06-26T15:18:54Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Contamination/contaminated_proof_7b_v1.0
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Contamination/contaminated_proof_7b_v1.0 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/Contamination-contaminated_proof_7b_v1.0-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/Contamination-contaminated_proof_7b_v1.0-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("Contamination/contaminated_proof_7b_v1.0")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Contamination/contaminated_proof_7b_v1.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
PrunaAI/Contamination-contaminated_proof_7b_v1.0-QUANTO-float8bit-smashed
|
PrunaAI
| 2024-07-01T08:00:23Z | 0 | 0 |
transformers
|
[
"transformers",
"pruna-ai",
"base_model:Contamination/contaminated_proof_7b_v1.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T15:19:11Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Contamination/contaminated_proof_7b_v1.0
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Contamination/contaminated_proof_7b_v1.0 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/Contamination-contaminated_proof_7b_v1.0-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("Contamination/contaminated_proof_7b_v1.0")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Contamination/contaminated_proof_7b_v1.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
MrChuki/lora_model
|
MrChuki
| 2024-06-27T10:42:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T15:19:31Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** MrChuki
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
habulaj/202356183473
|
habulaj
| 2024-06-26T15:20:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:20:09Z |
Entry not found
|
ako83/test01
|
ako83
| 2024-06-26T16:05:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"lilt",
"token-classification",
"generated_from_trainer",
"base_model:SCUT-DLVCLab/lilt-roberta-en-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-06-26T15:21:51Z |
---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: test01
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. -->
# test01
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
thuvasooriya/bprs_models
|
thuvasooriya
| 2024-06-26T16:14:13Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2024-06-26T15:22:17Z |
---
license: mit
---
|
fearlessbj4/tw_law_ft_data
|
fearlessbj4
| 2024-06-26T15:41:25Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-06-26T15:30:57Z |
---
license: apache-2.0
---
|
habulaj/245075216247
|
habulaj
| 2024-06-26T15:33:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:33:54Z |
Entry not found
|
shouray/Condition-Model-3
|
shouray
| 2024-06-26T15:36:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T15:36:16Z |
---
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]
|
habulaj/263118233669
|
habulaj
| 2024-06-26T15:37:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:37:28Z |
Entry not found
|
Harusoda/exp1-1
|
Harusoda
| 2024-06-26T15:38:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T15:38:38Z |
---
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]
|
Rude34/guerra
|
Rude34
| 2024-06-26T15:40:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:40:05Z |
Entry not found
|
habulaj/134720136104
|
habulaj
| 2024-06-26T15:41:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:40:47Z |
Entry not found
|
johnlockejrr/pylaia-ar-hand_v1
|
johnlockejrr
| 2024-07-01T10:28:20Z | 0 | 0 |
PyLaia
|
[
"PyLaia",
"PyTorch",
"atr",
"htr",
"ocr",
"historical",
"handwritten",
"image-to-text",
"ar",
"dataset:KHATT_v1.0",
"license:mit",
"region:us"
] |
image-to-text
| 2024-06-26T15:45:13Z |
---
library_name: PyLaia
pipeline_tag: image-to-text
license: mit
tags:
- PyLaia
- PyTorch
- atr
- htr
- ocr
- historical
- handwritten
metrics:
- cer
- wer
language:
- ar
datasets:
- KHATT_v1.0
---
Many thanks to Teklia team for their work on PyLaia library!
```bibtex
@inproceedings{pylaia-lib,
author = "Tarride, Solène and Schneider, Yoann and Generali, Marie and Boillet, Melodie and Abadie, Bastien and Kermorvant, Christopher",
title = "Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library",
booktitle = "Submitted at ICDAR2024",
year = "2024"
}
```
```bibtex
@inproceedings{KHATT,
author = "Sabri A. Mahmoud, Irfan Ahmad, Wasfi G. Al-Khatib, Mohammad Alshayeb, Mohammad Tanvir Parvez, Volker Märgner, Gernot A. Fink",
title = "KHATT: an open Arabic offline handwritten text database, Pattern Recognition.",
year = "2014"
```
|
Litzy619/MIS0626T1F150200
|
Litzy619
| 2024-06-26T18:28:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:46:30Z |
Entry not found
|
Lxt115/fine_tuning_0627
|
Lxt115
| 2024-06-27T01:17:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:47:48Z |
Entry not found
|
jimchen2/jimchen2-hello-world
|
jimchen2
| 2024-06-26T15:48:54Z | 0 | 1 | null |
[
"region:us"
] | null | 2024-06-26T15:48:54Z |
Entry not found
|
John12091209/unet_trained_model_20epoch
|
John12091209
| 2024-06-26T15:50:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:50:09Z |
Entry not found
|
matanalfasi/PAPI
|
matanalfasi
| 2024-06-26T15:51:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:51:01Z |
Entry not found
|
AudreyTrungNguyen/Smop_rte_seed11
|
AudreyTrungNguyen
| 2024-06-26T15:51:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:51:34Z |
Entry not found
|
zxssc/dfdf
|
zxssc
| 2024-06-26T15:53:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:53:30Z |
Entry not found
|
zxssc/dfdfegerg
|
zxssc
| 2024-06-26T15:55:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:54:09Z |
Entry not found
|
janphilippfranken/llama3-8b-instruct-star-gate
|
janphilippfranken
| 2024-06-26T15:56:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:56:21Z |
Entry not found
|
vidore/baseline-results
|
vidore
| 2024-07-02T20:49:38Z | 0 | 0 | null |
[
"vidore",
"license:mit",
"region:us"
] | null | 2024-06-26T15:56:45Z |
---
license: mit
tags:
- vidore
---
## Description
Contains the evaluation results for the baseline models from the *ColPali: Efficient Document Retrieval with Vision Language Models*.
|
gaur3009/bert_model
|
gaur3009
| 2024-06-26T16:08:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:57:32Z |
Entry not found
|
satyakotari/my-first-model
|
satyakotari
| 2024-06-26T15:59:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T15:59:01Z |
Entry not found
|
Pravalika18/example-model
|
Pravalika18
| 2024-06-26T16:03:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:01:32Z |
# Example model
This is my model card README
---
license: mit
---
|
Kudod/Vistral_finetuned_A100_June26th_1
|
Kudod
| 2024-06-26T21:15:59Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2024-06-26T16:02:36Z |
Entry not found
|
sixte99/marian-finetuned-kde4-en-to-fr
|
sixte99
| 2024-06-26T16:03:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:03:03Z |
Entry not found
|
aben118/tiny-common-voice-finetuning-server-10k
|
aben118
| 2024-06-26T16:04:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:04:10Z |
Entry not found
|
ChanceChou/RV2
|
ChanceChou
| 2024-06-26T16:05:59Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-06-26T16:05:59Z |
---
license: apache-2.0
---
|
caspro/nepali_tokenizer
|
caspro
| 2024-06-26T16:06:08Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T16:06:07Z |
---
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]
|
trudnyvozrast/AnnaPletnyova
|
trudnyvozrast
| 2024-06-26T16:08:45Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2024-06-26T16:07:35Z |
---
license: openrail
---
|
Litzy619/MIS0626T2F150200
|
Litzy619
| 2024-06-26T17:46:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:07:36Z |
Entry not found
|
karan29/llama3-8b-qa-tuned-merged
|
karan29
| 2024-06-26T16:08:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:08:20Z |
Entry not found
|
riddhiparmar/PVC2o_Orange1o_Bilayer10
|
riddhiparmar
| 2024-06-26T16:15:20Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2024-06-26T16:08:43Z |
---
license: unknown
---
|
sixte99/t5-small
|
sixte99
| 2024-06-26T16:09:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:09:02Z |
Entry not found
|
hamidei/dangeouswoman
|
hamidei
| 2024-06-26T16:32:49Z | 0 | 0 | null |
[
"music",
"en",
"dataset:HuggingFaceFW/fineweb-edu",
"license:epl-2.0",
"region:us"
] | null | 2024-06-26T16:10:09Z |
---
license: epl-2.0
datasets:
- HuggingFaceFW/fineweb-edu
language:
- en
tags:
- music
---
|
riddhiparmar/PVC2o_Orange2o_Bilayer2o
|
riddhiparmar
| 2024-06-26T16:16:47Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2024-06-26T16:10:14Z |
---
license: unknown
---
|
mdmitjans/distilbert-base-uncased-finetuned-clinc
|
mdmitjans
| 2024-06-26T16:12:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:12:38Z |
Entry not found
|
Kudod/hoa1b4_finetuned_kc_June26th_1
|
Kudod
| 2024-06-26T16:13:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:13:00Z |
Entry not found
|
yepyepyo/Iroha-ILLIT
|
yepyepyo
| 2024-06-28T18:25:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:14:57Z |
Entry not found
|
evanslur/detr-finetuned-balloon-v2
|
evanslur
| 2024-06-26T16:14:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:14:59Z |
Entry not found
|
sixte99/t5small-finetuned-opusbooks-en-fr
|
sixte99
| 2024-06-26T16:15:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:15:55Z |
Entry not found
|
AudreyTrungNguyen/hihi
|
AudreyTrungNguyen
| 2024-06-26T16:16:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:16:14Z |
Entry not found
|
jointriple/brand_classification_2_20240626_model_1
|
jointriple
| 2024-06-26T18:05:51Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:eu"
] | null | 2024-06-26T16:16:58Z |
---
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]
|
mrr-codes/dqn-SpaceInvadersNoFrameskip-v4
|
mrr-codes
| 2024-06-26T16:17:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-06-26T16:17:31Z |
Entry not found
|
srivatsaHFhub/googleGemma2b-dolly15k-instruct_tuned
|
srivatsaHFhub
| 2024-06-26T16:21:44Z | 0 | 0 | null |
[
"safetensors",
"license:mit",
"region:us"
] | null | 2024-06-26T16:18:25Z |
---
license: mit
---
|
Kibalama/PixelCopter
|
Kibalama
| 2024-06-26T16:22:42Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-26T16:20:58Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 15.90 +/- 14.77
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
mikejrodd/grapeleaf_KAN
|
mikejrodd
| 2024-06-26T16:35:41Z | 0 | 0 | null |
[
"arxiv:2406.13155",
"license:mit",
"region:us"
] | null | 2024-06-26T16:22:06Z |
---
license: mit
---
# Grape Leaf Disease Detection with KANConv2D
## Description
This project used a custom convolutional layer, KANConv2D, to build a convolutional neural network (CNN) for detecting Esca disease in grape leaves. The KANConv2D layer incorporates kernel adaptive networks to enhance the performance of traditional Conv2D layers.
For more on KAN see: https://arxiv.org/pdf/2406.13155
Final accuracy was 84.17%
## Features
- Custom KANConv2D layer with kernel adaptive networks
- Data augmentation for robust training
- Early stopping to prevent overfitting
- Class weighting to handle imbalanced datasets
## Model Architecture
The CNN model consists of:
- Multiple KANConv2D layers
- Max pooling layers
- Dense and dropout layers
- Binary output with sigmoid activation
## Training
The model is compiled with the Adam optimizer, binary cross-entropy loss, and accuracy as the metric. Class weights are adjusted to reduce false negatives for Esca detection. The model is trained with early stopping to restore the best weights based on validation loss.
|
Lxt115/fine_tunning_0627
|
Lxt115
| 2024-06-27T03:24:24Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2024-06-26T16:22:36Z |
Entry not found
|
AItechthinker/GenAii
|
AItechthinker
| 2024-06-26T16:22:52Z | 0 | 0 | null |
[
"license:artistic-2.0",
"region:us"
] | null | 2024-06-26T16:22:52Z |
---
license: artistic-2.0
---
|
shayantreylon2/lora_model5
|
shayantreylon2
| 2024-06-26T16:23:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-26T16:23:20Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** shayantreylon2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
NeelJ/xlm-roberta-base-finetuned-panx-en
|
NeelJ
| 2024-06-26T16:25:04Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2024-06-26T16:25:04Z |
---
license: mit
---
|
NeelJ/xlm-roberta-base-finetuned-panx-it
|
NeelJ
| 2024-06-26T16:25:22Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2024-06-26T16:25:22Z |
---
license: mit
---
|
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