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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-27 12:29:05
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| library_name
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princeton-nlp/Llama-3-Base-8B-SFT-IPO | princeton-nlp | 2024-06-17T11:45:42Z | 5,090 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2405.14734",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-17T21:31:53Z | This is a model released from the preprint: *[SimPO: Simple Preference Optimization with a Reference-Free Reward](https://arxiv.org/abs/2405.14734)* Please refer to our [repository](https://github.com/princeton-nlp/SimPO) for more details.
|
princeton-nlp/Llama-3-Base-8B-SFT-DPO | princeton-nlp | 2024-06-17T11:45:40Z | 5,282 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2405.14734",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-17T21:28:41Z | This is a model released from the preprint: *[SimPO: Simple Preference Optimization with a Reference-Free Reward](https://arxiv.org/abs/2405.14734)* Please refer to our [repository](https://github.com/princeton-nlp/SimPO) for more details.
|
mradermacher/K2S3-14b-v0.2-GGUF | mradermacher | 2024-06-17T11:42:14Z | 7 | 0 | transformers | [
"transformers",
"gguf",
"en",
"ko",
"base_model:Changgil/K2S3-14b-v0.2",
"base_model:quantized:Changgil/K2S3-14b-v0.2",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T10:50:43Z | ---
base_model: Changgil/K2S3-14b-v0.2
language:
- en
- ko
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Changgil/K2S3-14b-v0.2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q2_K.gguf) | Q2_K | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.IQ3_XS.gguf) | IQ3_XS | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q3_K_S.gguf) | Q3_K_S | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.IQ3_S.gguf) | IQ3_S | 6.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.IQ3_M.gguf) | IQ3_M | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q3_K_M.gguf) | Q3_K_M | 7.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q3_K_L.gguf) | Q3_K_L | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.IQ4_XS.gguf) | IQ4_XS | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q4_K_S.gguf) | Q4_K_S | 8.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q4_K_M.gguf) | Q4_K_M | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q5_K_S.gguf) | Q5_K_S | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q5_K_M.gguf) | Q5_K_M | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q6_K.gguf) | Q6_K | 11.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/K2S3-14b-v0.2-GGUF/resolve/main/K2S3-14b-v0.2.Q8_0.gguf) | Q8_0 | 15.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
DARK-SOUL/ppo-LunarLander-v2 | DARK-SOUL | 2024-06-17T11:42:10Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-06-17T11:41:50Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.33 +/- 14.77
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
alibaba-yuanjing-aigclab/ViViD | alibaba-yuanjing-aigclab | 2024-06-17T11:39:34Z | 0 | 3 | null | [
"arxiv:2405.11794",
"region:us"
] | null | 2024-06-14T08:34:01Z | # ViViD
ViViD: Video Virtual Try-on using Diffusion Models
[](https://arxiv.org/abs/2405.11794)
[](https://alibaba-yuanjing-aigclab.github.io/ViViD)
[](https://huggingface.co/alibaba-yuanjing-aigclab/ViViD)
## Installation
```
git clone https://github.com/alibaba-yuanjing-aigclab/ViViD
cd ViViD
```
### Environment
```
conda create -n vivid python=3.10
conda activate vivid
pip install -r requirements.txt
```
### Weights
You can place the weights anywhere you like, for example, ```./ckpts```. If you put them somewhere else, you just need to update the path in ```./configs/prompts/*.yaml```.
#### Stable Diffusion Image Variations
```
cd ckpts
git lfs install
git clone https://huggingface.co/lambdalabs/sd-image-variations-diffusers
```
#### SD-VAE-ft-mse
```
git lfs install
git clone https://huggingface.co/stabilityai/sd-vae-ft-mse
```
#### Motion Module
Download [mm_sd_v15_v2](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt)
#### ViViD
```
git lfs install
git clone git clone https://huggingface.co/alibaba-yuanjing-aigclab/ViViD
```
## Inference
We provide two demos in ```./configs/prompts/```, run the following commands to have a try😼.
```
python vivid.py --config ./configs/prompts/upper1.yaml
python vivid.py --config ./configs/prompts/lower1.yaml
```
## Data
As illustrated in ```./data```, the following data should be provided.
```text
./data/
|-- agnostic
| |-- video1.mp4
| |-- video2.mp4
| ...
|-- agnostic_mask
| |-- video1.mp4
| |-- video2.mp4
| ...
|-- cloth
| |-- cloth1.jpg
| |-- cloth2.jpg
| ...
|-- cloth_mask
| |-- cloth1.jpg
| |-- cloth2.jpg
| ...
|-- densepose
| |-- video1.mp4
| |-- video2.mp4
| ...
|-- videos
| |-- video1.mp4
| |-- video2.mp4
| ...
```
### Agnostic and agnostic_mask video
This part is a bit complex, you can obtain them through any of the following three ways:
1. Follow [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) to extract them frame-by-frame.(recommended)
2. Use [SAM](https://github.com/facebookresearch/segment-anything) + Gaussian Blur.(see ```./tools/sam_agnostic.py``` for an example)
3. Mask editor tools.
Note that the shape and size of the agnostic area may affect the try-on results.
### Densepose video
See [vid2densepose](https://github.com/Flode-Labs/vid2densepose).(Thanks)
### Cloth mask
Any detection tool is ok for obtaining the mask, like [SAM](https://github.com/facebookresearch/segment-anything).
## BibTeX
```text
@misc{fang2024vivid,
title={ViViD: Video Virtual Try-on using Diffusion Models},
author={Zixun Fang and Wei Zhai and Aimin Su and Hongliang Song and Kai Zhu and Mao Wang and Yu Chen and Zhiheng Liu and Yang Cao and Zheng-Jun Zha},
year={2024},
eprint={2405.11794},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Contact Us
**Zixun Fang**: [[email protected]](mailto:[email protected])
**Yu Chen**: [[email protected]](mailto:[email protected])
|
okxou/Qwen-Qwen1.5-0.5B-1718624315 | okxou | 2024-06-17T11:38:41Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-17T11:38:33Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## 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
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[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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
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### Framework versions
- PEFT 0.11.1 |
okxou/Qwen-Qwen1.5-1.8B-1718624049 | okxou | 2024-06-17T11:34:23Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-17T11:34:07Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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]
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[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
Dhahlan2000/Chitti-Base-model-for-GPT-v5 | Dhahlan2000 | 2024-06-17T11:33:51Z | 115 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Dhahlan2000/Chitti-Base-model-for-GPT-v4",
"base_model:finetune:Dhahlan2000/Chitti-Base-model-for-GPT-v4",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-17T11:33:41Z | ---
license: apache-2.0
base_model: Dhahlan2000/Chitti-Base-model-for-GPT-v4
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Chitti-Base-model-for-GPT-v5
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. -->
# Chitti-Base-model-for-GPT-v5
This model is a fine-tuned version of [Dhahlan2000/Chitti-Base-model-for-GPT-v4](https://huggingface.co/Dhahlan2000/Chitti-Base-model-for-GPT-v4) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1554
- Bleu: 2.4789
- Gen Len: 13.0087
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 3.3712 | 1.0 | 9282 | 3.1689 | 2.4693 | 13.056 |
| 3.3445 | 2.0 | 18564 | 3.1554 | 2.4789 | 13.0087 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Redgalaxy2/gemma-reformat_text-Finetune-2 | Redgalaxy2 | 2024-06-17T11:20:13Z | 148 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T11:18:08Z | ---
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
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
Osru/prueba-gguf-mistral | Osru | 2024-06-17T11:18:21Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"mistral",
"gguf",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-14T13:02:43Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
---
# Uploaded model
- **Developed by:** Osru
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-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)
|
mradermacher/AnLLM-EP-xllarge-wikiart-GGUF | mradermacher | 2024-06-17T11:17:26Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:pangjh3/AnLLM-EP-xllarge-wikiart",
"base_model:quantized:pangjh3/AnLLM-EP-xllarge-wikiart",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T11:15:44Z | ---
base_model: pangjh3/AnLLM-EP-xllarge-wikiart
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/pangjh3/AnLLM-EP-xllarge-wikiart
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.IQ3_XS.gguf) | IQ3_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.IQ3_S.gguf) | IQ3_S | 0.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.IQ3_M.gguf) | IQ3_M | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/AnLLM-EP-xllarge-wikiart-GGUF/resolve/main/AnLLM-EP-xllarge-wikiart.f16.gguf) | f16 | 0.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF | mradermacher | 2024-06-17T11:15:02Z | 9 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"cognitivecomputations/TinyDolphin-2.8-1.1b",
"78health/TinyLlama_1.1B-function-calling",
"DaertML/TinyGauss-1.1B",
"en",
"base_model:JoPmt/TinyEnsemble-3x1.1B-TinyMoE",
"base_model:quantized:JoPmt/TinyEnsemble-3x1.1B-TinyMoE",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T11:04:37Z | ---
base_model: JoPmt/TinyEnsemble-3x1.1B-TinyMoE
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- cognitivecomputations/TinyDolphin-2.8-1.1b
- 78health/TinyLlama_1.1B-function-calling
- DaertML/TinyGauss-1.1B
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/JoPmt/TinyEnsemble-3x1.1B-TinyMoE
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q2_K.gguf) | Q2_K | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.IQ3_XS.gguf) | IQ3_XS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q3_K_S.gguf) | Q3_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.IQ3_S.gguf) | IQ3_S | 1.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.IQ3_M.gguf) | IQ3_M | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q3_K_M.gguf) | Q3_K_M | 1.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q3_K_L.gguf) | Q3_K_L | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.IQ4_XS.gguf) | IQ4_XS | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q4_K_S.gguf) | Q4_K_S | 1.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q4_K_M.gguf) | Q4_K_M | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q5_K_S.gguf) | Q5_K_S | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q5_K_M.gguf) | Q5_K_M | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q6_K.gguf) | Q6_K | 2.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.Q8_0.gguf) | Q8_0 | 2.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/TinyEnsemble-3x1.1B-TinyMoE-GGUF/resolve/main/TinyEnsemble-3x1.1B-TinyMoE.f16.gguf) | f16 | 5.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/TiamaPY-v28-GGUF | mradermacher | 2024-06-17T11:13:40Z | 15 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"en",
"base_model:Ramikan-BR/TiamaPY-v28",
"base_model:quantized:Ramikan-BR/TiamaPY-v28",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-17T10:59:43Z | ---
base_model: Ramikan-BR/TiamaPY-v28
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Ramikan-BR/TiamaPY-v28
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q2_K.gguf) | Q2_K | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.IQ3_XS.gguf) | IQ3_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q3_K_S.gguf) | Q3_K_S | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.IQ3_S.gguf) | IQ3_S | 0.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.IQ3_M.gguf) | IQ3_M | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q3_K_M.gguf) | Q3_K_M | 0.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q3_K_L.gguf) | Q3_K_L | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.IQ4_XS.gguf) | IQ4_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q4_K_S.gguf) | Q4_K_S | 0.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q5_K_S.gguf) | Q5_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q5_K_M.gguf) | Q5_K_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q6_K.gguf) | Q6_K | 1.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.Q8_0.gguf) | Q8_0 | 1.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/TiamaPY-v28-GGUF/resolve/main/TiamaPY-v28.f16.gguf) | f16 | 2.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
c-eshih/models_human | c-eshih | 2024-06-17T11:09:49Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"diffusers-training",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-06-10T17:03:43Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
base_model: runwayml/stable-diffusion-v1-5
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# controlnet-c-eshih/models_human
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: realistic road scene
prompt: realistic road scene
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
UKV/mistral_4bit_maths_dataset | UKV | 2024-06-17T11:06:08Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/mistral-7b-v0.3-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-v0.3-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-06-17T10:59:21Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
---
# Uploaded model
- **Developed by:** UKV
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-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)
|
mradermacher/nyun-c1-llama3-60B-GGUF | mradermacher | 2024-06-17T11:04:39Z | 8 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:nyunai/nyun-c1-llama3-60B",
"base_model:quantized:nyunai/nyun-c1-llama3-60B",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T17:23:06Z | ---
base_model: nyunai/nyun-c1-llama3-60B
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/nyunai/nyun-c1-llama3-60B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/nyun-c1-llama3-60B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q2_K.gguf) | Q2_K | 22.6 | |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.IQ3_XS.gguf) | IQ3_XS | 25.1 | |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q3_K_S.gguf) | Q3_K_S | 26.4 | |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.IQ3_S.gguf) | IQ3_S | 26.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.IQ3_M.gguf) | IQ3_M | 27.4 | |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q3_K_M.gguf) | Q3_K_M | 29.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q3_K_L.gguf) | Q3_K_L | 31.9 | |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.IQ4_XS.gguf) | IQ4_XS | 32.8 | |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q4_K_S.gguf) | Q4_K_S | 34.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q4_K_M.gguf) | Q4_K_M | 36.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q5_K_S.gguf) | Q5_K_S | 41.7 | |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q5_K_M.gguf) | Q5_K_M | 42.8 | |
| [GGUF](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q6_K.gguf) | Q6_K | 49.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/nyun-c1-llama3-60B-GGUF/resolve/main/nyun-c1-llama3-60B.Q8_0.gguf.part2of2) | Q8_0 | 64.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Muckthaa/my-llama2-7b-chat-hf | Muckthaa | 2024-06-17T11:03:12Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"conversational",
"en",
"arxiv:2307.09288",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-12T10:54:23Z | ---
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language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
license: llama2
---
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)|
|70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)| |
llmvetter/a2c-PandaReachDense-v3 | llmvetter | 2024-06-17T10:54:55Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-06-17T10:50:41Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.16 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
WhiteHunter111/lora_model | WhiteHunter111 | 2024-06-17T10:54:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T10:53:56Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** WhiteHunter111
- **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)
|
okxou/Qwen-Qwen1.5-1.8B-1718621553 | okxou | 2024-06-17T10:52:37Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-17T10:52:31Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
AkashSKulkarni/ImageProfanity | AkashSKulkarni | 2024-06-17T10:51:17Z | 247 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-06-17T10:48:29Z | ---
license: apache-2.0
---
|
h-uns/RS_66_attn_noneng_nw | h-uns | 2024-06-17T10:49:14Z | 164 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T10:28:05Z | ---
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] |
ByteDance/shot2story | ByteDance | 2024-06-17T10:48:46Z | 0 | 27 | null | [
"visual-question-answering",
"en",
"dataset:mhan/Shot2Story-20K",
"dataset:mhan/shot2story",
"arxiv:2312.10300",
"region:us"
] | visual-question-answering | 2023-12-16T11:47:48Z | ---
datasets:
- mhan/Shot2Story-20K
- mhan/shot2story
language:
- en
metrics:
- bleu
pipeline_tag: visual-question-answering
---
# Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos
- **Repository:** [Shot2Story](https://github.com/bytedance/Shot2Story)
- **Paper:** [2312.10300](https://arxiv.org/abs/2312.10300)
- **Point of Contact:** mailto:[Mingfei Han]([email protected])
## Training Dataset
**Please download the multi-shot videos [here](https://1drv.ms/f/s!Ap3OKt6-X52NgXoG4-64N9WZDenS?e=oIHfkZ).**
We are excited to release a new video-text benchmark for multi-shot video understanding. This release contains a 134k version of our dataset. It includes detailed long summaries (human annotated + GPTV generated) for 134k videos and shot captions (human annotated) for 188k video shots. Please check the dataset [here](https://huggingface.co/datasets/mhan/Shot2Story-134K).
## Models
We are releasing the checkpoints trained with our [Shot2Story-20K](https://huggingface.co/datasets/mhan/Shot2Story-20K) and [Shot2Story-134K](https://huggingface.co/datasets/mhan/Shot2Story-134K).
- **{20k,134k}-version/sum_shot_best_epoch.pth:** Model tuned on our multi-shot summary data. Used in the config files `ckpt`.
- **{20k,134k}-version/shot_av_best_epoch.pth:** Model trained on our single-shot caption data. Used in the config files `ckpt`.
- **transnetv2-pytorch-weights.pth:** Checkpoint used for automatic shot detection method, which is used in the Bot demo. Please following the original license of the TransNetv2.
- **BLIP.cache.tar:** Cached checkpoints for training, testing and offline demos. This is only to ease the usage case that servers can't access huggingface. Please be restriected the original license to the different models.
## License <a name="license"></a>
Our text annotations are licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License](https://creativecommons.org/licenses/by-nc-sa/4.0/). They are available strictly for non-commercial research.
Users must refer to [HD-VILA-100M](https://github.com/microsoft/XPretrain/blob/main/hd-vila-100m/README.md) for original video access. By downloading our annotations, you agree to these terms. Respect for video copyright holders is paramount. Ensure your use of the videos aligns with the original source's terms.
---
## Citation <a name="citation"></a>
If you find our work useful for your research, please consider citing the paper
```
@misc{han2023shot2story20k,
title={Shot2Story20K: A New Benchmark for Comprehensive Understanding of Multi-shot Videos},
author={Mingfei Han and Linjie Yang and Xiaojun Chang and Heng Wang},
year={2023},
eprint={2312.10300},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
AlekseyElygin/Qwen2-7B | AlekseyElygin | 2024-06-17T10:48:27Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-17T10:45:41Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
base_model: unsloth/qwen2-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** AlekseyElygin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2-7b-bnb-4bit
This qwen2 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)
|
Dhahlan2000/Chitti-Base-model-for-GPT-v4 | Dhahlan2000 | 2024-06-17T10:47:52Z | 114 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Dhahlan2000/Chitti-Base-model-for-GPT-v3",
"base_model:finetune:Dhahlan2000/Chitti-Base-model-for-GPT-v3",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-17T10:47:43Z | ---
license: apache-2.0
base_model: Dhahlan2000/Chitti-Base-model-for-GPT-v3
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Chitti-Base-model-for-GPT-v4
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. -->
# Chitti-Base-model-for-GPT-v4
This model is a fine-tuned version of [Dhahlan2000/Chitti-Base-model-for-GPT-v3](https://huggingface.co/Dhahlan2000/Chitti-Base-model-for-GPT-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1847
- Bleu: 2.2129
- Gen Len: 13.0373
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 3.414 | 1.0 | 9282 | 3.1997 | 2.1556 | 13.0713 |
| 3.4101 | 2.0 | 18564 | 3.1847 | 2.2129 | 13.0373 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
MaziyarPanahi/mergekit-slerp-guwkdma-GGUF | MaziyarPanahi | 2024-06-17T10:45:42Z | 16 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:WizardLM/WizardMath-7B-V1.1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-guwkdma",
"base_model:quantized:mergekit-community/mergekit-slerp-guwkdma"
] | text-generation | 2024-06-17T10:23:13Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- conversational
- base_model:NousResearch/Hermes-2-Pro-Mistral-7B
- base_model:WizardLM/WizardMath-7B-V1.1
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-guwkdma-GGUF
base_model: mergekit-community/mergekit-slerp-guwkdma
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-guwkdma-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-guwkdma-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-guwkdma](https://huggingface.co/mergekit-community/mergekit-slerp-guwkdma)
## Description
[MaziyarPanahi/mergekit-slerp-guwkdma-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-guwkdma-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-guwkdma](https://huggingface.co/mergekit-community/mergekit-slerp-guwkdma).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
h-uns/RS_67_hv_noneng_w | h-uns | 2024-06-17T10:44:49Z | 165 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T10:27:51Z | ---
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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## 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
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### 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
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[More Information Needed]
## Training Details
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[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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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h-uns/RS_66_hv_noneng_w | h-uns | 2024-06-17T10:43:35Z | 164 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T10:27:43Z | ---
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]
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## Uses
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### Direct Use
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[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. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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xared/test_model_eng_2 | xared | 2024-06-17T10:41:21Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct",
"base_model:quantized:unsloth/llama-3-8b-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-17T10:27:12Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/llama-3-8b-Instruct
---
# Uploaded model
- **Developed by:** xared
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct
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)
|
pribadihcr/outSDXL_defect_no_7 | pribadihcr | 2024-06-17T10:40:15Z | 3 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-06-13T09:21:22Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks tray
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - pribadihcr/outSDXL_defect_no_7
<Gallery />
## Model description
These are pribadihcr/outSDXL_defect_no_7 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks tray to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](pribadihcr/outSDXL_defect_no_7/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
h-uns/RS_67_hv_eng_w | h-uns | 2024-06-17T10:39:25Z | 95 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T10:27:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### 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] |
debenoist/cubemistral16bit | debenoist | 2024-06-17T10:34:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T10:34:02Z | ---
base_model: mistralai/Mistral-7B-Instruct-v0.3
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** debenoist
- **License:** apache-2.0
- **Finetuned from model :** mistralai/Mistral-7B-Instruct-v0.3
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)
|
yetanotherhif/Llama-3-8B-alpaca | yetanotherhif | 2024-06-17T10:32:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T10:14:07Z | ---
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:** yetanotherhif
- **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)
|
AndrewDOrlov/bert_prof_single_v3_128_below_100 | AndrewDOrlov | 2024-06-17T10:15:22Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T08:38:46Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert_prof_single_v3_128_below_100
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_prof_single_v3_128_below_100
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6341
- Accuracy: 0.8500
- F1: 0.8455
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.6289314429698796e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.7676 | 1.0 | 7505 | 0.7432 | 0.8097 | 0.7967 |
| 0.579 | 2.0 | 15010 | 0.6401 | 0.8404 | 0.8343 |
| 0.4389 | 3.0 | 22515 | 0.6213 | 0.8482 | 0.8430 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
YYYYYYibo/nash_ave_pi_iter_3 | YYYYYYibo | 2024-06-17T10:13:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"dataset:updated",
"dataset:original",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"base_model:adapter:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"region:us"
] | null | 2024-06-17T08:21:06Z | ---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
base_model: alignment-handbook/zephyr-7b-sft-full
datasets:
- updated
- original
model-index:
- name: nash_ave_pi_iter_3
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. -->
# nash_ave_pi_iter_3
This model is a fine-tuned version of [YYYYYYibo/nash_ave_pi_iter_2](https://huggingface.co/YYYYYYibo/nash_ave_pi_iter_2) on the updated and the original datasets.
It achieves the following results on the evaluation set:
- Loss: 0.6740
- Rewards/chosen: 0.0085
- Rewards/rejected: -0.0290
- Rewards/accuracies: 0.6360
- Rewards/margins: 0.0375
- Logps/rejected: -263.6366
- Logps/chosen: -286.1499
- Logits/rejected: -2.6278
- Logits/chosen: -2.7115
## 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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6837 | 0.61 | 100 | 0.6740 | 0.0085 | -0.0290 | 0.6360 | 0.0375 | -263.6366 | -286.1499 | -2.6278 | -2.7115 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.3.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |
datvtn/RealVisXL_V4.0_Lightning_TRT | datvtn | 2024-06-17T10:12:56Z | 0 | 0 | null | [
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2024-06-14T14:29:12Z | ---
license: apache-2.0
---
|
talli96123/meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_12_best | talli96123 | 2024-06-17T10:08:39Z | 199 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-06-17T10:06:11Z | ---
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]
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MaziyarPanahi/mergekit-slerp-bjlsrkr-GGUF | MaziyarPanahi | 2024-06-17T10:08:32Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:cognitivecomputations/dolphin-2.8-mistral-7b-v02",
"base_model:arcee-ai/sec-mistral-7b-instruct-1.6-epoch",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-bjlsrkr",
"base_model:quantized:mergekit-community/mergekit-slerp-bjlsrkr"
] | text-generation | 2024-06-17T09:46:23Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- conversational
- base_model:cognitivecomputations/dolphin-2.8-mistral-7b-v02
- base_model:arcee-ai/sec-mistral-7b-instruct-1.6-epoch
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-bjlsrkr-GGUF
base_model: mergekit-community/mergekit-slerp-bjlsrkr
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-bjlsrkr-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-bjlsrkr-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-bjlsrkr](https://huggingface.co/mergekit-community/mergekit-slerp-bjlsrkr)
## Description
[MaziyarPanahi/mergekit-slerp-bjlsrkr-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-bjlsrkr-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-bjlsrkr](https://huggingface.co/mergekit-community/mergekit-slerp-bjlsrkr).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
spycoder/vit-base-patch16-224-in21k-enhanced-ham10000 | spycoder | 2024-06-17T10:00:21Z | 220 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-06-17T09:59:55Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-beans
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. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/HAM_db_enhanced_balanced_reduced_50_20_20_50 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5305
- Accuracy: 0.8451
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.0791 | 0.2304 | 100 | 1.0348 | 0.6335 |
| 0.9415 | 0.4608 | 200 | 0.9576 | 0.6449 |
| 0.7839 | 0.6912 | 300 | 0.8963 | 0.6662 |
| 0.7181 | 0.9217 | 400 | 0.8479 | 0.6963 |
| 0.3995 | 1.1521 | 500 | 0.7821 | 0.7170 |
| 0.5025 | 1.3825 | 600 | 0.6300 | 0.7837 |
| 0.4985 | 1.6129 | 700 | 0.7059 | 0.7490 |
| 0.4388 | 1.8433 | 800 | 0.5893 | 0.7857 |
| 0.2389 | 2.0737 | 900 | 0.5929 | 0.8077 |
| 0.2767 | 2.3041 | 1000 | 0.5795 | 0.8091 |
| 0.2387 | 2.5346 | 1100 | 0.6100 | 0.8091 |
| 0.1691 | 2.7650 | 1200 | 0.6175 | 0.8071 |
| 0.1738 | 2.9954 | 1300 | 0.5877 | 0.8198 |
| 0.0397 | 3.2258 | 1400 | 0.5766 | 0.8358 |
| 0.03 | 3.4562 | 1500 | 0.5681 | 0.8371 |
| 0.092 | 3.6866 | 1600 | 0.5305 | 0.8451 |
| 0.0416 | 3.9171 | 1700 | 0.5443 | 0.8471 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
slimaneMakh/MultiLBinSClass_Pensions_17june_student_XLMR | slimaneMakh | 2024-06-17T09:59:03Z | 163 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T09:58:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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slimaneMakh/MultiLBinSClass_Payables_17june_student_XLMR | slimaneMakh | 2024-06-17T09:57:55Z | 184 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T09:57:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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slimaneMakh/MultiLBinSClass_Cash_and_cash_equivalents_17june_student_XLMR | slimaneMakh | 2024-06-17T09:53:48Z | 164 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T09:53:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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
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[More Information Needed]
## Training Details
### Training Data
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ibbb/model | ibbb | 2024-06-17T09:53:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T09:52:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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AlexanderDadario/Mistral_qualnti | AlexanderDadario | 2024-06-17T09:50:48Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T09:50:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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slimaneMakh/MultiLBinSClass_Inventories_17june_student_XLMR | slimaneMakh | 2024-06-17T09:48:20Z | 164 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T09:47:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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
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[More Information Needed]
## Training Details
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Yatinginging/query-rewriter-lora | Yatinginging | 2024-06-17T09:47:34Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-12T09:16:53Z | ---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: query-rewriter-lora
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. -->
# query-rewriter-lora
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.15.2
|
imirandam/CLIP_Detector | imirandam | 2024-06-17T09:44:45Z | 0 | 0 | null | [
"dataset:imirandam/TROHN-Img",
"arxiv:2406.09952",
"license:mit",
"region:us"
] | null | 2024-06-12T20:38:58Z | ---
license: mit
datasets:
- imirandam/TROHN-Img
---
# Model Card for CLIP_Detector
## Model Description
- **Homepage:** https://imirandam.github.io/BiVLC_project_page/
- **Repository:** https://github.com/IMirandaM/BiVLC
- **Paper:** https://arxiv.org/abs/2406.09952
- **Point of Contact:** [Imanol Miranda](mailto:[email protected])
### Model Summary
CLIP_Detector is a model presented in the [BiVLC](https://github.com/IMirandaM/BiVLC) paper for experimentation. It has been trained with the OpenCLIP framework using the CLIP ViT-B-32 model pre-trained by 'openai' as a basis. For binary classification, the encoders are kept frozen. A sigmoid neuron is added over the CLS embedding for the image encoder and over the EOT embedding for the text encoder (more details in the paper). The objective of the model is to classify text and images as natural or synthetic. Hyperparameters:
* Learning rate: 1e-6.
* Optimizer: Adam optimizer with beta1 = 0.9, beta2 = 0.999, eps = 1e-08 and without weight decay.
* Loss function: Binary cross-entropy loss (BCELoss).
* Batch size: We define a batch size of 400.
* Epochs: We trained the text detector over 10 epochs and the image detector over 1 epoch. We used validation accuracy as the model selection criterion, i.e. we selected the model with highest accuracy in the corresponding validation set.
* Data: Then sigmoid neuron is trained with [TROHN-Img](https://huggingface.co/datasets/imirandam/TROHN-Img) dataset.
### Licensing Information
This work is licensed under a MIT License.
## Citation Information
If you find this dataset useful, please consider citing our paper:
```
@misc{miranda2024bivlc,
title={BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval},
author={Imanol Miranda and Ander Salaberria and Eneko Agirre and Gorka Azkune},
year={2024},
eprint={2406.09952},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
imirandam/CLIP_TROHN-Img | imirandam | 2024-06-17T09:44:14Z | 0 | 0 | null | [
"dataset:imirandam/TROHN-Img",
"arxiv:2406.09952",
"license:mit",
"region:us"
] | null | 2024-06-12T19:04:39Z | ---
license: mit
datasets:
- imirandam/TROHN-Img
---
# Model Card for CLIP_TROHN-Img
## Model Description
- **Homepage:** https://imirandam.github.io/BiVLC_project_page/
- **Repository:** https://github.com/IMirandaM/BiVLC
- **Paper:** https://arxiv.org/abs/2406.09952
- **Point of Contact:** [Imanol Miranda](mailto:[email protected])
### Model Summary
CLIP_TROHN-Img is a model presented in the [BiVLC](https://github.com/IMirandaM/BiVLC) paper for experimentation. It has been fine-tuned with OpenCLIP framework using as basis the CLIP ViT-B-32 model pre-trained by 'openai'. The idea behind this fine-tuning is to improve the compositional understanding of the model by adding negative pairs, i.e., negative captions and negative images. The negatives present small compositional changes. Hyperparameters:
* Learning rate: 1e-6.
* Scheduler: Cosine scheduler with 50 warmup steps.
* Optimizer: AdamW optimizer with beta1 = 0.9, beta2 = 0.98, eps = 1e-6 and weight decay = 0.1.
* Loss function: InfoNCE Loss.
* Batch size: We define a batch size of 200, and then we add negatives. It results in 400 images x 400 captions (200 positive + 200 hard negatives).
* Epochs: We fine-tune all models over 10 epochs and we used validation accuracy as the model selection criterion, i.e. we selected the model with the highest accuracy on the corresponding validation set.
* Data: It is fine-tuned with [TROHN-Img](https://huggingface.co/datasets/imirandam/TROHN-Img) dataset.
### Evaluation Data
The model is evaluated in [BiVLC](https://huggingface.co/datasets/imirandam/BiVLC).
### Licensing Information
This work is licensed under a MIT License.
## Citation Information
If you find this dataset useful, please consider citing our paper:
```
@misc{miranda2024bivlc,
title={BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval},
author={Imanol Miranda and Ander Salaberria and Eneko Agirre and Gorka Azkune},
year={2024},
eprint={2406.09952},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
imirandam/CLIP_TROHN-Text | imirandam | 2024-06-17T09:43:33Z | 0 | 0 | null | [
"dataset:imirandam/TROHN-Text",
"arxiv:2406.09952",
"license:mit",
"region:us"
] | null | 2024-06-12T19:04:54Z | ---
license: mit
datasets:
- imirandam/TROHN-Text
---
# Model Card for CLIP_TROHN-Text
## Model Description
- **Homepage:** https://imirandam.github.io/BiVLC_project_page/
- **Repository:** https://github.com/IMirandaM/BiVLC
- **Paper:** https://arxiv.org/abs/2406.09952
- **Point of Contact:** [Imanol Miranda](mailto:[email protected])
### Model Summary
CLIP_TROHN-Text is a model presented in the [BiVLC](https://github.com/IMirandaM/BiVLC) paper for experimentation. It has been fine-tuned with OpenCLIP framework using as basis the CLIP ViT-B-32 model pre-trained by 'openai'. The idea behind this fine-tuning is to improve the compositional understanding of the model by adding negative captions. The negatives present small compositional changes. Hyperparameters:
* Learning rate: 1e-6.
* Scheduler: Cosine scheduler with 50 warmup steps.
* Optimizer: AdamW optimizer with beta1 = 0.9, beta2 = 0.98, eps = 1e-6 and weight decay = 0.1.
* Loss function: InfoNCE Loss. The loss is modified to add only negative captions following the idea proposed in NEGCLIP.
* Batch size: We define a batch size of 200, and then we add negatives. As it has not hard negative images, it results in 200 images x 400 captions (positive + hard negatives).
* Epochs: We fine-tune all models over 10 epochs and we used validation accuracy as the model selection criterion, i.e. we selected the model with the highest accuracy on the corresponding validation set.
* Data: It is fine-tuned with [TROHN-Text](https://huggingface.co/datasets/imirandam/TROHN-Text) dataset.
### Evaluation Data
The model is evaluated in [BiVLC](https://huggingface.co/datasets/imirandam/BiVLC).
### Licensing Information
This work is licensed under a MIT License.
## Citation Information
If you find this dataset useful, please consider citing our paper:
```
@misc{miranda2024bivlc,
title={BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval},
author={Imanol Miranda and Ander Salaberria and Eneko Agirre and Gorka Azkune},
year={2024},
eprint={2406.09952},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
MaziyarPanahi/mergekit-slerp-urmzxzt-GGUF | MaziyarPanahi | 2024-06-17T09:35:46Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:WizardLM/WizardMath-7B-V1.1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-urmzxzt",
"base_model:quantized:mergekit-community/mergekit-slerp-urmzxzt"
] | text-generation | 2024-06-17T09:12:43Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- conversational
- base_model:NousResearch/Hermes-2-Pro-Mistral-7B
- base_model:WizardLM/WizardMath-7B-V1.1
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-urmzxzt-GGUF
base_model: mergekit-community/mergekit-slerp-urmzxzt
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-urmzxzt-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-urmzxzt-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-urmzxzt](https://huggingface.co/mergekit-community/mergekit-slerp-urmzxzt)
## Description
[MaziyarPanahi/mergekit-slerp-urmzxzt-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-urmzxzt-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-urmzxzt](https://huggingface.co/mergekit-community/mergekit-slerp-urmzxzt).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
slimaneMakh/MultiLBinSClass_Borrowings_17june_student_XLMR | slimaneMakh | 2024-06-17T09:35:00Z | 165 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T09:34:20Z | ---
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
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#### Software
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## 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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numen-tech/Hathor_Stable-v0.2-L3-8B-w3a16g40sym | numen-tech | 2024-06-17T09:34:45Z | 0 | 0 | null | [
"arxiv:2308.13137",
"license:llama3",
"region:us"
] | null | 2024-06-17T09:26:13Z | ---
license: llama3
---
3-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Hathor_Stable-v0.2-L3-8B](https://huggingface.co/Nitral-AI/Hathor_Stable-v0.2-L3-8B).
|
HienHNMU/Summarization | HienHNMU | 2024-06-17T09:27:29Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:wcep-10",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-17T07:24:28Z | ---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
datasets:
- wcep-10
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wcep-10
type: wcep-10
config: roberta
split: validation
args: roberta
metrics:
- name: Rouge1
type: rouge
value: 22.6862
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wcep-10 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1575
- Rouge1: 22.6862
- Rouge2: 7.7268
- Rougel: 19.1961
- Rougelsum: 19.3808
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.5905 | 1.0 | 1020 | 3.4711 | 21.2268 | 7.4345 | 18.5023 | 18.6264 |
| 4.1604 | 2.0 | 2040 | 3.3228 | 21.6354 | 7.3939 | 18.4926 | 18.6047 |
| 3.914 | 3.0 | 3060 | 3.2606 | 21.9787 | 7.5818 | 18.6971 | 18.8603 |
| 3.7698 | 4.0 | 4080 | 3.2058 | 21.8859 | 7.5625 | 18.6413 | 18.8169 |
| 3.679 | 5.0 | 5100 | 3.1824 | 22.6515 | 7.7467 | 19.1196 | 19.3121 |
| 3.6131 | 6.0 | 6120 | 3.1678 | 22.0223 | 7.6153 | 18.7956 | 18.9968 |
| 3.5722 | 7.0 | 7140 | 3.1631 | 22.679 | 7.7952 | 19.1784 | 19.384 |
| 3.5432 | 8.0 | 8160 | 3.1575 | 22.6862 | 7.7268 | 19.1961 | 19.3808 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
aviol/Meta-Llama-4-8B | aviol | 2024-06-17T09:25:48Z | 0 | 1 | null | [
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"en",
"license:llama3",
"region:us"
] | text-generation | 2024-06-17T09:23:46Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
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Llama Materials set forth herein.
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the State of California without regard to choice of law principles, and the UN Convention on Contracts
for the International Sale of Goods does not apply to this Agreement. The courts of California shall have
exclusive jurisdiction of any dispute arising out of this Agreement.
### Meta Llama 3 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
#### Prohibited Uses
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others to use, Meta Llama 3 to:
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1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
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Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
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---
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
>>> import transformers
>>> import torch
>>> model_id = "meta-llama/Meta-Llama-3-8B"
>>> pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
>>> pipeline("Hey how are you doing today?")
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/*" --local-dir Meta-Llama-3-8B
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 8B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
sebgobb/camembert-review-movie-test | sebgobb | 2024-06-17T09:24:22Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"camembert",
"text-classification",
"generated_from_trainer",
"base_model:almanach/camembert-base-legacy",
"base_model:finetune:almanach/camembert-base-legacy",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-16T20:32:54Z | ---
base_model: camembert/camembert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: camembert-review-movie-test
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. -->
# camembert-review-movie-test
This model is a fine-tuned version of [camembert/camembert-base](https://huggingface.co/camembert/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5094
- Accuracy: 0.4722
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 72 | 2.1355 | 0.3611 |
| No log | 2.0 | 144 | 1.9839 | 0.4444 |
| No log | 3.0 | 216 | 1.8587 | 0.4444 |
| No log | 4.0 | 288 | 1.7622 | 0.4444 |
| No log | 5.0 | 360 | 1.6754 | 0.5 |
| No log | 6.0 | 432 | 1.6065 | 0.4861 |
| 1.7682 | 7.0 | 504 | 1.5903 | 0.5139 |
| 1.7682 | 8.0 | 576 | 1.5316 | 0.5417 |
| 1.7682 | 9.0 | 648 | 1.5195 | 0.4861 |
| 1.7682 | 10.0 | 720 | 1.5094 | 0.4722 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Abhinandha/sentence_sum | Abhinandha | 2024-06-17T09:20:52Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-06T06:12:34Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: sentence_sum
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. -->
# sentence_sum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 48 | 1.9852 | 47.1796 | 26.0895 | 41.0934 | 41.5442 | 17.7895 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Dhahlan2000/Chitti-Base-model-for-GPT-v3 | Dhahlan2000 | 2024-06-17T09:18:12Z | 113 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Dhahlan2000/Chitti-Base-model-for-GPT-v2",
"base_model:finetune:Dhahlan2000/Chitti-Base-model-for-GPT-v2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-17T09:17:58Z | ---
license: apache-2.0
base_model: Dhahlan2000/Chitti-Base-model-for-GPT-v2
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Chitti-Base-model-for-GPT-v3
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. -->
# Chitti-Base-model-for-GPT-v3
This model is a fine-tuned version of [Dhahlan2000/Chitti-Base-model-for-GPT-v2](https://huggingface.co/Dhahlan2000/Chitti-Base-model-for-GPT-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2825
- Bleu: 2.1101
- Gen Len: 13.4787
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 3.51 | 1.0 | 9282 | 3.3020 | 1.9318 | 13.4793 |
| 3.4658 | 2.0 | 18564 | 3.2825 | 2.1101 | 13.4787 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
pribadihcr/outSDXL_defect_no_2 | pribadihcr | 2024-06-17T09:14:18Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-06-17T07:43:34Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks defect tray
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - pribadihcr/outSDXL_defect_no_2
<Gallery />
## Model description
These are pribadihcr/outSDXL_defect_no_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks defect tray to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](pribadihcr/outSDXL_defect_no_2/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
iharrisonfu/hk-suicidenews-extractor-llama-8b-16bit | iharrisonfu | 2024-06-17T09:13:19Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T03:36:09Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** iharrisonfu
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
- **Usage:** Extract information about the deceased from traditional Chinese suicide-related news in Hong Kong.
|
yewo/KoTST | yewo | 2024-06-17T09:12:54Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bart",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-05-08T00:30:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Ariffiq99/e_care_CRAB_COPA_KUCI_bert_base_uncased_finetuned | Ariffiq99 | 2024-06-17T09:11:00Z | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"base_model:Ariffiq99/CRAB_COPA_KUCI_Bert_Base_Uncased_finetuned",
"base_model:finetune:Ariffiq99/CRAB_COPA_KUCI_Bert_Base_Uncased_finetuned",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | multiple-choice | 2024-06-17T09:10:29Z | ---
license: apache-2.0
base_model: Ariffiq99/CRAB_COPA_KUCI_Bert_Base_Uncased_finetuned
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: e_care_CRAB_COPA_KUCI_bert_base_uncased_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# e_care_CRAB_COPA_KUCI_bert_base_uncased_finetuned
This model is a fine-tuned version of [Ariffiq99/CRAB_COPA_KUCI_Bert_Base_Uncased_finetuned](https://huggingface.co/Ariffiq99/CRAB_COPA_KUCI_Bert_Base_Uncased_finetuned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8644
- F1: 0.7531
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5812 | 1.0 | 933 | 0.4923 | 0.7342 |
| 0.4015 | 2.0 | 1866 | 0.5055 | 0.7512 |
| 0.2845 | 3.0 | 2799 | 0.6494 | 0.7493 |
| 0.1812 | 4.0 | 3732 | 0.7457 | 0.7620 |
| 0.1344 | 5.0 | 4665 | 0.8267 | 0.7568 |
| 0.1094 | 6.0 | 5598 | 0.8644 | 0.7531 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
excalibur12/wav2vec2-large_phoneme-timit_english_timit-4k_001 | excalibur12 | 2024-06-17T09:10:31Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-03-31T23:13:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: wav2vec2-large_phoneme-timit_english_timit-4k_001
results: []
language:
- en
metrics:
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large_phoneme-timit_english_timit-4k_001
This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the timit dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4952
- Per: 0.1134
## Model description
The wav2vec 2.0 large model is pre-trained on 960 hours of the LibriSpeech dataset.
- 24 Transformer blocks (Each block: 1024 dimensions & 16 attention heads)
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Per |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.5458 | 3.46 | 1000 | 0.9087 | 0.2354 |
| 0.7877 | 6.92 | 2000 | 0.4441 | 0.1506 |
| 0.5125 | 10.38 | 3000 | 0.4241 | 0.1451 |
| 0.4485 | 13.84 | 4000 | 0.4244 | 0.1461 |
| 0.4193 | 17.3 | 5000 | 0.4618 | 0.1510 |
| 0.3899 | 20.76 | 6000 | 0.4700 | 0.1469 |
| 0.3244 | 24.22 | 7000 | 0.4496 | 0.1438 |
| 0.2717 | 27.68 | 8000 | 0.4988 | 0.1455 |
| 0.2222 | 31.14 | 9000 | 0.5182 | 0.1414 |
| 0.1872 | 34.6 | 10000 | 0.5320 | 0.1411 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.13.3 |
ai-human-lab/SOLAR-10.7B-Vocab_Expanded-v1.0 | ai-human-lab | 2024-06-17T09:08:04Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T08:32:44Z | ---
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]
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## 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
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[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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
wh2004/model4 | wh2004 | 2024-06-17T09:05:31Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T08:56:19Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** wh2004
- **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)
|
mb7419/Tesla-QA-Llama-3-8B-Instruct | mb7419 | 2024-06-17T08:57:35Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T08:23:24Z | ---
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. -->
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## 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] |
Niggendar/ponyForanime_v01 | Niggendar | 2024-06-17T08:55:19Z | 134 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-06-17T08:46:43Z | ---
library_name: diffusers
---
# 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 🧨 diffusers 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
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### Training Procedure
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf | RichardErkhov | 2024-06-17T08:51:54Z | 31 | 0 | null | [
"gguf",
"arxiv:2101.03961",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T22:28:02Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
MistralTrix8x9B - GGUF
- Model creator: https://huggingface.co/Kquant03/
- Original model: https://huggingface.co/Kquant03/MistralTrix8x9B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [MistralTrix8x9B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q2_K.gguf) | Q2_K | 20.12GB |
| [MistralTrix8x9B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.IQ3_XS.gguf) | IQ3_XS | 22.49GB |
| [MistralTrix8x9B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.IQ3_S.gguf) | IQ3_S | 23.75GB |
| [MistralTrix8x9B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q3_K_S.gguf) | Q3_K_S | 23.75GB |
| [MistralTrix8x9B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.IQ3_M.gguf) | IQ3_M | 24.91GB |
| [MistralTrix8x9B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q3_K.gguf) | Q3_K | 26.18GB |
| [MistralTrix8x9B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q3_K_M.gguf) | Q3_K_M | 26.18GB |
| [MistralTrix8x9B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q3_K_L.gguf) | Q3_K_L | 28.1GB |
| [MistralTrix8x9B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.IQ4_XS.gguf) | IQ4_XS | 29.5GB |
| [MistralTrix8x9B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q4_0.gguf) | Q4_0 | 30.74GB |
| [MistralTrix8x9B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.IQ4_NL.gguf) | IQ4_NL | 31.09GB |
| [MistralTrix8x9B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q4_K_S.gguf) | Q4_K_S | 31.09GB |
| [MistralTrix8x9B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q4_K.gguf) | Q4_K | 33.08GB |
| [MistralTrix8x9B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q4_K_M.gguf) | Q4_K_M | 33.08GB |
| [MistralTrix8x9B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/blob/main/MistralTrix8x9B.Q4_1.gguf) | Q4_1 | 34.11GB |
| [MistralTrix8x9B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/tree/main/) | Q5_0 | 37.48GB |
| [MistralTrix8x9B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/tree/main/) | Q5_K_S | 37.48GB |
| [MistralTrix8x9B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/tree/main/) | Q5_K | 38.64GB |
| [MistralTrix8x9B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/tree/main/) | Q5_K_M | 38.64GB |
| [MistralTrix8x9B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/tree/main/) | Q5_1 | 40.84GB |
| [MistralTrix8x9B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/tree/main/) | Q6_K | 44.63GB |
| [MistralTrix8x9B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Kquant03_-_MistralTrix8x9B-gguf/tree/main/) | Q8_0 | 57.71GB |
Original model description:
---
license: apache-2.0
---
An attempt to beat Mixtral Instruct by conjuring frankenMoE's monster: **THE 8X MISTRALTRIX!!!!** I had trouble quantizing this one so until mergekit this will remain in BF16.
But don't worry, a **titan looms on the horizon**...
# "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)"
### (from the MistralAI papers...click the quoted question above to navigate to it directly.)
The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps.
Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining.
So, what exactly is a MoE? In the context of transformer models, a MoE consists of two main elements:
Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 32 in my "frankenMoE"), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs!
A gate network or router, that determines which tokens are sent to which expert. For example, in the image below, the token “More” is sent to the second expert, and the token "Parameters” is sent to the first network. As we’ll explore later, we can send a token to more than one expert. How to route a token to an expert is one of the big decisions when working with MoEs - the router is composed of learned parameters and is pretrained at the same time as the rest of the network.
At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively.
Switch Layer
MoE layer from the [Switch Transformers paper](https://arxiv.org/abs/2101.03961)
So, to recap, in MoEs we replace every FFN layer of the transformer model with an MoE layer, which is composed of a gate network and a certain number of experts.
Although MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges:
Training: MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting.
Inference: Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters. However, all parameters need to be loaded in RAM, so memory requirements are high. For example, [given a MoE like Mixtral 8x7B](https://huggingface.co/blog/moe), we’ll need to have enough VRAM to hold a dense 47B parameter model. Why 47B parameters and not 8 x 7B = 56B? That’s because in MoE models, only the FFN layers are treated as individual experts, and the rest of the model parameters are shared. At the same time, assuming just two experts are being used per token, the inference speed (FLOPs) is like using a 12B model (as opposed to a 14B model), because it computes 2x7B matrix multiplications, but with some layers shared (more on this soon).
If all our tokens are sent to just a few popular experts, that will make training inefficient. In a normal MoE training, the gating network converges to mostly activate the same few experts. This self-reinforces as favored experts are trained quicker and hence selected more. To mitigate this, an auxiliary loss is added to encourage giving all experts equal importance. This loss ensures that all experts receive a roughly equal number of training examples. The following sections will also explore the concept of expert capacity, which introduces a threshold of how many tokens can be processed by an expert. In transformers, the auxiliary loss is exposed via the aux_loss parameter.
## "Wait...but you called this a frankenMoE?"
The difference between MoE and "frankenMoE" lies in the fact that the router layer in a model like the one on this repo is not trained simultaneously. There are rumors about someone developing a way for us to unscuff these frankenMoE models by training the router layer simultaneously. For now, frankenMoE remains psychotic. I'm excited to see how this model performs in the open llm leaderboard.
|
taras-sereda/uk-pods-conformer | taras-sereda | 2024-06-17T08:44:02Z | 4 | 0 | nemo | [
"nemo",
"uk",
"dataset:taras-sereda/uk-pods",
"arxiv:2005.08100",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-06-07T10:49:20Z | ---
license: cc-by-nc-4.0
datasets:
- taras-sereda/uk-pods
language:
- uk
library_name: nemo
---
## Usage
The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
```
pip install nemo_toolkit['all']
```
### Automatically instantiate the model
```python
from nemo.collections.asr.models import EncDecCTCModelBPE
asr_model = EncDecCTCModelBPE.from_pretrained("taras-sereda/uk-pods-conformer")
```
### Transcribing using Python
First, let's get a sample
```
wget "https://huggingface.co/datasets/taras-sereda/uk-pods/resolve/main/example/e934c3e4-c37b-4607-98a8-22cdff933e4a_0266.wav?download=true" -O e934c3e4-c37b-4607-98a8-22cdff933e4a_0266.wav
```
Then simply do:
```
asr_model.transcribe(['e934c3e4-c37b-4607-98a8-22cdff933e4a_0266.wav'])
```
### Input
This model accepts 16000 kHz Mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
Conformer-CTC model is a non-autoregressive variant of Conformer model [2] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc).
### Datasets
This model has been trained using a combination of 2 datasets:
- UK-PODS [3] train dataset: This dataset comprises 46 hours of conversational speech collected from Ukrainian podcasts.
- Validated Mozilla Common Voice Corpus 10.0: (excluding dev and test data) dataset that includes 50.1 hours of Ukrainian speech.
## Performance
Performances of the ASR model is reported in terms of Word Error Rate (WER) with greedy decoding.
| Tokenizer | Vocabulary Size | UK-PODS test | MCV-10 test |
|:-------------:| :--------------: | :----------: | :---------: |
| SentencePiece | 1024 | 0.093 | 0.116 |
## References
- [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
- [2] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
- [3] [UK-PODS](https://huggingface.co/datasets/taras-sereda/uk-pods)
|
beckra/GermanLanguageLearningAssistant | beckra | 2024-06-17T08:43:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"de",
"base_model:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:finetune:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-06-11T10:17:18Z | ---
language:
- en
- de
license: llama3
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
---
# German Language Training Assistant
- **Finetuned from model :** VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
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) |
RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf | RichardErkhov | 2024-06-17T08:41:27Z | 8 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-17T07:13:03Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
yi-9b-may-ortho-baukit-30fail-3000total-bf16 - GGUF
- Model creator: https://huggingface.co/Edgerunners/
- Original model: https://huggingface.co/Edgerunners/yi-9b-may-ortho-baukit-30fail-3000total-bf16/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q2_K.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q2_K.gguf) | Q2_K | 3.12GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ3_XS.gguf) | IQ3_XS | 3.46GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ3_S.gguf) | IQ3_S | 3.64GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q3_K_S.gguf) | Q3_K_S | 3.63GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ3_M.gguf) | IQ3_M | 3.78GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q3_K.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q3_K.gguf) | Q3_K | 4.03GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q3_K_M.gguf) | Q3_K_M | 4.03GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q3_K_L.gguf) | Q3_K_L | 4.37GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ4_XS.gguf) | IQ4_XS | 4.5GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_0.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_0.gguf) | Q4_0 | 4.69GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.IQ4_NL.gguf) | IQ4_NL | 4.73GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_K_S.gguf) | Q4_K_S | 4.72GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_K.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_K.gguf) | Q4_K | 4.96GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_K_M.gguf) | Q4_K_M | 4.96GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_1.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q4_1.gguf) | Q4_1 | 5.19GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_0.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_0.gguf) | Q5_0 | 5.69GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_K_S.gguf) | Q5_K_S | 5.69GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_K.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_K.gguf) | Q5_K | 5.83GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_K_M.gguf) | Q5_K_M | 5.83GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_1.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q5_1.gguf) | Q5_1 | 6.19GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q6_K.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q6_K.gguf) | Q6_K | 6.75GB |
| [yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q8_0.gguf](https://huggingface.co/RichardErkhov/Edgerunners_-_yi-9b-may-ortho-baukit-30fail-3000total-bf16-gguf/blob/main/yi-9b-may-ortho-baukit-30fail-3000total-bf16.Q8_0.gguf) | Q8_0 | 8.74GB |
Original model description:
---
license: cc-by-nc-4.0
---
new 9b-yi released in may
test results: refusal removal worked, but yi 9b chat is still kind of bad, ortho won't fix that; but judge for yourself
this version had only 30 refusals out of 3000 ortho-tests, in-line with the others in terms of refusals.
---
wassname (updated baukit) implementation of the paper: https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction
applied to llama3 8b instruct
1. The Model is meant purely for alignment research and exploration of alignmentforum theory
2. The Model is provided ""AS IS"" and ""AS AVAILABLE"" without warranty of any kind, express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, title, or non-infringement.
3. The Provider disclaims all liability for any damages or losses resulting from the use or misuse of the Model, including but not limited to any damages or losses arising from the use of the Model for purposes other than those intended by the Provider.
4. The Provider does not endorse or condone the use of the Model for any purpose that violates applicable laws, regulations, or ethical standards.
5. The Provider does not warrant that the Model will meet your specific requirements or that it will be error-free or that it will function without interruption.
6. You assume all risks associated with the use of the Model, including but not limited to any loss of data, loss of business, or damage to your reputation.
|
Ariffiq99/COPA_KUCI_e_care_CRAB_xlm_roberta_large_finetuned | Ariffiq99 | 2024-06-17T08:37:35Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"multiple-choice",
"generated_from_trainer",
"base_model:Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_large_Finetuned",
"base_model:finetune:Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_large_Finetuned",
"license:mit",
"endpoints_compatible",
"region:us"
] | multiple-choice | 2024-06-17T08:22:26Z | ---
license: mit
base_model: Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_large_Finetuned
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: COPA_KUCI_e_care_CRAB_xlm_roberta_large_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# COPA_KUCI_e_care_CRAB_xlm_roberta_large_finetuned
This model is a fine-tuned version of [Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_large_Finetuned](https://huggingface.co/Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_large_Finetuned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8428
- F1: 0.82
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 63 | 0.4466 | 0.8140 |
| No log | 2.0 | 126 | 0.4047 | 0.816 |
| No log | 3.0 | 189 | 0.4252 | 0.828 |
| No log | 4.0 | 252 | 0.6281 | 0.822 |
| No log | 5.0 | 315 | 0.5377 | 0.824 |
| No log | 6.0 | 378 | 0.7201 | 0.804 |
| No log | 7.0 | 441 | 0.7403 | 0.822 |
| 0.2369 | 8.0 | 504 | 0.7664 | 0.826 |
| 0.2369 | 9.0 | 567 | 0.8375 | 0.818 |
| 0.2369 | 10.0 | 630 | 0.8428 | 0.82 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Naveen20o1/UAE_Large_V1_nav1 | Naveen20o1 | 2024-06-17T08:36:16Z | 10 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:899",
"loss:CoSENTLoss",
"arxiv:1908.10084",
"base_model:WhereIsAI/UAE-Large-V1",
"base_model:finetune:WhereIsAI/UAE-Large-V1",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-06-17T08:35:25Z | ---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:899
- loss:CoSENTLoss
base_model: WhereIsAI/UAE-Large-V1
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: hr
sentences:
- Geographical
- Quantity
- Person
- source_sentence: product
sentences:
- Organization
- Time
- Artifact
- source_sentence: council
sentences:
- Person
- Person
- Quantity
- source_sentence: salesman
sentences:
- Person
- Time
- Person
- source_sentence: joint_venture_name
sentences:
- Person
- Organization
- Person
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on WhereIsAI/UAE-Large-V1
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.8883347646952768
name: Pearson Cosine
- type: spearman_cosine
value: 0.8463283813349622
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8611263810572393
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.838590521848471
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8622761936152195
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8405249867200939
name: Spearman Euclidean
- type: pearson_dot
value: 0.8773449747713008
name: Pearson Dot
- type: spearman_dot
value: 0.8443939164633394
name: Spearman Dot
- type: pearson_max
value: 0.8883347646952768
name: Pearson Max
- type: spearman_max
value: 0.8463283813349622
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev test
type: sts-dev_test
metrics:
- type: pearson_cosine
value: 0.9278166656810813
name: Pearson Cosine
- type: spearman_cosine
value: 0.8783100656536799
name: Spearman Cosine
- type: pearson_manhattan
value: 0.954242190347034
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8783100656536799
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9519570678729806
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8783100656536799
name: Spearman Euclidean
- type: pearson_dot
value: 0.9258180799496141
name: Pearson Dot
- type: spearman_dot
value: 0.8783100656536799
name: Spearman Dot
- type: pearson_max
value: 0.954242190347034
name: Pearson Max
- type: spearman_max
value: 0.8783100656536799
name: Spearman Max
---
# SentenceTransformer based on WhereIsAI/UAE-Large-V1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) <!-- at revision 52d9e291d9fc7fc7f5276ff077b26fd1880c7c4f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Naveen20o1/UAE_Large_V1_nav1")
# Run inference
sentences = [
'joint_venture_name',
'Organization',
'Person',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8883 |
| **spearman_cosine** | **0.8463** |
| pearson_manhattan | 0.8611 |
| spearman_manhattan | 0.8386 |
| pearson_euclidean | 0.8623 |
| spearman_euclidean | 0.8405 |
| pearson_dot | 0.8773 |
| spearman_dot | 0.8444 |
| pearson_max | 0.8883 |
| spearman_max | 0.8463 |
#### Semantic Similarity
* Dataset: `sts-dev_test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9278 |
| **spearman_cosine** | **0.8783** |
| pearson_manhattan | 0.9542 |
| spearman_manhattan | 0.8783 |
| pearson_euclidean | 0.952 |
| spearman_euclidean | 0.8783 |
| pearson_dot | 0.9258 |
| spearman_dot | 0.8783 |
| pearson_max | 0.9542 |
| spearman_max | 0.8783 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 899 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 4.33 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------|:---------------------------|:-----------------|
| <code>postcode</code> | <code>Communication</code> | <code>0.0</code> |
| <code>telephone_number</code> | <code>Communication</code> | <code>1.0</code> |
| <code>vehicle_type</code> | <code>Person</code> | <code>0.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 60 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 4.15 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.55</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------|:----------------------|:-----------------|
| <code>surgical_history</code> | <code>Person</code> | <code>0.0</code> |
| <code>count</code> | <code>Quantity</code> | <code>1.0</code> |
| <code>board</code> | <code>Person</code> | <code>0.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 11
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 11
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-dev_test_spearman_cosine |
|:-------:|:----:|:-------------:|:------:|:-----------------------:|:----------------------------:|
| 0.8772 | 50 | 2.6697 | - | - | - |
| 1.7544 | 100 | 0.5212 | 2.4196 | 0.8057 | - |
| 2.6316 | 150 | 0.3741 | - | - | - |
| 3.5088 | 200 | 0.0033 | 1.7749 | 0.8115 | - |
| 4.3860 | 250 | 0.0257 | - | - | - |
| 5.2632 | 300 | 0.0159 | 2.2808 | 0.8154 | - |
| 6.1404 | 350 | 0.0057 | - | - | - |
| 7.0175 | 400 | 0.0044 | 1.5027 | 0.8444 | - |
| 7.8947 | 450 | 0.0004 | - | - | - |
| 8.7719 | 500 | 0.0008 | 0.9416 | 0.8483 | - |
| 9.6491 | 550 | 0.0001 | - | - | - |
| 10.5263 | 600 | 0.0002 | 1.1264 | 0.8463 | - |
| 11.0 | 627 | - | - | - | 0.8783 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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Ariffiq99/COPA_KUCI_e_care_CRAB_xlm_roberta_base_finetuned | Ariffiq99 | 2024-06-17T08:30:24Z | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"multiple-choice",
"generated_from_trainer",
"base_model:Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_Base_Finetuned",
"base_model:finetune:Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_Base_Finetuned",
"license:mit",
"endpoints_compatible",
"region:us"
] | multiple-choice | 2024-06-17T08:21:41Z | ---
license: mit
base_model: Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_Base_Finetuned
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: COPA_KUCI_e_care_CRAB_xlm_roberta_base_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# COPA_KUCI_e_care_CRAB_xlm_roberta_base_finetuned
This model is a fine-tuned version of [Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_Base_Finetuned](https://huggingface.co/Ariffiq99/KUCI_e_care_CRAB_xlm_roberta_Base_Finetuned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3472
- F1: 0.646
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| No log | 1.0 | 250 | 0.6419 | 0.614 |
| 0.6799 | 2.0 | 500 | 0.6238 | 0.654 |
| 0.6799 | 3.0 | 750 | 0.6344 | 0.646 |
| 0.5169 | 4.0 | 1000 | 1.0708 | 0.64 |
| 0.5169 | 5.0 | 1250 | 1.0799 | 0.636 |
| 0.431 | 6.0 | 1500 | 1.2484 | 0.656 |
| 0.431 | 7.0 | 1750 | 1.3472 | 0.646 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
HiTZ/mt-hitz-eu-es | HiTZ | 2024-06-17T08:23:59Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"eu",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-17T08:21:04Z | ---
license: apache-2.0
language:
- eu
- es
metrics:
- BLEU
- TER
---
## Hitz Center’s Basque-Spanish machine translation model
## Model description
This model was trained from scratch using [Marian NMT](https://marian-nmt.github.io/) on a combination of Spanish-Basque datasets totalling 104,417,271 sentence pairs. 12,091,549 sentence pairs were parallel data collected from the web while the remaining 92,325,722 sentence pairs were parallel synthetic data created backtranslating [Oscar](https://oscar-project.org/) Spanish monolingual dataset. The model was evaluated on the Flores, TaCon and NTREX evaluation datasets.
- **Developed by:** HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
- **Model type:** traslation
- **Source Language:** Basque
- **Target Language:** Spanish
- **License:** apache-2.0
## Intended uses and limitations
You can use this model for machine translation from Basque to Spanish.
At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources.
## How to Get Started with the Model
Use the code below to get started with the model.
```
from transformers import MarianMTModel, MarianTokenizer
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
src_text = ["Hau proba bat da."]
model_name = "HiTZ/mt-hitz-eu-es"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=T
rue))
print([tokenizer.decode(t, skip_special_tokens=True) for t in translated])`
```
The recommended environments include the following transfomer versions: 4.12.3 , 4.15.0 , 4.26.1
## Training Details
### Training Data
The Spanish-Basque data collected from the web was a combination of the following datasets:
| Dataset | Sentences before cleaning |
|------------------------|--------------------------:|
| CCMatrix | 6,564,108 |
| MultiParaCrawl | 3,344,373 |
| Paracrawl | 2,410,895 |
| TranslationMemories_EJ | 1,127,141 |
| OpenData2017 (IWSLT18) | 926,941 |
| OpenSubtitles | 793,593 |
| TranslationMemories_GD | 788,776 |
| EhuHac | 609,912 |
| OPUS-Elhuyar | 642,347 |
| EiTB-ParCC | 637,182 |
| WikiMatrix | 154,281 |
| **Total** | ** 12,091,549 ** |
The 92,325,722 sentence pairs of synthetic parallel data were created by backtranslating the EusCrawl Basque monolingual dataset using a previous version (without synthetic parallel data) of the [ES-EU translator from the HiTZ center](https://huggingface.co/HiTZ/mt-hitz-es-eu).
### Training Procedure
#### Preprocessing
After concatenation, all datasets are cleaned and deduplicated using [bifixer](https://github.com/bitextor/bifixer) and [biclener](https://github.com/bitextor/bicleaner) tools [(Ramírez-Sánchez et al., 2020)](https://aclanthology.org/2020.eamt-1.31/). Any sentence pairs with a classification score of less than 0.5 is removed. The filtered corpus is composed of 100,843,973 parallel sentences.
#### Tokenization
All data is tokenized using sentencepiece, with a 32,000 token sentencepiece model learned from the combination of all filtered training data. This model is included.
## Evaluation
### Variable and metrics
We use the BLEU and TER scores for evaluation on test sets: [Flores-200](https://github.com/facebookresearch/flores/tree/main/flores200), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/) and [NTREX](https://github.com/MicrosoftTranslator/NTREX)
### Evaluation results
Below are the evaluation results on the machine translation from Basque to Spanish compared to [Google Translate](https://translate.google.com/) and [NLLB 200 3.3B](https://huggingface.co/facebook/nllb-200-3.3B):
####BLEU scores
| Test set |Google Translate | NLLB 3.3B |mt-hitz-eu-es|
|----------------------|-----------------|-----------|-------------|
| Flores 200 devtest |**22.1** | 21.3 | 20.4 |
| TaCON | 34.7 | 31.7 | **37.7** |
| NTREX | **28.8** | 27.8 | 26.9 |
| Average | **28.5** | 26.9 | 28.3 |
####TER scores
| Test set |Google Translate | NLLB 3.3 |mt-hitz-eu-es|
|----------------------|-----------------|----------|-------------|
| Flores 200 devtest |**59.2** | 61.6 | 61.2 |
| TaCON |**46.6** | 51.7 | **44.6** |
| NTREX |**55.5** | 57.6 | 57.2 |
| Average |**53.8** | 57.0 | 54.3 |
<!-- Momentuz ez dugu artikulurik. ILENIAn zerbait egiten bada eguneratu beharko da -->
<!--
## 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]
-->
## Additional information
### Author
HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
### Contact information
For further information, send an email to <[email protected]>
### Licensing information
This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA](https://proyectoilenia.es/) with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335 y 2022/TL22/00215334
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the models (HiTZ Research Center) be liable for any results arising from the use made by third parties of these models.
</details> |
HiTZ/mt-hitz-es-eu | HiTZ | 2024-06-17T08:20:10Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"es",
"eu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-17T08:19:26Z | ---
license: apache-2.0
language:
- es
- eu
metrics:
- BLEU
- TER
---
## Hitz Center’s Spanish-Basque machine translation model
## Model description
This model was trained from scratch using [Marian NMT](https://marian-nmt.github.io/) on a combination of Spanish-Basque datasets totalling 35,619,691 sentence pairs. 12,091,549 sentence pairs were parallel data collected from the web while the remaining 23,528,142 sentence pairs were parallel synthetic data created backtranslating [EusCrawl](https://www.ixa.eus/euscrawl/) Basque monolingual dataset. The model was evaluated on the Flores, TaCon and NTREX evaluation datasets.
- **Developed by:** HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
- **Model type:** traslation
- **Source Language:** Spanish
- **Target Language:** Basque
- **License:** apache-2.0
## Intended uses and limitations
You can use this model for machine translation from Spanish to Basque.
At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources.
## How to Get Started with the Model
Use the code below to get started with the model.
```
from transformers import MarianMTModel, MarianTokenizer
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
src_text = ["Esto es una prueba."]
model_name = "HiTZ/mt-hitz-es-eu"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=T
rue))
print([tokenizer.decode(t, skip_special_tokens=True) for t in translated])`
```
The recommended environments include the following transfomer versions: 4.12.3 , 4.15.0 , 4.26.1
## Training Details
### Training Data
The Spanish-Basque data collected from the web was a combination of the following datasets:
| Dataset | Sentences before cleaning |
|------------------------|--------------------------:|
| CCMatrix | 6,564,108 |
| MultiParaCrawl | 3,344,373 |
| Paracrawl | 2,410,895 |
| TranslationMemories_EJ | 1,127,141 |
| OpenData2017 (IWSLT18) | 926,941 |
| OpenSubtitles | 793,593 |
| TranslationMemories_GD | 788,776 |
| EhuHac | 609,912 |
| OPUS-Elhuyar | 642,347 |
| EiTB-ParCC | 637,182 |
| WikiMatrix | 154,281 |
| **Total** | ** 12,091,549 ** |
The 23,528,142 sentence pairs of synthetic parallel data were created by backtranslating the EusCrawl Basque monolingual dataset using a previous version (without synthetic parallel data) of the [EU-ES translator from the HiTZ center](https://huggingface.co/HiTZ/mt-hitz-eu-es).
### Training Procedure
#### Preprocessing
After concatenation, all datasets are cleaned and deduplicated using [bifixer](https://github.com/bitextor/bifixer) and [biclener](https://github.com/bitextor/bicleaner) tools [(Ramírez-Sánchez et al., 2020)](https://aclanthology.org/2020.eamt-1.31/). Any sentence pairs with a classification score of less than 0.5 is removed. The filtered corpus is composed of 30,776,776 parallel sentences.
#### Tokenization
All data is tokenized using sentencepiece, with a 32,000 token sentencepiece model learned from the combination of all filtered training data. This model is included.
## Evaluation
### Variable and metrics
We use the BLEU and TER scores for evaluation on test sets: [Flores-200](https://github.com/facebookresearch/flores/tree/main/flores200), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/) and [NTREX](https://github.com/MicrosoftTranslator/NTREX)
### Evaluation results
Below are the evaluation results on the machine translation from Spanish to Basque compared to [Google Translate](https://translate.google.com/) and [NLLB 200 3.3B](https://huggingface.co/facebook/nllb-200-3.3B):
####BLEU scores
| Test set |Google Translate | NLLB 3.3 |mt-hitz-es-eu|
|----------------------|-----------------|-----------|-------------|
| Flores 200 devtest | 13.7 | 11.7 | **13.8** |
| TaCON | **14.2** | 11.3 | 13.7 |
| NTREX | 13.9 | 11.3 | **14.3** |
| Average | 13.9 | 11.4 | **14.1** |
####TER scores
| Test set |Google Translate | NLLB 3.3 |mt-hitz-es-eu|
|----------------------|-----------------|----------|-------------|
| Flores 200 devtest |**70.4** | 74.2 | 71.1 |
| TaCON |**63.3** | 72.0 | 66.7 |
| NTREX |**69.5** | 74.3 | 69.7 |
| Average |**67.7** | 73.5 | 69.2 |
<!-- Momentuz ez dugu artikulurik. ILENIAn zerbait egiten bada eguneratu beharko da -->
<!--
## 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]
-->
## Additional information
### Author
HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
### Contact information
For further information, send an email to <[email protected]>
### Licensing information
This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA](https://proyectoilenia.es/) with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335 y 2022/TL22/00215334
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the models (HiTZ Research Center) be liable for any results arising from the use made by third parties of these models.
</details> |
HiTZ/mt-hitz-en-eu | HiTZ | 2024-06-17T08:17:56Z | 104 | 3 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"en",
"eu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-07T07:36:39Z | ---
license: apache-2.0
language:
- en
- eu
metrics:
- BLEU
- TER
---
## Hitz Center’s English-Basque machine translation model
## Model description
This model was trained from scratch using [Marian NMT](https://marian-nmt.github.io/) on a combination of English-Basque datasets totalling 20,523,431 sentence pairs. 9,033,998 sentence pairs were parallel data collected from the web while the remaining 11,489,433 sentence pairs were parallel synthetic data created using the [Google Translate translator](https://translate.google.com/about/). The model was evaluated on the Flores, TaCon and NTREX evaluation datasets.
- **Developed by:** HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
- **Model type:** traslation
- **Source Language:** English
- **Target Language:** Basque
- **License:** apache-2.0
## Intended uses and limitations
You can use this model for machine translation from English to Basque.
At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources.
## How to Get Started with the Model
Use the code below to get started with the model.
```
from transformers import MarianMTModel, MarianTokenizer
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
src_text = ["this is a test"]
model_name = "HiTZ/mt-hitz-en-eu"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=T
rue))
print([tokenizer.decode(t, skip_special_tokens=True) for t in translated])`
```
The recommended environments include the following transfomer versions: 4.12.3 , 4.15.0 , 4.26.1
## Training Details
### Training Data
The English-Basque data collected from the web was a combination of the following datasets:
| Dataset | Sentences before cleaning |
|-----------------|--------------------------:|
| CCMatrix v1 | 7,788,871 |
| EhuHac | 585,210 |
| Ehuskaratuak | 482,259 |
| Ehuskaratuak | 482,259 |
| Elhuyar | 1,176,529 |
| HPLT | 4,546,563 |
| OpenSubtitles | 805,780 |
| PaCO_2012 | 109,524 |
| PaCO_2013 | 48,892 |
| WikiMatrix | 119,480 |
| **Total** | **15,653,108** |
The 11,489,433 sentence pairs of synthetic parallel data were created by translating a compendium of ES-EU parallel corpora into English using the [ES-EN translator from Google Translate](https://translate.google.com/about/).
### Training Procedure
#### Preprocessing
After concatenation, all datasets are cleaned and deduplicated using [bifixer](https://github.com/bitextor/bifixer) [(Ramírez-Sánchez et al., 2020)](https://aclanthology.org/2020.eamt-1.31/) for identifying repetions and cleaning encoding problems and LaBSE embeddings to filter missaligned sentences. Any sentence pairs with a LaBSE similarity score of less than 0.5 is removed. The filtered corpus is composed of 9,033,998 parallel sentences.
#### Tokenization
All data is tokenized using sentencepiece, with a 32,000 token sentencepiece model learned from the combination of all filtered training data. This model is included.
## Evaluation
### Variable and metrics
We use the BLEU and TER scores for evaluation on test sets: [Flores-200](https://github.com/facebookresearch/flores/tree/main/flores200), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/) and [NTREX](https://github.com/MicrosoftTranslator/NTREX)
### Evaluation results
Below are the evaluation results on the machine translation from English to Basque compared to [Google Translate](https://translate.google.com/) and [NLLB 200 3.3B](https://huggingface.co/facebook/nllb-200-3.3B):
####BLEU scores
| Test set |Google Translate | NLLB 3.3 |mt-hitz-en-eu|
|----------------------|-----------------|----------|-------------|
| Flores 200 devtest |**20.5** | 13.3 | 19.2 |
| TaCON | **12.1** | 9.4 | 8.8 |
| NTREX | **15.7** | 8.0 | 14.5 |
| Average | **16.1** | 10.2 | 14.2 |
####TER scores
| Test set |Google Translate | NLLB 3.3 |mt-hitz-en-eu|
|----------------------|-----------------|----------|-------------|
| Flores 200 devtest |**59.5** | 70.4 | 65.0 |
| TaCON |**69.5** | 75.3 | 76.8 |
| NTREX |**65.8** | 81.6 | 66.7 |
| Average |**64.9** | 75.8 | **68.2** |
<!-- Momentuz ez dugu artikulurik. ILENIAn zerbait egiten bada eguneratu beharko da -->
<!--
## 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]
-->
## Additional information
### Author
HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
### Contact information
For further information, send an email to <[email protected]>
### Licensing information
This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA](https://proyectoilenia.es/) with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335 y 2022/TL22/00215334
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the models (HiTZ Research Center) be liable for any results arising from the use made by third parties of these models.
</details> |
anarvaez99/finetuning-sentiment-model-3000-samples | anarvaez99 | 2024-06-17T08:16:27Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-13T03:31:47Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3854
- Accuracy: 0.8667
- F1: 0.8693
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0.post301
- Datasets 2.20.0
- Tokenizers 0.19.1
|
RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf | RichardErkhov | 2024-06-17T08:16:17Z | 7 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T06:48:29Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Copium-Cola-9B - GGUF
- Model creator: https://huggingface.co/Nitral-AI/
- Original model: https://huggingface.co/Nitral-AI/Copium-Cola-9B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Copium-Cola-9B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q2_K.gguf) | Q2_K | 3.13GB |
| [Copium-Cola-9B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.IQ3_XS.gguf) | IQ3_XS | 3.48GB |
| [Copium-Cola-9B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.IQ3_S.gguf) | IQ3_S | 3.67GB |
| [Copium-Cola-9B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q3_K_S.gguf) | Q3_K_S | 3.65GB |
| [Copium-Cola-9B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.IQ3_M.gguf) | IQ3_M | 3.79GB |
| [Copium-Cola-9B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q3_K.gguf) | Q3_K | 4.05GB |
| [Copium-Cola-9B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q3_K_M.gguf) | Q3_K_M | 4.05GB |
| [Copium-Cola-9B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q3_K_L.gguf) | Q3_K_L | 4.41GB |
| [Copium-Cola-9B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.IQ4_XS.gguf) | IQ4_XS | 4.55GB |
| [Copium-Cola-9B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q4_0.gguf) | Q4_0 | 4.74GB |
| [Copium-Cola-9B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.IQ4_NL.gguf) | IQ4_NL | 4.79GB |
| [Copium-Cola-9B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q4_K_S.gguf) | Q4_K_S | 4.78GB |
| [Copium-Cola-9B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q4_K.gguf) | Q4_K | 5.04GB |
| [Copium-Cola-9B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q4_K_M.gguf) | Q4_K_M | 5.04GB |
| [Copium-Cola-9B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q4_1.gguf) | Q4_1 | 5.26GB |
| [Copium-Cola-9B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q5_0.gguf) | Q5_0 | 5.77GB |
| [Copium-Cola-9B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q5_K_S.gguf) | Q5_K_S | 5.77GB |
| [Copium-Cola-9B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q5_K.gguf) | Q5_K | 5.93GB |
| [Copium-Cola-9B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q5_K_M.gguf) | Q5_K_M | 5.93GB |
| [Copium-Cola-9B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q5_1.gguf) | Q5_1 | 6.29GB |
| [Copium-Cola-9B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q6_K.gguf) | Q6_K | 6.87GB |
| [Copium-Cola-9B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Nitral-AI_-_Copium-Cola-9B-gguf/blob/main/Copium-Cola-9B.Q8_0.gguf) | Q8_0 | 8.89GB |
Original model description:
---
base_model:
- ChaoticNeutrals/Eris_7B
library_name: transformers
tags:
- mergekit
- merge
license: other
---
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* [ChaoticNeutrals/Eris_7B](https://huggingface.co/ChaoticNeutrals/Eris_7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: ChaoticNeutrals/Eris_7B
layer_range: [0, 20]
- sources:
- model: ChaoticNeutrals/Eris_7B
layer_range: [12, 32]
merge_method: passthrough
dtype: float16
```
|
Dhahlan2000/Chitti-Base-model-for-GPT-v2 | Dhahlan2000 | 2024-06-17T08:15:51Z | 115 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Dhahlan2000/Chitti-Base-model-for-GPT-v1",
"base_model:finetune:Dhahlan2000/Chitti-Base-model-for-GPT-v1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-17T08:15:35Z | ---
license: apache-2.0
base_model: Dhahlan2000/Chitti-Base-model-for-GPT-v1
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Chitti-Base-model-for-GPT-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Chitti-Base-model-for-GPT-v2
This model is a fine-tuned version of [Dhahlan2000/Chitti-Base-model-for-GPT-v1](https://huggingface.co/Dhahlan2000/Chitti-Base-model-for-GPT-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3208
- Bleu: 1.2028
- Gen Len: 13.7173
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 3.5683 | 1.0 | 9282 | 3.3450 | 1.04 | 13.7287 |
| 3.5534 | 2.0 | 18564 | 3.3208 | 1.2028 | 13.7173 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
hungsvdut2k2/vistral-rank-16 | hungsvdut2k2 | 2024-06-17T08:14:52Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:Viet-Mistral/Vistral-7B-Chat",
"base_model:quantized:Viet-Mistral/Vistral-7B-Chat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-05T07:39:23Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: Viet-Mistral/Vistral-7B-Chat
---
# Uploaded model
- **Developed by:** hungsvdut2k2
- **License:** apache-2.0
- **Finetuned from model :** Viet-Mistral/Vistral-7B-Chat
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)
|
brugmark/all-MiniLM-L6-v2-personal-project-default-2024-06-17 | brugmark | 2024-06-17T08:14:10Z | 133 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-06-17T08:08:49Z | ---
license: apache-2.0
base_model: sentence-transformers/all-MiniLM-L6-v2
tags:
- generated_from_trainer
model-index:
- name: all-MiniLM-L6-v2-personal-project-default-2024-06-17
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. -->
# all-MiniLM-L6-v2-personal-project-default-2024-06-17
This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 10.7428
- eval_runtime: 307.7457
- eval_samples_per_second: 812.18
- eval_steps_per_second: 25.381
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
SumitxThokar/idefics-9b-guns2 | SumitxThokar | 2024-06-17T08:11:25Z | 78 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T07:50:12Z | ---
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] |
adhirajpandey/llama-3-8b-chat-wbn | adhirajpandey | 2024-06-17T08:11:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T07:28:07Z | ---
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] |
rushilJariwala/bert-base-cased-paraphrase-classification | rushilJariwala | 2024-06-17T08:09:55Z | 119 | 2 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"code",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T06:21:26Z | ---
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- code
---
# Model Card for Bert-base-cased Paraphrase Classification
## Model Details
### Model Description
The **bert-base-cased-paraphrase-classification** model is a fine-tuned version of the BERT (Bidirectional Encoder Representations from Transformers) architecture specifically designed for paraphrase classification. It uses the cased variant of BERT as the base model. This model has been fine-tuned for identifying whether two input sentences are paraphrases of each other.
- **Developed by:** Rushil Jariwala
- **Model type:** Transformer-based neural network
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** BERT-base-cased
### Model Sources
- **Repository:** [Hugging Face Model Hub](https://huggingface.co/rushilJariwala/bert-base-cased-paraphrase-classification)
## Uses
### Direct Use
This model can directly classify whether two sentences are paraphrases of each other.
### Downstream Use
When fine-tuned on a specific task or integrated into a larger application, this model can assist in tasks requiring paraphrase identification.
### Out-of-Scope Use
This model may not perform optimally on sentences with highly domain-specific vocabulary not seen during training, and it is limited to the English language.
## Bias, Risks, and Limitations
This model's performance may vary based on the similarity of sentences to those in the training data. It may exhibit biases based on the dataset used for training.
### Recommendations
Users should consider domain-specific fine-tuning for optimal performance in specific applications. Additionally, careful evaluation and validation are recommended for critical applications.
## How to Get Started with the Model
Use the following Python code to get started with the model:
```python
from transformers import pipeline
pipe = pipeline("text-classification", model="rushilJariwala/bert-base-cased-paraphrase-classification")
sequences = [
"I've been waiting for a HuggingFace course my whole life.",
"This course is amazing!",
]
result = pipe(sequences)
print(result)
#### Preprocessing
The text was tokenized using BERT's cased tokenizer with truncation and padding.
#### 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 -->
- Batch Size: 8
- Learning Rate: 5e-5
- Optimizer: AdamW
- Number of Epochs: 3
#### Testing Data
The model was evaluated on the MRPC validation set.
#### Metrics
Accuracy: 86.27%
#### Summary
The model achieved an accuracy of 86.27% on the MRPC validation set.
|
SoDehghan/test-target | SoDehghan | 2024-06-17T08:09:20Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T08:04:02Z | ---
license: apache-2.0
---
|
GAI-LLM/myungdonggil | GAI-LLM | 2024-06-17T08:05:12Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T06:59:46Z | ---
license: cc-by-nc-4.0
---
|
mc0c0z/t5-base-sst2 | mc0c0z | 2024-06-17T08:02:49Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-17T08:02:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Niggendar/fastPhotoPony_v20 | Niggendar | 2024-06-17T08:02:42Z | 98 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-06-17T07:56:08Z | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
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revelacion1/RF_course_cartPole_base_model | revelacion1 | 2024-06-17T08:01:01Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2024-06-17T08:00:59Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: RF_course_cartPole_base_model
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 17.60 +/- 3.80
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
Based model created from RF course part 4
|
longxia/Qwen-Qwen1.5-1.8B-1718611037 | longxia | 2024-06-17T07:57:22Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-17T07:57:18Z | ---
library_name: peft
base_model: Qwen/Qwen1.5-1.8B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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<!-- 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.11.1 |
MaziyarPanahi/mergekit-slerp-rcoqutv-GGUF | MaziyarPanahi | 2024-06-17T07:55:57Z | 8 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:WizardLM/WizardMath-7B-V1.1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-rcoqutv",
"base_model:quantized:mergekit-community/mergekit-slerp-rcoqutv"
] | text-generation | 2024-06-17T07:33:47Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- conversational
- base_model:NousResearch/Hermes-2-Pro-Mistral-7B
- base_model:WizardLM/WizardMath-7B-V1.1
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-rcoqutv-GGUF
base_model: mergekit-community/mergekit-slerp-rcoqutv
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-rcoqutv-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-rcoqutv-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-rcoqutv](https://huggingface.co/mergekit-community/mergekit-slerp-rcoqutv)
## Description
[MaziyarPanahi/mergekit-slerp-rcoqutv-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-rcoqutv-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-rcoqutv](https://huggingface.co/mergekit-community/mergekit-slerp-rcoqutv).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
longxia/google-gemma-2b-1718610853 | longxia | 2024-06-17T07:54:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | 2024-06-17T07:54:14Z | ---
library_name: peft
base_model: google/gemma-2b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- 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
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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[More Information Needed]
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[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]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.11.1 |
DAILAB-bitesnail/distilbert-base-uncased-finetuned-emotion | DAILAB-bitesnail | 2024-06-17T07:53:47Z | 118 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-17T07:44:02Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.8885
- name: F1
type: f1
value: 0.8814348986502284
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3845
- Accuracy: 0.8885
- F1: 0.8814
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.5905 | 0.799 | 0.7625 |
| No log | 2.0 | 250 | 0.3845 | 0.8885 | 0.8814 |
### Framework versions
- Transformers 4.41.2
- Pytorch 1.13.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
k4west/kpopLlama-3-8B-sentiment_3 | k4west | 2024-06-17T07:52:04Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T07:38:22Z | ---
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.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
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Ariffiq99/CRAB_COPA_KUCI_e_care_xlm_roberta_base_finetuned | Ariffiq99 | 2024-06-17T07:51:58Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"multiple-choice",
"generated_from_trainer",
"base_model:Ariffiq99/COPA_KUCI_e_care_xlm_roberta_base_finetuned",
"base_model:finetune:Ariffiq99/COPA_KUCI_e_care_xlm_roberta_base_finetuned",
"license:mit",
"endpoints_compatible",
"region:us"
] | multiple-choice | 2024-06-17T07:23:45Z | ---
license: mit
base_model: Ariffiq99/COPA_KUCI_e_care_xlm_roberta_base_finetuned
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: CRAB_COPA_KUCI_e_care_xlm_roberta_base_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CRAB_COPA_KUCI_e_care_xlm_roberta_base_finetuned
This model is a fine-tuned version of [Ariffiq99/COPA_KUCI_e_care_xlm_roberta_base_finetuned](https://huggingface.co/Ariffiq99/COPA_KUCI_e_care_xlm_roberta_base_finetuned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0585
- F1: 0.7292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.175 | 1.0 | 2880 | 1.0861 | 0.6889 |
| 0.9462 | 2.0 | 5760 | 1.1240 | 0.7208 |
| 0.7888 | 3.0 | 8640 | 0.9307 | 0.7014 |
| 0.9436 | 4.0 | 11520 | 1.1582 | 0.7194 |
| 0.8077 | 5.0 | 14400 | 1.0373 | 0.7236 |
| 0.8208 | 6.0 | 17280 | 1.1081 | 0.7292 |
| 0.7648 | 7.0 | 20160 | 1.0421 | 0.7306 |
| 0.6384 | 8.0 | 23040 | 1.0585 | 0.7292 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
habin/llama2-kornerstone-8b-ko | habin | 2024-06-17T07:43:13Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T07:32:04Z | ---
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. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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Niggendar/edgOnPony_v10 | Niggendar | 2024-06-17T07:37:59Z | 79 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-06-17T07:30:53Z | ---
library_name: diffusers
---
# 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 🧨 diffusers 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]
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[More Information Needed]
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[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- 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
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[More Information Needed]
## Training Details
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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glouriousgautam/Qwen2-1.5b-oasstguanaco-qdora-merged | glouriousgautam | 2024-06-17T07:37:19Z | 84 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T07:30:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
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[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]
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[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
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ninyx/Mistral-7B-Instruct-v0.3-advisegpt-v0.4 | ninyx | 2024-06-17T07:28:48Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2024-06-14T11:46:17Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.3
datasets:
- generator
metrics:
- bleu
- rouge
model-index:
- name: Mistral-7B-Instruct-v0.3-advisegpt-v0.4
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. -->
# Mistral-7B-Instruct-v0.3-advisegpt-v0.4
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0776
- Bleu: {'bleu': 0.9592766854579555, 'precisions': [0.9778672968005702, 0.9629777800504739, 0.952562376464522, 0.9440303244645156], 'brevity_penalty': 1.0, 'length_ratio': 1.0002070868729431, 'translation_length': 666525, 'reference_length': 666387}
- Rouge: {'rouge1': 0.9765393241338379, 'rouge2': 0.960274899679536, 'rougeL': 0.9752854409851488, 'rougeLsum': 0.9763366883065228}
- Exact Match: {'exact_match': 0.0}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 15
- total_train_batch_size: 15
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | Exact Match |
|:-------------:|:------:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------:|:--------------------:|
| 0.0592 | 0.9998 | 2664 | 0.0792 | {'bleu': 0.957140829496306, 'precisions': [0.9770110285842899, 0.9611535701983837, 0.9499650178830994, 0.9408134298916666], 'brevity_penalty': 1.0, 'length_ratio': 1.0000945396593872, 'translation_length': 666450, 'reference_length': 666387} | {'rouge1': 0.9756420869808171, 'rouge2': 0.958253583847128, 'rougeL': 0.9741670140375769, 'rougeLsum': 0.9753898276329086} | {'exact_match': 0.0} |
| 0.0518 | 2.0000 | 5329 | 0.0776 | {'bleu': 0.9592766854579555, 'precisions': [0.9778672968005702, 0.9629777800504739, 0.952562376464522, 0.9440303244645156], 'brevity_penalty': 1.0, 'length_ratio': 1.0002070868729431, 'translation_length': 666525, 'reference_length': 666387} | {'rouge1': 0.9765393241338379, 'rouge2': 0.960274899679536, 'rougeL': 0.9752854409851488, 'rougeLsum': 0.9763366883065228} | {'exact_match': 0.0} |
| 0.0439 | 2.9994 | 7992 | 0.0830 | {'bleu': 0.9593680325138967, 'precisions': [0.97789654044549, 0.9630261327317164, 0.9526617494511856, 0.9442157972615742], 'brevity_penalty': 1.0, 'length_ratio': 1.0001725723941193, 'translation_length': 666502, 'reference_length': 666387} | {'rouge1': 0.9766709553577743, 'rouge2': 0.9604006931620985, 'rougeL': 0.9753845279467352, 'rougeLsum': 0.9764641972952484} | {'exact_match': 0.0} |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.2.0
- Datasets 2.19.1
- Tokenizers 0.19.1 |
xirigh/mymodel | xirigh | 2024-06-17T07:27:58Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-17T07:18:37Z | ---
license: apache-2.0
---
|
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