modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 18:29:29
| downloads
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223M
| likes
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11.7k
| library_name
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Arpan908/Arpan
|
Arpan908
| 2024-02-27T05:57:14Z | 0 | 0 | null |
[
"finance",
"summarization",
"ae",
"dataset:teknium/OpenHermes-2.5",
"arxiv:1910.09700",
"region:us"
] |
summarization
| 2024-02-27T05:52:56Z |
---
datasets:
- teknium/OpenHermes-2.5
language:
- ae
metrics:
- bertscore
pipeline_tag: summarization
tags:
- finance
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
vyshnavidasa/my-pet-dog
|
vyshnavidasa
| 2024-02-27T05:55:27Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-27T05:51:14Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by vyshnavidasa following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
anhtranhong/fingpt-mt_llama2-7b_lora_with_fiqa-qa-test
|
anhtranhong
| 2024-02-27T05:54:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T05:53:48Z |
---
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]
|
jsingh/dpo_rlaif_v0.1
|
jsingh
| 2024-02-27T05:49:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T05:49:13Z |
---
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]
|
prava09/my-pet-dog
|
prava09
| 2024-02-27T05:48:53Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-27T05:44:44Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by prava09 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
Annuu/my-pet-dog
|
Annuu
| 2024-02-27T05:42:50Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-27T05:38:16Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by Annuu following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
bartowski/Senzu-7B-v0.1-DPO-exl2
|
bartowski
| 2024-02-27T05:40:37Z | 0 | 1 | null |
[
"generated_from_trainer",
"text-generation",
"dataset:practical-dreamer/RPGPT_PublicDomain-alpaca",
"dataset:shuyuej/metamath_gsm8k",
"dataset:NeuralNovel/Neural-DPO",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-02-27T05:25:56Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
datasets:
- practical-dreamer/RPGPT_PublicDomain-alpaca
- shuyuej/metamath_gsm8k
- NeuralNovel/Neural-DPO
tags:
- generated_from_trainer
model-index:
- name: out
results: []
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Senzu-7B-v0.1-DPO
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.14">turboderp's ExLlamaV2 v0.0.14</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/NeuralNovel/Senzu-7B-v0.1-DPO
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Senzu-7B-v0.1-DPO-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Senzu-7B-v0.1-DPO-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Senzu-7B-v0.1-DPO-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Senzu-7B-v0.1-DPO-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Senzu-7B-v0.1-DPO-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Senzu-7B-v0.1-DPO-exl2 Senzu-7B-v0.1-DPO-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Senzu-7B-v0.1-DPO-exl2`:
```shell
mkdir Senzu-7B-v0.1-DPO-exl2
huggingface-cli download bartowski/Senzu-7B-v0.1-DPO-exl2 --local-dir Senzu-7B-v0.1-DPO-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Senzu-7B-v0.1-DPO-exl2-6_5
huggingface-cli download bartowski/Senzu-7B-v0.1-DPO-exl2 --revision 6_5 --local-dir Senzu-7B-v0.1-DPO-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Senzu-7B-v0.1-DPO-exl2-6.5
huggingface-cli download bartowski/Senzu-7B-v0.1-DPO-exl2 --revision 6_5 --local-dir Senzu-7B-v0.1-DPO-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
LinWeizheDragon/PreFLMR_ViT-L
|
LinWeizheDragon
| 2024-02-27T05:35:20Z | 1,124 | 1 |
transformers
|
[
"transformers",
"safetensors",
"flmr",
"feature-extraction",
"retrieval",
"multi-modal",
"knowledge-based visual question answering",
"FLMR",
"PreFLMR",
"custom_code",
"en",
"arxiv:2402.08327",
"license:mit",
"region:us"
] |
feature-extraction
| 2024-02-20T02:11:20Z |
---
library_name: transformers
license: mit
language:
- en
tags:
- retrieval
- multi-modal
- knowledge-based visual question answering
- FLMR
- PreFLMR
---
# PreFLMR model card
PreFLMR is an open-source model for multimodal knowledge retrieval. It is a transformer-based model that uses a combination of text and image inputs to retrieve relevant documents from a large corpus.
## Model Details
### Model Description
- **Model type:** FLMRModelForRetrieval
- **Language(s) (NLP):** English
- **License:** MIT License
### Paper and resources for more detail
- **Blog Post for quick overview:** https://www.jinghong-chen.net/preflmr-sota-open-sourced-multi/
- **Paper:** https://arxiv.org/abs/2402.08327
- **Gradio Demo:** https://u60544-b8d4-53eaa55d.westx.seetacloud.com:8443/
- **Repository:** https://github.com/LinWeizheDragon/FLMR
- **Project Page:** https://preflmr.github.io/
## Uses
### Direct Use
This model can be used directly to retrieve documents from a large corpus using a combination of text and image input queries. The retrieval usage can be found in the [official implementation](https://github.com/LinWeizheDragon/FLMR).
### Downstream Use
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
This model can be used combined with language models to create a retrieval-augmented language model. The use for Knowledge-based VQA can be found in [RAVQA](https://github.com/linweizhedragon/retrieval-augmented-visual-question-answering)
## How to Get Started with the Model
For details of training, indexing, and performing retrieval, please refer to [here](https://github.com/LinWeizheDragon/FLMR).
## Training datasets
The model is pre-trained on three types of tasks with a total of nine datasets:
1. Image to Text retrieval: WIT, KVQA, and CC3M
2. Question to Text retrieval: MSMARCO
3. Image & Question to Text retrieval: LLaVA, OVEN, OKVQA, Infoseek and E-VQA
These datasets were converted to retrieval format. For details on the dataset split and conversion process, please refer to the paper [PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers](https://arxiv.org/abs/2402.08327). We will release the proprocessed datasets soon.
## Evaluation datasets
We evaluate our models on WIT, LLaVA, OVEN, KVQA, IGLUE (subset of WIT), Infoseek, E-VQA, OKVQA and MSMARCO.
| Model | Vision Encoder | Text Encoder | Checkpoint Name | No. Param. | WIT | LLaVA | OVEN | KVQA | IGLUE | Infoseek | E-VQA | OKVQA | MSMARCO |
|---------|----------------|--------------|-------------------------------------------------------------|-------|-------|--------|-------|-------|-------|----------|-------|--------|-------|
| PreFLMR | ViT-B | Base-v2 | [LinWeizheDragon/PreFLMR_ViT-B](https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-B) | 327M | 41.7 | 67.2 | 46.3 | 28.6 | 57.3 | 48.8 | 67.9 | 66.1 | 79.5 |
| PreFLMR | ViT-L | Base-v2 | [LinWeizheDragon/PreFLMR_ViT-L](https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-L) | 543M | 60.5 | 71.8 | 59.8 | 43.6 | 69.2 | 57.9 | 70.8 | 68.5 | 78.7 |
| PreFLMR | ViT-G | Base-v2 | [LinWeizheDragon/PreFLMR_ViT-G](https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-G) | 2.1B | 61.5 | 72.4 | 63.4 | 42.1 |71.5 | 59.6 | 73.1 | 68.6 | 78.6 |
For the evaluation metrics, WIT uses Recall@10, IGLUE uses Recall@1, and all the rest datasets use Recall@5.
## Citation
**BibTeX:**
```
@article{Lin_Mei_Chen_Byrne_2024,
title={PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers},
url={http://arxiv.org/abs/2402.08327},
number={arXiv:2402.08327},
publisher={arXiv},
author={Lin, Weizhe and Mei, Jingbiao and Chen, Jinghong and Byrne, Bill},
year={2024}}
```
|
Abyuday/my-pet-dog
|
Abyuday
| 2024-02-27T05:34:15Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-27T05:29:52Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by Abyuday following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
htp40400/Reinforce-pixelcopter-v1
|
htp40400
| 2024-02-27T05:33:56Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-27T05:33:53Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 23.00 +/- 13.51
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
angel450/KoAlpaca-Polyglot-5.8B-kra-LoRA
|
angel450
| 2024-02-27T05:31:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-26T11:16:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
|
Prateekjain24/autotrain-fco56-qnzow
|
Prateekjain24
| 2024-02-27T05:28:04Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2024-02-27T05:28:01Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Propertyguru marketing banner
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
Preethi1234/my-pet-dog
|
Preethi1234
| 2024-02-27T05:18:54Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-27T05:08:55Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by Preethi1234 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
.png)
|
Dangurangu/LaBSE-masakhane-news-finetuned-shona
|
Dangurangu
| 2024-02-27T05:16:10Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2024-02-27T05:05:04Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaBSE-masakhane-news-finetuned-shona
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaBSE-masakhane-news-finetuned-shona")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
DimalChathuranga/bert-finetuned-ner
|
DimalChathuranga
| 2024-02-27T05:10:00Z | 106 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-02-27T04:38:26Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0563
- Precision: 0.9290
- Recall: 0.9487
- F1: 0.9387
- Accuracy: 0.9863
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0776 | 1.0 | 1756 | 0.0800 | 0.9070 | 0.9310 | 0.9189 | 0.9790 |
| 0.0399 | 2.0 | 3512 | 0.0577 | 0.9239 | 0.9458 | 0.9347 | 0.9849 |
| 0.0264 | 3.0 | 5268 | 0.0563 | 0.9290 | 0.9487 | 0.9387 | 0.9863 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.2
|
khaterm/fine_tuned_sparql_model2
|
khaterm
| 2024-02-27T04:58:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T04:58:48Z |
---
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]
|
evannaderi/distilbert-base-uncased-finetuned-emotion
|
evannaderi
| 2024-02-27T04:52:41Z | 118 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T01:48:52Z |
---
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: 0.933
- name: F1
type: f1
value: 0.932933898333218
---
<!-- 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: 0.1561
- Accuracy: 0.933
- F1: 0.9329
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.1706 | 0.9265 | 0.9265 |
| No log | 2.0 | 500 | 0.1561 | 0.933 | 0.9329 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
Evgeny105/my_model_e3
|
Evgeny105
| 2024-02-27T04:45:22Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-02-27T03:27:44Z |
---
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]
<!-- 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]
|
pankaj-munde/pixel_peft_model-new
|
pankaj-munde
| 2024-02-27T04:43:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T04:43:22Z |
---
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.
- **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]
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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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**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
pankaj-munde/toy_peft_model-new
|
pankaj-munde
| 2024-02-27T04:42:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T04:42:47Z |
---
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]
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- **Carbon Emitted:** [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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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
imagepipeline/Logo.Redmond-XL
|
imagepipeline
| 2024-02-27T04:41:55Z | 0 | 0 | null |
[
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-02-27T04:41:47Z |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## Logo.Redmond-XL
<img src="" alt="Generated on Image Pipeline" style="border-radius: 10px;">
**This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - LOGO LORA
[](https://imagepipeline.io/models/Logo.Redmond-XL?id=829471ee-11b8-402f-b3cb-ff87fe4bc911/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sdxl/text2image/v1/run"
payload = json.dumps({
"model_id": "sdxl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "829471ee-11b8-402f-b3cb-ff87fe4bc911",
"lora_weights": "0.5"
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sdxl/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at hello@imagepipeline.io
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
smahns/astrollama
|
smahns
| 2024-02-27T04:38:55Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null | 2024-02-27T04:38:52Z |
---
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: meta-llama/Llama-2-13b-chat-hf
model-index:
- name: astrollama
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. -->
# astrollama
This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- training_steps: 2000
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.1
- Pytorch 2.1.0.dev20230605+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
mins0o0/transforemr_16
|
mins0o0
| 2024-02-27T04:26:42Z | 98 | 0 |
transformers
|
[
"transformers",
"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
| 2024-02-27T04:26:10Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: transforemr_16
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. -->
# transforemr_16
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4841
- Bleu: 8.6082
- Gen Len: 17.5647
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 1.8767 | 1.0 | 6355 | 1.6443 | 7.2826 | 17.6214 |
| 1.7864 | 2.0 | 12710 | 1.5863 | 7.7743 | 17.5883 |
| 1.7465 | 3.0 | 19065 | 1.5544 | 8.0399 | 17.5689 |
| 1.7034 | 4.0 | 25420 | 1.5304 | 8.1983 | 17.5708 |
| 1.6912 | 5.0 | 31775 | 1.5148 | 8.3483 | 17.5603 |
| 1.6652 | 6.0 | 38130 | 1.5022 | 8.4549 | 17.5658 |
| 1.6534 | 7.0 | 44485 | 1.4951 | 8.5235 | 17.563 |
| 1.6615 | 8.0 | 50840 | 1.4884 | 8.562 | 17.5624 |
| 1.6426 | 9.0 | 57195 | 1.4854 | 8.5932 | 17.5643 |
| 1.6451 | 10.0 | 63550 | 1.4841 | 8.6082 | 17.5647 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0
- Datasets 2.17.1
- Tokenizers 0.15.2
|
ibrahimahmood/segformer-b0-finetuned-pidray-segments
|
ibrahimahmood
| 2024-02-27T04:22:26Z | 189 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2024-02-19T07:42:15Z |
---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-pidray-segments
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. -->
# segformer-b0-finetuned-pidray-segments
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the jaradat/pidray-semantics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2957
- Mean Iou: 0.3964
- Mean Accuracy: 0.7929
- Overall Accuracy: 0.7929
- Accuracy Baton: nan
- Accuracy Pliers: 0.7929
- Accuracy Hammer: nan
- Accuracy Powerbank: nan
- Accuracy Scissors: nan
- Accuracy Wrench: nan
- Accuracy Gun: nan
- Accuracy Bullet: nan
- Accuracy Sprayer: nan
- Accuracy Handcuffs: nan
- Accuracy Knife: nan
- Accuracy Lighter: nan
- Iou Baton: 0.0
- Iou Pliers: 0.7929
- Iou Hammer: nan
- Iou Powerbank: nan
- Iou Scissors: nan
- Iou Wrench: nan
- Iou Gun: nan
- Iou Bullet: nan
- Iou Sprayer: nan
- Iou Handcuffs: nan
- Iou Knife: nan
- Iou Lighter: nan
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Baton | Accuracy Pliers | Accuracy Hammer | Accuracy Powerbank | Accuracy Scissors | Accuracy Wrench | Accuracy Gun | Accuracy Bullet | Accuracy Sprayer | Accuracy Handcuffs | Accuracy Knife | Accuracy Lighter | Iou Baton | Iou Pliers | Iou Hammer | Iou Powerbank | Iou Scissors | Iou Wrench | Iou Gun | Iou Bullet | Iou Sprayer | Iou Handcuffs | Iou Knife | Iou Lighter |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:---------------:|:---------------:|:------------------:|:-----------------:|:---------------:|:------------:|:---------------:|:----------------:|:------------------:|:--------------:|:----------------:|:---------:|:----------:|:----------:|:-------------:|:------------:|:----------:|:-------:|:----------:|:-----------:|:-------------:|:---------:|:-----------:|
| 0.0583 | 0.5 | 20 | 0.1593 | 0.3956 | 0.7913 | 0.7913 | nan | 0.7913 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7913 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0374 | 1.0 | 40 | 0.1574 | 0.3840 | 0.7680 | 0.7680 | nan | 0.7680 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7680 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0231 | 1.5 | 60 | 0.1546 | 0.4221 | 0.8443 | 0.8443 | nan | 0.8443 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8443 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.1145 | 2.0 | 80 | 0.1491 | 0.4087 | 0.8174 | 0.8174 | nan | 0.8174 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8174 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.042 | 2.5 | 100 | 0.1537 | 0.4084 | 0.8168 | 0.8168 | nan | 0.8168 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8168 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0914 | 3.0 | 120 | 0.1718 | 0.3960 | 0.7920 | 0.7920 | nan | 0.7920 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7920 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0292 | 3.5 | 140 | 0.1526 | 0.3946 | 0.7891 | 0.7891 | nan | 0.7891 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7891 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0326 | 4.0 | 160 | 0.1557 | 0.3947 | 0.7895 | 0.7895 | nan | 0.7895 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7895 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0255 | 4.5 | 180 | 0.1555 | 0.4027 | 0.8055 | 0.8055 | nan | 0.8055 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8055 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0993 | 5.0 | 200 | 0.1742 | 0.3780 | 0.7561 | 0.7561 | nan | 0.7561 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7561 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0187 | 5.5 | 220 | 0.1466 | 0.3961 | 0.7923 | 0.7923 | nan | 0.7923 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7923 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0338 | 6.0 | 240 | 0.1673 | 0.4202 | 0.8403 | 0.8403 | nan | 0.8403 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8403 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.024 | 6.5 | 260 | 0.1798 | 0.4196 | 0.8392 | 0.8392 | nan | 0.8392 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8392 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0354 | 7.0 | 280 | 0.1829 | 0.4008 | 0.8016 | 0.8016 | nan | 0.8016 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8016 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0297 | 7.5 | 300 | 0.1861 | 0.4138 | 0.8276 | 0.8276 | nan | 0.8276 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8276 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0452 | 8.0 | 320 | 0.1759 | 0.3796 | 0.7593 | 0.7593 | nan | 0.7593 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7593 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0341 | 8.5 | 340 | 0.1689 | 0.4145 | 0.8289 | 0.8289 | nan | 0.8289 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8289 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0218 | 9.0 | 360 | 0.1623 | 0.4019 | 0.8039 | 0.8039 | nan | 0.8039 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8039 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.018 | 9.5 | 380 | 0.1724 | 0.3974 | 0.7947 | 0.7947 | nan | 0.7947 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7947 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0265 | 10.0 | 400 | 0.1544 | 0.4123 | 0.8245 | 0.8245 | nan | 0.8245 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8245 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0529 | 10.5 | 420 | 0.1754 | 0.3957 | 0.7915 | 0.7915 | nan | 0.7915 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7915 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.054 | 11.0 | 440 | 0.2007 | 0.3811 | 0.7622 | 0.7622 | nan | 0.7622 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7622 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0182 | 11.5 | 460 | 0.1723 | 0.3874 | 0.7747 | 0.7747 | nan | 0.7747 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7747 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0289 | 12.0 | 480 | 0.1668 | 0.4180 | 0.8360 | 0.8360 | nan | 0.8360 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8360 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0157 | 12.5 | 500 | 0.1788 | 0.3752 | 0.7504 | 0.7504 | nan | 0.7504 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7504 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0156 | 13.0 | 520 | 0.1649 | 0.3936 | 0.7871 | 0.7871 | nan | 0.7871 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7871 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0396 | 13.5 | 540 | 0.1771 | 0.4034 | 0.8068 | 0.8068 | nan | 0.8068 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8068 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.034 | 14.0 | 560 | 0.1767 | 0.4066 | 0.8132 | 0.8132 | nan | 0.8132 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8132 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0273 | 14.5 | 580 | 0.1668 | 0.4068 | 0.8136 | 0.8136 | nan | 0.8136 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8136 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0221 | 15.0 | 600 | 0.1635 | 0.4125 | 0.8250 | 0.8250 | nan | 0.8250 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8250 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0189 | 15.5 | 620 | 0.1886 | 0.4045 | 0.8091 | 0.8091 | nan | 0.8091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0201 | 16.0 | 640 | 0.1736 | 0.3985 | 0.7970 | 0.7970 | nan | 0.7970 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7970 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0328 | 16.5 | 660 | 0.1776 | 0.3889 | 0.7778 | 0.7778 | nan | 0.7778 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7778 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0368 | 17.0 | 680 | 0.1925 | 0.4113 | 0.8227 | 0.8227 | nan | 0.8227 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8227 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0185 | 17.5 | 700 | 0.1857 | 0.3852 | 0.7705 | 0.7705 | nan | 0.7705 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7705 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0224 | 18.0 | 720 | 0.1763 | 0.3972 | 0.7943 | 0.7943 | nan | 0.7943 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7943 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0204 | 18.5 | 740 | 0.1955 | 0.3912 | 0.7823 | 0.7823 | nan | 0.7823 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7823 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0162 | 19.0 | 760 | 0.1896 | 0.3985 | 0.7970 | 0.7970 | nan | 0.7970 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7970 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0672 | 19.5 | 780 | 0.1873 | 0.3994 | 0.7987 | 0.7987 | nan | 0.7987 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7987 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0222 | 20.0 | 800 | 0.1932 | 0.3916 | 0.7831 | 0.7831 | nan | 0.7831 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7831 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0168 | 20.5 | 820 | 0.2070 | 0.3984 | 0.7967 | 0.7967 | nan | 0.7967 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7967 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.017 | 21.0 | 840 | 0.1964 | 0.4081 | 0.8161 | 0.8161 | nan | 0.8161 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8161 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0161 | 21.5 | 860 | 0.1972 | 0.3954 | 0.7908 | 0.7908 | nan | 0.7908 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7908 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0104 | 22.0 | 880 | 0.2017 | 0.3901 | 0.7803 | 0.7803 | nan | 0.7803 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7803 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0455 | 22.5 | 900 | 0.1981 | 0.4025 | 0.8050 | 0.8050 | nan | 0.8050 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8050 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.015 | 23.0 | 920 | 0.2073 | 0.4017 | 0.8035 | 0.8035 | nan | 0.8035 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8035 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.02 | 23.5 | 940 | 0.2105 | 0.3891 | 0.7781 | 0.7781 | nan | 0.7781 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7781 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0223 | 24.0 | 960 | 0.2260 | 0.3833 | 0.7666 | 0.7666 | nan | 0.7666 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7666 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0308 | 24.5 | 980 | 0.2174 | 0.3918 | 0.7837 | 0.7837 | nan | 0.7837 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7837 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0168 | 25.0 | 1000 | 0.1956 | 0.4058 | 0.8116 | 0.8116 | nan | 0.8116 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8116 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.01 | 25.5 | 1020 | 0.2042 | 0.4122 | 0.8243 | 0.8243 | nan | 0.8243 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8243 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0215 | 26.0 | 1040 | 0.2018 | 0.4107 | 0.8214 | 0.8214 | nan | 0.8214 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8214 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0138 | 26.5 | 1060 | 0.2072 | 0.3979 | 0.7958 | 0.7958 | nan | 0.7958 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7958 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0123 | 27.0 | 1080 | 0.2046 | 0.4072 | 0.8145 | 0.8145 | nan | 0.8145 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8145 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0172 | 27.5 | 1100 | 0.2095 | 0.3896 | 0.7792 | 0.7792 | nan | 0.7792 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7792 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0201 | 28.0 | 1120 | 0.1979 | 0.3994 | 0.7988 | 0.7988 | nan | 0.7988 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7988 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.013 | 28.5 | 1140 | 0.1970 | 0.3994 | 0.7988 | 0.7988 | nan | 0.7988 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7988 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0389 | 29.0 | 1160 | 0.2140 | 0.4001 | 0.8002 | 0.8002 | nan | 0.8002 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8002 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0125 | 29.5 | 1180 | 0.2060 | 0.4009 | 0.8019 | 0.8019 | nan | 0.8019 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8019 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0337 | 30.0 | 1200 | 0.2070 | 0.3817 | 0.7634 | 0.7634 | nan | 0.7634 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7634 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0189 | 30.5 | 1220 | 0.2292 | 0.3755 | 0.7510 | 0.7510 | nan | 0.7510 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7510 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.018 | 31.0 | 1240 | 0.2162 | 0.3843 | 0.7685 | 0.7685 | nan | 0.7685 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7685 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0164 | 31.5 | 1260 | 0.2154 | 0.3978 | 0.7956 | 0.7956 | nan | 0.7956 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7956 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0112 | 32.0 | 1280 | 0.2161 | 0.4061 | 0.8123 | 0.8123 | nan | 0.8123 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8123 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0484 | 32.5 | 1300 | 0.2174 | 0.4127 | 0.8253 | 0.8253 | nan | 0.8253 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8253 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0176 | 33.0 | 1320 | 0.2085 | 0.4054 | 0.8108 | 0.8108 | nan | 0.8108 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8108 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0078 | 33.5 | 1340 | 0.2135 | 0.4011 | 0.8023 | 0.8023 | nan | 0.8023 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8023 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0196 | 34.0 | 1360 | 0.2199 | 0.4015 | 0.8030 | 0.8030 | nan | 0.8030 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8030 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.01 | 34.5 | 1380 | 0.2166 | 0.4058 | 0.8117 | 0.8117 | nan | 0.8117 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8117 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.017 | 35.0 | 1400 | 0.2173 | 0.3916 | 0.7831 | 0.7831 | nan | 0.7831 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7831 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0219 | 35.5 | 1420 | 0.2232 | 0.3929 | 0.7857 | 0.7857 | nan | 0.7857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.049 | 36.0 | 1440 | 0.2130 | 0.3938 | 0.7875 | 0.7875 | nan | 0.7875 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7875 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0248 | 36.5 | 1460 | 0.2146 | 0.3966 | 0.7932 | 0.7932 | nan | 0.7932 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7932 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0155 | 37.0 | 1480 | 0.2431 | 0.4145 | 0.8289 | 0.8289 | nan | 0.8289 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8289 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0332 | 37.5 | 1500 | 0.2177 | 0.3977 | 0.7953 | 0.7953 | nan | 0.7953 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7953 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0084 | 38.0 | 1520 | 0.2185 | 0.3835 | 0.7669 | 0.7669 | nan | 0.7669 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7669 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0346 | 38.5 | 1540 | 0.2386 | 0.4044 | 0.8087 | 0.8087 | nan | 0.8087 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8087 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0282 | 39.0 | 1560 | 0.2172 | 0.3995 | 0.7990 | 0.7990 | nan | 0.7990 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7990 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0194 | 39.5 | 1580 | 0.2301 | 0.3920 | 0.7839 | 0.7839 | nan | 0.7839 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7839 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0119 | 40.0 | 1600 | 0.2342 | 0.3952 | 0.7904 | 0.7904 | nan | 0.7904 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7904 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0069 | 40.5 | 1620 | 0.2206 | 0.3979 | 0.7957 | 0.7957 | nan | 0.7957 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7957 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0246 | 41.0 | 1640 | 0.2224 | 0.3908 | 0.7816 | 0.7816 | nan | 0.7816 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7816 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0391 | 41.5 | 1660 | 0.2208 | 0.3947 | 0.7894 | 0.7894 | nan | 0.7894 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7894 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0161 | 42.0 | 1680 | 0.2188 | 0.3882 | 0.7765 | 0.7765 | nan | 0.7765 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7765 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0084 | 42.5 | 1700 | 0.2125 | 0.4009 | 0.8018 | 0.8018 | nan | 0.8018 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8018 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0192 | 43.0 | 1720 | 0.2341 | 0.3912 | 0.7823 | 0.7823 | nan | 0.7823 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7823 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0127 | 43.5 | 1740 | 0.2203 | 0.4059 | 0.8119 | 0.8119 | nan | 0.8119 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8119 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0157 | 44.0 | 1760 | 0.2114 | 0.4008 | 0.8017 | 0.8017 | nan | 0.8017 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8017 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0288 | 44.5 | 1780 | 0.2418 | 0.4073 | 0.8146 | 0.8146 | nan | 0.8146 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8146 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0144 | 45.0 | 1800 | 0.2436 | 0.4025 | 0.8050 | 0.8050 | nan | 0.8050 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8050 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0147 | 45.5 | 1820 | 0.2446 | 0.4095 | 0.8190 | 0.8190 | nan | 0.8190 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8190 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0137 | 46.0 | 1840 | 0.2430 | 0.3973 | 0.7947 | 0.7947 | nan | 0.7947 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7947 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0098 | 46.5 | 1860 | 0.2298 | 0.3904 | 0.7808 | 0.7808 | nan | 0.7808 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7808 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0164 | 47.0 | 1880 | 0.2238 | 0.3999 | 0.7998 | 0.7998 | nan | 0.7998 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7998 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0097 | 47.5 | 1900 | 0.2449 | 0.3988 | 0.7976 | 0.7976 | nan | 0.7976 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7976 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0069 | 48.0 | 1920 | 0.2391 | 0.3975 | 0.7949 | 0.7949 | nan | 0.7949 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7949 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0136 | 48.5 | 1940 | 0.2432 | 0.3917 | 0.7834 | 0.7834 | nan | 0.7834 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7834 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0167 | 49.0 | 1960 | 0.2383 | 0.4070 | 0.8141 | 0.8141 | nan | 0.8141 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8141 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0146 | 49.5 | 1980 | 0.2363 | 0.3941 | 0.7883 | 0.7883 | nan | 0.7883 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7883 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0147 | 50.0 | 2000 | 0.2288 | 0.3947 | 0.7893 | 0.7893 | nan | 0.7893 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7893 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0109 | 50.5 | 2020 | 0.2538 | 0.4066 | 0.8133 | 0.8133 | nan | 0.8133 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8133 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0132 | 51.0 | 2040 | 0.2370 | 0.4024 | 0.8049 | 0.8049 | nan | 0.8049 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8049 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.1149 | 51.5 | 2060 | 0.2504 | 0.3950 | 0.7901 | 0.7901 | nan | 0.7901 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7901 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0127 | 52.0 | 2080 | 0.2449 | 0.4047 | 0.8094 | 0.8094 | nan | 0.8094 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8094 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.009 | 52.5 | 2100 | 0.2518 | 0.3956 | 0.7912 | 0.7912 | nan | 0.7912 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7912 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0132 | 53.0 | 2120 | 0.2531 | 0.3984 | 0.7968 | 0.7968 | nan | 0.7968 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7968 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0293 | 53.5 | 2140 | 0.2691 | 0.3962 | 0.7924 | 0.7924 | nan | 0.7924 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7924 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0184 | 54.0 | 2160 | 0.2564 | 0.3975 | 0.7949 | 0.7949 | nan | 0.7949 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7949 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0342 | 54.5 | 2180 | 0.2490 | 0.3955 | 0.7910 | 0.7910 | nan | 0.7910 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7910 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0124 | 55.0 | 2200 | 0.2617 | 0.3922 | 0.7844 | 0.7844 | nan | 0.7844 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7844 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.012 | 55.5 | 2220 | 0.2415 | 0.4072 | 0.8144 | 0.8144 | nan | 0.8144 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8144 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0162 | 56.0 | 2240 | 0.2455 | 0.4062 | 0.8124 | 0.8124 | nan | 0.8124 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8124 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0134 | 56.5 | 2260 | 0.2488 | 0.3970 | 0.7940 | 0.7940 | nan | 0.7940 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7940 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0221 | 57.0 | 2280 | 0.2533 | 0.3900 | 0.7799 | 0.7799 | nan | 0.7799 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7799 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0064 | 57.5 | 2300 | 0.2505 | 0.3956 | 0.7913 | 0.7913 | nan | 0.7913 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7913 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0181 | 58.0 | 2320 | 0.2649 | 0.3968 | 0.7936 | 0.7936 | nan | 0.7936 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7936 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0358 | 58.5 | 2340 | 0.2569 | 0.3974 | 0.7948 | 0.7948 | nan | 0.7948 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7948 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0081 | 59.0 | 2360 | 0.2517 | 0.3956 | 0.7911 | 0.7911 | nan | 0.7911 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7911 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0174 | 59.5 | 2380 | 0.2654 | 0.3908 | 0.7816 | 0.7816 | nan | 0.7816 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7816 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0257 | 60.0 | 2400 | 0.2634 | 0.4032 | 0.8063 | 0.8063 | nan | 0.8063 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8063 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.018 | 60.5 | 2420 | 0.2744 | 0.3854 | 0.7708 | 0.7708 | nan | 0.7708 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7708 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0114 | 61.0 | 2440 | 0.2569 | 0.3943 | 0.7885 | 0.7885 | nan | 0.7885 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7885 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0348 | 61.5 | 2460 | 0.2688 | 0.3998 | 0.7997 | 0.7997 | nan | 0.7997 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7997 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.012 | 62.0 | 2480 | 0.2712 | 0.3909 | 0.7819 | 0.7819 | nan | 0.7819 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7819 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0078 | 62.5 | 2500 | 0.2749 | 0.3920 | 0.7841 | 0.7841 | nan | 0.7841 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7841 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0209 | 63.0 | 2520 | 0.2561 | 0.3911 | 0.7821 | 0.7821 | nan | 0.7821 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7821 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0105 | 63.5 | 2540 | 0.2623 | 0.3995 | 0.7990 | 0.7990 | nan | 0.7990 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7990 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0136 | 64.0 | 2560 | 0.2654 | 0.3937 | 0.7874 | 0.7874 | nan | 0.7874 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7874 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0073 | 64.5 | 2580 | 0.2670 | 0.3963 | 0.7926 | 0.7926 | nan | 0.7926 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7926 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0182 | 65.0 | 2600 | 0.2659 | 0.3935 | 0.7870 | 0.7870 | nan | 0.7870 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7870 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0154 | 65.5 | 2620 | 0.2665 | 0.3952 | 0.7903 | 0.7903 | nan | 0.7903 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7903 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0061 | 66.0 | 2640 | 0.2752 | 0.3963 | 0.7926 | 0.7926 | nan | 0.7926 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7926 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0115 | 66.5 | 2660 | 0.2866 | 0.3983 | 0.7967 | 0.7967 | nan | 0.7967 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7967 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0084 | 67.0 | 2680 | 0.2819 | 0.3944 | 0.7888 | 0.7888 | nan | 0.7888 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7888 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0115 | 67.5 | 2700 | 0.2871 | 0.3922 | 0.7844 | 0.7844 | nan | 0.7844 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7844 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0072 | 68.0 | 2720 | 0.2792 | 0.4044 | 0.8088 | 0.8088 | nan | 0.8088 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8088 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0102 | 68.5 | 2740 | 0.2836 | 0.3892 | 0.7783 | 0.7783 | nan | 0.7783 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7783 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0115 | 69.0 | 2760 | 0.2698 | 0.3944 | 0.7887 | 0.7887 | nan | 0.7887 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7887 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0126 | 69.5 | 2780 | 0.2790 | 0.3972 | 0.7944 | 0.7944 | nan | 0.7944 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7944 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0055 | 70.0 | 2800 | 0.2846 | 0.3963 | 0.7927 | 0.7927 | nan | 0.7927 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7927 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0205 | 70.5 | 2820 | 0.2766 | 0.3999 | 0.7997 | 0.7997 | nan | 0.7997 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7997 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0184 | 71.0 | 2840 | 0.2876 | 0.3924 | 0.7847 | 0.7847 | nan | 0.7847 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7847 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.015 | 71.5 | 2860 | 0.2900 | 0.3954 | 0.7908 | 0.7908 | nan | 0.7908 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7908 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0163 | 72.0 | 2880 | 0.2721 | 0.3997 | 0.7993 | 0.7993 | nan | 0.7993 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7993 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0121 | 72.5 | 2900 | 0.2840 | 0.4007 | 0.8013 | 0.8013 | nan | 0.8013 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8013 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0162 | 73.0 | 2920 | 0.2732 | 0.3960 | 0.7919 | 0.7919 | nan | 0.7919 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7919 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0102 | 73.5 | 2940 | 0.2870 | 0.4009 | 0.8017 | 0.8017 | nan | 0.8017 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8017 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0101 | 74.0 | 2960 | 0.2752 | 0.4035 | 0.8070 | 0.8070 | nan | 0.8070 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8070 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0113 | 74.5 | 2980 | 0.2781 | 0.4010 | 0.8020 | 0.8020 | nan | 0.8020 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8020 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0351 | 75.0 | 3000 | 0.2847 | 0.3995 | 0.7991 | 0.7991 | nan | 0.7991 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7991 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0096 | 75.5 | 3020 | 0.2767 | 0.3947 | 0.7894 | 0.7894 | nan | 0.7894 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7894 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0135 | 76.0 | 3040 | 0.2712 | 0.3979 | 0.7958 | 0.7958 | nan | 0.7958 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7958 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0062 | 76.5 | 3060 | 0.2697 | 0.3890 | 0.7780 | 0.7780 | nan | 0.7780 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7780 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.012 | 77.0 | 3080 | 0.2888 | 0.4004 | 0.8008 | 0.8008 | nan | 0.8008 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8008 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0072 | 77.5 | 3100 | 0.2763 | 0.3999 | 0.7998 | 0.7998 | nan | 0.7998 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7998 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0085 | 78.0 | 3120 | 0.2748 | 0.4016 | 0.8033 | 0.8033 | nan | 0.8033 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8033 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0065 | 78.5 | 3140 | 0.2864 | 0.4006 | 0.8012 | 0.8012 | nan | 0.8012 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8012 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0395 | 79.0 | 3160 | 0.2758 | 0.4026 | 0.8053 | 0.8053 | nan | 0.8053 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.8053 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0172 | 79.5 | 3180 | 0.2864 | 0.3907 | 0.7814 | 0.7814 | nan | 0.7814 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7814 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0067 | 80.0 | 3200 | 0.2824 | 0.3961 | 0.7922 | 0.7922 | nan | 0.7922 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7922 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0106 | 80.5 | 3220 | 0.2919 | 0.3962 | 0.7924 | 0.7924 | nan | 0.7924 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7924 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0076 | 81.0 | 3240 | 0.2936 | 0.3929 | 0.7857 | 0.7857 | nan | 0.7857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0128 | 81.5 | 3260 | 0.2857 | 0.3964 | 0.7928 | 0.7928 | nan | 0.7928 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7928 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.006 | 82.0 | 3280 | 0.2797 | 0.3987 | 0.7975 | 0.7975 | nan | 0.7975 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7975 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0121 | 82.5 | 3300 | 0.2934 | 0.3891 | 0.7783 | 0.7783 | nan | 0.7783 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7783 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0057 | 83.0 | 3320 | 0.2842 | 0.3957 | 0.7915 | 0.7915 | nan | 0.7915 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7915 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0081 | 83.5 | 3340 | 0.2787 | 0.3895 | 0.7790 | 0.7790 | nan | 0.7790 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7790 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0135 | 84.0 | 3360 | 0.2785 | 0.3943 | 0.7886 | 0.7886 | nan | 0.7886 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7886 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0042 | 84.5 | 3380 | 0.2952 | 0.3885 | 0.7770 | 0.7770 | nan | 0.7770 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7770 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0199 | 85.0 | 3400 | 0.2861 | 0.3936 | 0.7873 | 0.7873 | nan | 0.7873 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7873 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0104 | 85.5 | 3420 | 0.2919 | 0.3968 | 0.7935 | 0.7935 | nan | 0.7935 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7935 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0052 | 86.0 | 3440 | 0.2862 | 0.3943 | 0.7886 | 0.7886 | nan | 0.7886 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7886 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0108 | 86.5 | 3460 | 0.2845 | 0.3977 | 0.7953 | 0.7953 | nan | 0.7953 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7953 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0122 | 87.0 | 3480 | 0.2958 | 0.3969 | 0.7939 | 0.7939 | nan | 0.7939 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7939 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.009 | 87.5 | 3500 | 0.2986 | 0.3981 | 0.7961 | 0.7961 | nan | 0.7961 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7961 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0162 | 88.0 | 3520 | 0.2882 | 0.3971 | 0.7941 | 0.7941 | nan | 0.7941 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7941 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0082 | 88.5 | 3540 | 0.2871 | 0.3967 | 0.7933 | 0.7933 | nan | 0.7933 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7933 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0074 | 89.0 | 3560 | 0.2944 | 0.3968 | 0.7936 | 0.7936 | nan | 0.7936 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7936 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0097 | 89.5 | 3580 | 0.2848 | 0.3975 | 0.7949 | 0.7949 | nan | 0.7949 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7949 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0071 | 90.0 | 3600 | 0.2887 | 0.3987 | 0.7974 | 0.7974 | nan | 0.7974 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7974 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0072 | 90.5 | 3620 | 0.2931 | 0.3970 | 0.7939 | 0.7939 | nan | 0.7939 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7939 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0074 | 91.0 | 3640 | 0.2934 | 0.3967 | 0.7935 | 0.7935 | nan | 0.7935 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7935 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0067 | 91.5 | 3660 | 0.2917 | 0.3975 | 0.7950 | 0.7950 | nan | 0.7950 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7950 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0054 | 92.0 | 3680 | 0.2906 | 0.3969 | 0.7939 | 0.7939 | nan | 0.7939 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7939 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0118 | 92.5 | 3700 | 0.2905 | 0.3953 | 0.7905 | 0.7905 | nan | 0.7905 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7905 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0098 | 93.0 | 3720 | 0.2896 | 0.3992 | 0.7985 | 0.7985 | nan | 0.7985 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7985 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0117 | 93.5 | 3740 | 0.2998 | 0.3958 | 0.7916 | 0.7916 | nan | 0.7916 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7916 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0107 | 94.0 | 3760 | 0.2917 | 0.3961 | 0.7922 | 0.7922 | nan | 0.7922 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7922 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0106 | 94.5 | 3780 | 0.2961 | 0.3948 | 0.7896 | 0.7896 | nan | 0.7896 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7896 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0129 | 95.0 | 3800 | 0.2966 | 0.3929 | 0.7859 | 0.7859 | nan | 0.7859 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7859 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0093 | 95.5 | 3820 | 0.2981 | 0.3966 | 0.7933 | 0.7933 | nan | 0.7933 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7933 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.006 | 96.0 | 3840 | 0.2969 | 0.3953 | 0.7906 | 0.7906 | nan | 0.7906 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7906 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0077 | 96.5 | 3860 | 0.2861 | 0.3968 | 0.7937 | 0.7937 | nan | 0.7937 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7937 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0082 | 97.0 | 3880 | 0.3000 | 0.3972 | 0.7945 | 0.7945 | nan | 0.7945 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7945 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.006 | 97.5 | 3900 | 0.2954 | 0.3961 | 0.7921 | 0.7921 | nan | 0.7921 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7921 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0067 | 98.0 | 3920 | 0.2927 | 0.3948 | 0.7897 | 0.7897 | nan | 0.7897 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7897 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0103 | 98.5 | 3940 | 0.2942 | 0.3946 | 0.7893 | 0.7893 | nan | 0.7893 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7893 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0069 | 99.0 | 3960 | 0.2885 | 0.3961 | 0.7922 | 0.7922 | nan | 0.7922 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7922 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0092 | 99.5 | 3980 | 0.2866 | 0.3963 | 0.7926 | 0.7926 | nan | 0.7926 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7926 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 0.0074 | 100.0 | 4000 | 0.2957 | 0.3964 | 0.7929 | 0.7929 | nan | 0.7929 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.7929 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
Fhermin/ppo-SnowballTarget2
|
Fhermin
| 2024-02-27T04:18:38Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-02-27T04:18:33Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Fhermin/ppo-SnowballTarget2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
alonzogarbanzo/Bloom-1b7-dialogsum
|
alonzogarbanzo
| 2024-02-27T04:16:58Z | 154 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bloom",
"text-generation",
"generated_from_trainer",
"base_model:bigscience/bloom-1b7",
"base_model:finetune:bigscience/bloom-1b7",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T03:03:37Z |
---
license: bigscience-bloom-rail-1.0
base_model: bigscience/bloom-1b7
tags:
- generated_from_trainer
model-index:
- name: Bloom-1b7-dialogsum
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. -->
# Bloom-1b7-dialogsum
This model is a fine-tuned version of [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- 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: 5
- mixed_precision_training: Native AMP
### Training results
Final epoch results: {'loss': 0.024, 'learning_rate': 1.4000000000000001e-06, 'epoch': 5.0}
After finished: {'train_runtime': 582.2106, 'train_samples_per_second': 1.718, 'train_steps_per_second': 0.429, 'train_loss': 0.72078223118186, 'epoch': 5.0}
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
GraphWiz/Mistral-7B-RFT
|
GraphWiz
| 2024-02-27T04:13:53Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"graph problem",
"dataset:GraphWiz/GraphInstruct-RFT-72K",
"arxiv:2402.16029",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-18T11:59:18Z |
---
license: apache-2.0
datasets:
- GraphWiz/GraphInstruct-RFT-72K
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- graph problem
---
# GraphWiz
Project Page: [https://graph-wiz.github.io/](https://graph-wiz.github.io/)
Paper: [https://arxiv.org/abs/2402.16029.pdf](https://arxiv.org/abs/2402.16029)
Code: [https://github.com/nuochenpku/Graph-Reasoning-LLM](https://github.com/nuochenpku/Graph-Reasoning-LLM)
GraphWiz is a powerful instruction-following LLM that can map textural descriptions of graphs and structures, and then solve different graph problems explicitly in natural language.
Training strategies include two stages: **Mixed-task Training** and **DPO Alignment**.
## Results
| *Models* | **Cycle** | **Connect** | **Bipartite** | **Topology** | **Shortest** | **Triangle** | **Flow** | **Hamilton** | **Subgraph** | **Average** |
|:-------------------------------------:|:-------------------------------:|:--------------------------------:|:----------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------:|:---------------------------------:|:---------------------------------:|:--------------------------------------:|
| *In-Context Learning* |||||||||||
| **GPT-4 (zero-shot)** | 38.75 | 17.00 | 65.25 | 5.00 | 9.25 | 5.75 | 3.25 | 59.25 | 45.50 | 27.67 |
| **GhatGPT (2-shot)** | 51.25 | 43.75 | 70.75 | 4.50 | 3.50 | 17.25 | 8.50 | 54.25 | 43.00 | 32.97 |
| **GPT-4 (2-shot)** | 52.50 | 62.75 | 74.25 | 25.25 | 18.25 | 31.00 | 7.75 | {75.75} | 46.75 | 43.81 |
| *Mistral-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 78.50 | 1.00 | 23.00 | 47.00 | 28.75 | 31.75 | 41.25 | 46.56 |
| **GraphWiz** | **92.00** | **89.50** | 72.00 | 19.00 | **31.25** | 38.75 | 29.25 | 26.50 | **85.50** | 53.75 |
| **GraphWiz-DPO** | 85.50 | 79.50 | **85.50** | **85.25** | 12.50 | 29.00 | 35.50 | 62.75 | 48.50 | 58.22 |
| *LLaMA 2-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 41.25 | 4.00 | 9.50 | 30.00 | 16.50 | 69.00 | 75.45 | 44.81 |
| **GraphWiz** | 91.50 | 87.00 | 74.00 | 18.00 | **28.00** | 38.25 | 24.50 | 52.25 | **82.25** | 55.08 |
| **GraphWiz-DPO** | 89.00 | 82.50 | 84.75 | 46.75 | 24.00 | **52.75** | **43.50** | **81.50** | 77.25 | **65.00** |
| *LLaMA 2-13B* |||||||||||
| **Naive SFT** | 73.75 | 83.75 | 59.00 | 0.50 | 11.75 | 34.75 | 24.25 | 59.75 | 54.75 | 44.69 |
| **GraphWiz** | **94.75** | 87.00 | 78.00 | 28.00 | 27.75 | 36.00 | 24.50 | 59.00 | 81.50 | 57.39 |
| **GraphWiz-DPO** | 87.50 | **88.50** | **88.25** | **72.75** | 22.00 | **48.75** | **43.75** | 46.50 | 77.00 | **63.89** |
## Examples
```
G-Q: Determine whether or not there is a cycle in an undirected graph. In an undirected graph..,the
nodes are numbered from 0 to 88, and the edges are: (0, 73) (0, 51) (0, 10) (0, 63) (0, 28) (1, 62) (1, 57) (1, 84) (1, 61) (1, 5)
(1, 24) (2, 84) (2, 3) (2, 66) (2, 68) (2, 17) (2, 35) (2, 34) (2, 15) (3, 39) (3, 52) (3, 16) (3, 15) (3, 8) (4, 69) (4, 85)
(4, 36) (4, 72) (5, 44) (6, 77) (6, 7) (7, 85) (8, 64) (8, 23) (8, 28) (9, 34) (9, 31) (9, 61) (9, 28) (10, 26) (11, 37) (11, 39)
(11, 19) (11, 64) (13, 73) (13, 61) (13, 80) (13, 85) (14, 86) (14, 59) (14, 32) (14, 58) (14, 85) (14, 66) (15, 43) (15, 48) (15, 73)
(15, 19) (15, 47) (15, 68) (16, 46) (16, 60) (16, 84) (17, 44) (17, 72) (17, 36) (17, 37) (17, 61) (18, 20) (18, 24) (18, 22) (18, 41)
(19, 45) (19, 83) (20, 25) (20, 29) (21, 38) (21, 64) (21, 24) (21, 22) (21, 34) (22, 23) (23, 34) (23, 30) (23, 83) (24, 47) (24, 50)
(25, 59) (25, 42) (25, 70) (25, 72) (26, 45) (26, 30) (26, 87) (26, 80) (26, 50) (27, 77) (27, 58) (27, 60) (27, 29) (28, 36) (28, 59)
(28, 88) (29, 31) (29, 33) (29, 56) (29, 55) (29, 78) (29, 73) (29, 82) (30, 34) (31, 58) (31, 70) (32, 78) (33, 73) (33, 68) (34, 48)
(34, 88) (34, 51) (34, 66) (34, 54) (35, 43) (35, 56) (36, 44) (36, 39) (37, 57) (37, 71) (38, 47) (38, 58) (38, 88) (39, 74) (39, 48)
(39, 45) (39, 42) (40, 60) (40, 59) (40, 71) (40, 76) (41, 79) (42, 60) (42, 62) (42, 72) (43, 57) (44, 47) (44, 74) (44, 75) (44, 82)
(44, 70) (45, 68) (45, 67) (45, 71) (46, 84) (47, 56) (48, 75) (49, 71) (49, 58) (50, 76) (51, 55) (51, 87) (51, 79) (52, 78) (56, 69)
(56, 83) (56, 79) (57, 76) (57, 70) (57, 72) (57, 78) (59, 70) (60, 80) (60, 76) (60, 77) (60, 75) (61, 74) (61, 77) (62, 84) (62, 66)
(66, 69) (68, 88) (70, 88) (70, 76) (71, 81) (71, 72) (71, 86) (72, 75) (73, 82) (73, 83) (74, 85). Is there a cycle in this graph?
GraphWiz:
There are many possible ways to find a cycle in this graph, but one example is: Starting from node 0, we can go to node 73 (via edge 0-73), then to node 82 (via edge 73-82),
then to node 44 (via edge 82-44), then to node 17 (via edge 44-17), then to node 36 (via edge 17-36), then to node 28 (via edge 36-28),
and back to node 0 (via edge 28-0). This forms a cycle [0-73-82-44-17-36-28-0] without revisiting any edge. So, there is a cycle in this graph.
```
|
GraphWiz/LLaMA2-13B-DPO
|
GraphWiz
| 2024-02-27T04:13:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"graph problem",
"dataset:GraphWiz/GraphInstruct-RFT-72K",
"arxiv:2402.16029",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-23T08:28:07Z |
---
license: apache-2.0
datasets:
- GraphWiz/GraphInstruct-RFT-72K
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- graph problem
---
# GraphWiz
Project Page: [https://graph-wiz.github.io/](https://graph-wiz.github.io/)
Paper: [https://arxiv.org/abs/2402.16029.pdf](https://arxiv.org/abs/2402.16029)
Code: [https://github.com/nuochenpku/Graph-Reasoning-LLM](https://github.com/nuochenpku/Graph-Reasoning-LLM)
GraphWiz is a powerful instruction-following LLM that can map textural descriptions of graphs and structures, and then solve different graph problems explicitly in natural language.
Training strategies include two stages: **Mixed-task Training** and **DPO Alignment**.
## Results
| *Models* | **Cycle** | **Connect** | **Bipartite** | **Topology** | **Shortest** | **Triangle** | **Flow** | **Hamilton** | **Subgraph** | **Average** |
|:-------------------------------------:|:-------------------------------:|:--------------------------------:|:----------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------:|:---------------------------------:|:---------------------------------:|:--------------------------------------:|
| *In-Context Learning* |||||||||||
| **GPT-4 (zero-shot)** | 38.75 | 17.00 | 65.25 | 5.00 | 9.25 | 5.75 | 3.25 | 59.25 | 45.50 | 27.67 |
| **GhatGPT (2-shot)** | 51.25 | 43.75 | 70.75 | 4.50 | 3.50 | 17.25 | 8.50 | 54.25 | 43.00 | 32.97 |
| **GPT-4 (2-shot)** | 52.50 | 62.75 | 74.25 | 25.25 | 18.25 | 31.00 | 7.75 | {75.75} | 46.75 | 43.81 |
| *Mistral-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 78.50 | 1.00 | 23.00 | 47.00 | 28.75 | 31.75 | 41.25 | 46.56 |
| **GraphWiz** | **92.00** | **89.50** | 72.00 | 19.00 | **31.25** | 38.75 | 29.25 | 26.50 | **85.50** | 53.75 |
| **GraphWiz-DPO** | 85.50 | 79.50 | **85.50** | **85.25** | 12.50 | 29.00 | 35.50 | 62.75 | 48.50 | 58.22 |
| *LLaMA 2-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 41.25 | 4.00 | 9.50 | 30.00 | 16.50 | 69.00 | 75.45 | 44.81 |
| **GraphWiz** | 91.50 | 87.00 | 74.00 | 18.00 | **28.00** | 38.25 | 24.50 | 52.25 | **82.25** | 55.08 |
| **GraphWiz-DPO** | 89.00 | 82.50 | 84.75 | 46.75 | 24.00 | **52.75** | **43.50** | **81.50** | 77.25 | **65.00** |
| *LLaMA 2-13B* |||||||||||
| **Naive SFT** | 73.75 | 83.75 | 59.00 | 0.50 | 11.75 | 34.75 | 24.25 | 59.75 | 54.75 | 44.69 |
| **GraphWiz** | **94.75** | 87.00 | 78.00 | 28.00 | 27.75 | 36.00 | 24.50 | 59.00 | 81.50 | 57.39 |
| **GraphWiz-DPO** | 87.50 | **88.50** | **88.25** | **72.75** | 22.00 | **48.75** | **43.75** | 46.50 | 77.00 | **63.89** |
## Examples
```
G-Q: Determine whether or not there is a cycle in an undirected graph. In an undirected graph..,the
nodes are numbered from 0 to 88, and the edges are: (0, 73) (0, 51) (0, 10) (0, 63) (0, 28) (1, 62) (1, 57) (1, 84) (1, 61) (1, 5)
(1, 24) (2, 84) (2, 3) (2, 66) (2, 68) (2, 17) (2, 35) (2, 34) (2, 15) (3, 39) (3, 52) (3, 16) (3, 15) (3, 8) (4, 69) (4, 85)
(4, 36) (4, 72) (5, 44) (6, 77) (6, 7) (7, 85) (8, 64) (8, 23) (8, 28) (9, 34) (9, 31) (9, 61) (9, 28) (10, 26) (11, 37) (11, 39)
(11, 19) (11, 64) (13, 73) (13, 61) (13, 80) (13, 85) (14, 86) (14, 59) (14, 32) (14, 58) (14, 85) (14, 66) (15, 43) (15, 48) (15, 73)
(15, 19) (15, 47) (15, 68) (16, 46) (16, 60) (16, 84) (17, 44) (17, 72) (17, 36) (17, 37) (17, 61) (18, 20) (18, 24) (18, 22) (18, 41)
(19, 45) (19, 83) (20, 25) (20, 29) (21, 38) (21, 64) (21, 24) (21, 22) (21, 34) (22, 23) (23, 34) (23, 30) (23, 83) (24, 47) (24, 50)
(25, 59) (25, 42) (25, 70) (25, 72) (26, 45) (26, 30) (26, 87) (26, 80) (26, 50) (27, 77) (27, 58) (27, 60) (27, 29) (28, 36) (28, 59)
(28, 88) (29, 31) (29, 33) (29, 56) (29, 55) (29, 78) (29, 73) (29, 82) (30, 34) (31, 58) (31, 70) (32, 78) (33, 73) (33, 68) (34, 48)
(34, 88) (34, 51) (34, 66) (34, 54) (35, 43) (35, 56) (36, 44) (36, 39) (37, 57) (37, 71) (38, 47) (38, 58) (38, 88) (39, 74) (39, 48)
(39, 45) (39, 42) (40, 60) (40, 59) (40, 71) (40, 76) (41, 79) (42, 60) (42, 62) (42, 72) (43, 57) (44, 47) (44, 74) (44, 75) (44, 82)
(44, 70) (45, 68) (45, 67) (45, 71) (46, 84) (47, 56) (48, 75) (49, 71) (49, 58) (50, 76) (51, 55) (51, 87) (51, 79) (52, 78) (56, 69)
(56, 83) (56, 79) (57, 76) (57, 70) (57, 72) (57, 78) (59, 70) (60, 80) (60, 76) (60, 77) (60, 75) (61, 74) (61, 77) (62, 84) (62, 66)
(66, 69) (68, 88) (70, 88) (70, 76) (71, 81) (71, 72) (71, 86) (72, 75) (73, 82) (73, 83) (74, 85). Is there a cycle in this graph?
GraphWiz:
There are many possible ways to find a cycle in this graph, but one example is: Starting from node 0, we can go to node 73 (via edge 0-73), then to node 82 (via edge 73-82),
then to node 44 (via edge 82-44), then to node 17 (via edge 44-17), then to node 36 (via edge 17-36), then to node 28 (via edge 36-28),
and back to node 0 (via edge 28-0). This forms a cycle [0-73-82-44-17-36-28-0] without revisiting any edge. So, there is a cycle in this graph.
```
|
GraphWiz/LLaMA2-7B
|
GraphWiz
| 2024-02-27T04:13:14Z | 49 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"graph problem",
"dataset:GraphWiz/GraphInstruct-RFT-72K",
"arxiv:2402.16029",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-14T04:34:20Z |
---
license: apache-2.0
datasets:
- GraphWiz/GraphInstruct-RFT-72K
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- graph problem
---
# GraphWiz
Project Page: [https://graph-wiz.github.io/](https://graph-wiz.github.io/)
Paper: [https://arxiv.org/abs/2402.16029.pdf](https://arxiv.org/abs/2402.16029)
Code: [https://github.com/nuochenpku/Graph-Reasoning-LLM](https://github.com/nuochenpku/Graph-Reasoning-LLM)
GraphWiz is a powerful instruction-following LLM that can map textural descriptions of graphs and structures, and then solve different graph problems explicitly in natural language.
Training strategies include two stages: **Mixed-task Training** and **DPO Alignment**.
## Results
| *Models* | **Cycle** | **Connect** | **Bipartite** | **Topology** | **Shortest** | **Triangle** | **Flow** | **Hamilton** | **Subgraph** | **Average** |
|:-------------------------------------:|:-------------------------------:|:--------------------------------:|:----------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------:|:---------------------------------:|:---------------------------------:|:--------------------------------------:|
| *In-Context Learning* |||||||||||
| **GPT-4 (zero-shot)** | 38.75 | 17.00 | 65.25 | 5.00 | 9.25 | 5.75 | 3.25 | 59.25 | 45.50 | 27.67 |
| **GhatGPT (2-shot)** | 51.25 | 43.75 | 70.75 | 4.50 | 3.50 | 17.25 | 8.50 | 54.25 | 43.00 | 32.97 |
| **GPT-4 (2-shot)** | 52.50 | 62.75 | 74.25 | 25.25 | 18.25 | 31.00 | 7.75 | {75.75} | 46.75 | 43.81 |
| *Mistral-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 78.50 | 1.00 | 23.00 | 47.00 | 28.75 | 31.75 | 41.25 | 46.56 |
| **GraphWiz** | **92.00** | **89.50** | 72.00 | 19.00 | **31.25** | 38.75 | 29.25 | 26.50 | **85.50** | 53.75 |
| **GraphWiz-DPO** | 85.50 | 79.50 | **85.50** | **85.25** | 12.50 | 29.00 | 35.50 | 62.75 | 48.50 | 58.22 |
| *LLaMA 2-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 41.25 | 4.00 | 9.50 | 30.00 | 16.50 | 69.00 | 75.45 | 44.81 |
| **GraphWiz** | 91.50 | 87.00 | 74.00 | 18.00 | **28.00** | 38.25 | 24.50 | 52.25 | **82.25** | 55.08 |
| **GraphWiz-DPO** | 89.00 | 82.50 | 84.75 | 46.75 | 24.00 | **52.75** | **43.50** | **81.50** | 77.25 | **65.00** |
| *LLaMA 2-13B* |||||||||||
| **Naive SFT** | 73.75 | 83.75 | 59.00 | 0.50 | 11.75 | 34.75 | 24.25 | 59.75 | 54.75 | 44.69 |
| **GraphWiz** | **94.75** | 87.00 | 78.00 | 28.00 | 27.75 | 36.00 | 24.50 | 59.00 | 81.50 | 57.39 |
| **GraphWiz-DPO** | 87.50 | **88.50** | **88.25** | **72.75** | 22.00 | **48.75** | **43.75** | 46.50 | 77.00 | **63.89** |
## Examples
```
G-Q: Determine whether or not there is a cycle in an undirected graph. In an undirected graph..,the
nodes are numbered from 0 to 88, and the edges are: (0, 73) (0, 51) (0, 10) (0, 63) (0, 28) (1, 62) (1, 57) (1, 84) (1, 61) (1, 5)
(1, 24) (2, 84) (2, 3) (2, 66) (2, 68) (2, 17) (2, 35) (2, 34) (2, 15) (3, 39) (3, 52) (3, 16) (3, 15) (3, 8) (4, 69) (4, 85)
(4, 36) (4, 72) (5, 44) (6, 77) (6, 7) (7, 85) (8, 64) (8, 23) (8, 28) (9, 34) (9, 31) (9, 61) (9, 28) (10, 26) (11, 37) (11, 39)
(11, 19) (11, 64) (13, 73) (13, 61) (13, 80) (13, 85) (14, 86) (14, 59) (14, 32) (14, 58) (14, 85) (14, 66) (15, 43) (15, 48) (15, 73)
(15, 19) (15, 47) (15, 68) (16, 46) (16, 60) (16, 84) (17, 44) (17, 72) (17, 36) (17, 37) (17, 61) (18, 20) (18, 24) (18, 22) (18, 41)
(19, 45) (19, 83) (20, 25) (20, 29) (21, 38) (21, 64) (21, 24) (21, 22) (21, 34) (22, 23) (23, 34) (23, 30) (23, 83) (24, 47) (24, 50)
(25, 59) (25, 42) (25, 70) (25, 72) (26, 45) (26, 30) (26, 87) (26, 80) (26, 50) (27, 77) (27, 58) (27, 60) (27, 29) (28, 36) (28, 59)
(28, 88) (29, 31) (29, 33) (29, 56) (29, 55) (29, 78) (29, 73) (29, 82) (30, 34) (31, 58) (31, 70) (32, 78) (33, 73) (33, 68) (34, 48)
(34, 88) (34, 51) (34, 66) (34, 54) (35, 43) (35, 56) (36, 44) (36, 39) (37, 57) (37, 71) (38, 47) (38, 58) (38, 88) (39, 74) (39, 48)
(39, 45) (39, 42) (40, 60) (40, 59) (40, 71) (40, 76) (41, 79) (42, 60) (42, 62) (42, 72) (43, 57) (44, 47) (44, 74) (44, 75) (44, 82)
(44, 70) (45, 68) (45, 67) (45, 71) (46, 84) (47, 56) (48, 75) (49, 71) (49, 58) (50, 76) (51, 55) (51, 87) (51, 79) (52, 78) (56, 69)
(56, 83) (56, 79) (57, 76) (57, 70) (57, 72) (57, 78) (59, 70) (60, 80) (60, 76) (60, 77) (60, 75) (61, 74) (61, 77) (62, 84) (62, 66)
(66, 69) (68, 88) (70, 88) (70, 76) (71, 81) (71, 72) (71, 86) (72, 75) (73, 82) (73, 83) (74, 85). Is there a cycle in this graph?
GraphWiz:
There are many possible ways to find a cycle in this graph, but one example is: Starting from node 0, we can go to node 73 (via edge 0-73), then to node 82 (via edge 73-82),
then to node 44 (via edge 82-44), then to node 17 (via edge 44-17), then to node 36 (via edge 17-36), then to node 28 (via edge 36-28),
and back to node 0 (via edge 28-0). This forms a cycle [0-73-82-44-17-36-28-0] without revisiting any edge. So, there is a cycle in this graph.
```
|
GraphWiz/LLaMA2-7B-DPO
|
GraphWiz
| 2024-02-27T04:12:58Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"graph problem",
"dataset:GraphWiz/GraphInstruct-RFT-72K",
"arxiv:2402.16029",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-23T08:27:06Z |
---
license: apache-2.0
datasets:
- GraphWiz/GraphInstruct-RFT-72K
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- graph problem
---
# GraphWiz
Project Page: [https://graph-wiz.github.io/](https://graph-wiz.github.io/)
Paper: [https://arxiv.org/abs/2402.16029.pdf](https://arxiv.org/abs/2402.16029)
Code: [https://github.com/nuochenpku/Graph-Reasoning-LLM](https://github.com/nuochenpku/Graph-Reasoning-LLM)
GraphWiz is a powerful instruction-following LLM that can map textural descriptions of graphs and structures, and then solve different graph problems explicitly in natural language.
Training strategies include two stages: **Mixed-task Training** and **DPO Alignment**.
## Results
| *Models* | **Cycle** | **Connect** | **Bipartite** | **Topology** | **Shortest** | **Triangle** | **Flow** | **Hamilton** | **Subgraph** | **Average** |
|:-------------------------------------:|:-------------------------------:|:--------------------------------:|:----------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------:|:---------------------------------:|:---------------------------------:|:--------------------------------------:|
| *In-Context Learning* |||||||||||
| **GPT-4 (zero-shot)** | 38.75 | 17.00 | 65.25 | 5.00 | 9.25 | 5.75 | 3.25 | 59.25 | 45.50 | 27.67 |
| **GhatGPT (2-shot)** | 51.25 | 43.75 | 70.75 | 4.50 | 3.50 | 17.25 | 8.50 | 54.25 | 43.00 | 32.97 |
| **GPT-4 (2-shot)** | 52.50 | 62.75 | 74.25 | 25.25 | 18.25 | 31.00 | 7.75 | {75.75} | 46.75 | 43.81 |
| *Mistral-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 78.50 | 1.00 | 23.00 | 47.00 | 28.75 | 31.75 | 41.25 | 46.56 |
| **GraphWiz** | **92.00** | **89.50** | 72.00 | 19.00 | **31.25** | 38.75 | 29.25 | 26.50 | **85.50** | 53.75 |
| **GraphWiz-DPO** | 85.50 | 79.50 | **85.50** | **85.25** | 12.50 | 29.00 | 35.50 | 62.75 | 48.50 | 58.22 |
| *LLaMA 2-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 41.25 | 4.00 | 9.50 | 30.00 | 16.50 | 69.00 | 75.45 | 44.81 |
| **GraphWiz** | 91.50 | 87.00 | 74.00 | 18.00 | **28.00** | 38.25 | 24.50 | 52.25 | **82.25** | 55.08 |
| **GraphWiz-DPO** | 89.00 | 82.50 | 84.75 | 46.75 | 24.00 | **52.75** | **43.50** | **81.50** | 77.25 | **65.00** |
| *LLaMA 2-13B* |||||||||||
| **Naive SFT** | 73.75 | 83.75 | 59.00 | 0.50 | 11.75 | 34.75 | 24.25 | 59.75 | 54.75 | 44.69 |
| **GraphWiz** | **94.75** | 87.00 | 78.00 | 28.00 | 27.75 | 36.00 | 24.50 | 59.00 | 81.50 | 57.39 |
| **GraphWiz-DPO** | 87.50 | **88.50** | **88.25** | **72.75** | 22.00 | **48.75** | **43.75** | 46.50 | 77.00 | **63.89** |
## Examples
```
G-Q: Determine whether or not there is a cycle in an undirected graph. In an undirected graph..,the
nodes are numbered from 0 to 88, and the edges are: (0, 73) (0, 51) (0, 10) (0, 63) (0, 28) (1, 62) (1, 57) (1, 84) (1, 61) (1, 5)
(1, 24) (2, 84) (2, 3) (2, 66) (2, 68) (2, 17) (2, 35) (2, 34) (2, 15) (3, 39) (3, 52) (3, 16) (3, 15) (3, 8) (4, 69) (4, 85)
(4, 36) (4, 72) (5, 44) (6, 77) (6, 7) (7, 85) (8, 64) (8, 23) (8, 28) (9, 34) (9, 31) (9, 61) (9, 28) (10, 26) (11, 37) (11, 39)
(11, 19) (11, 64) (13, 73) (13, 61) (13, 80) (13, 85) (14, 86) (14, 59) (14, 32) (14, 58) (14, 85) (14, 66) (15, 43) (15, 48) (15, 73)
(15, 19) (15, 47) (15, 68) (16, 46) (16, 60) (16, 84) (17, 44) (17, 72) (17, 36) (17, 37) (17, 61) (18, 20) (18, 24) (18, 22) (18, 41)
(19, 45) (19, 83) (20, 25) (20, 29) (21, 38) (21, 64) (21, 24) (21, 22) (21, 34) (22, 23) (23, 34) (23, 30) (23, 83) (24, 47) (24, 50)
(25, 59) (25, 42) (25, 70) (25, 72) (26, 45) (26, 30) (26, 87) (26, 80) (26, 50) (27, 77) (27, 58) (27, 60) (27, 29) (28, 36) (28, 59)
(28, 88) (29, 31) (29, 33) (29, 56) (29, 55) (29, 78) (29, 73) (29, 82) (30, 34) (31, 58) (31, 70) (32, 78) (33, 73) (33, 68) (34, 48)
(34, 88) (34, 51) (34, 66) (34, 54) (35, 43) (35, 56) (36, 44) (36, 39) (37, 57) (37, 71) (38, 47) (38, 58) (38, 88) (39, 74) (39, 48)
(39, 45) (39, 42) (40, 60) (40, 59) (40, 71) (40, 76) (41, 79) (42, 60) (42, 62) (42, 72) (43, 57) (44, 47) (44, 74) (44, 75) (44, 82)
(44, 70) (45, 68) (45, 67) (45, 71) (46, 84) (47, 56) (48, 75) (49, 71) (49, 58) (50, 76) (51, 55) (51, 87) (51, 79) (52, 78) (56, 69)
(56, 83) (56, 79) (57, 76) (57, 70) (57, 72) (57, 78) (59, 70) (60, 80) (60, 76) (60, 77) (60, 75) (61, 74) (61, 77) (62, 84) (62, 66)
(66, 69) (68, 88) (70, 88) (70, 76) (71, 81) (71, 72) (71, 86) (72, 75) (73, 82) (73, 83) (74, 85). Is there a cycle in this graph?
GraphWiz:
There are many possible ways to find a cycle in this graph, but one example is: Starting from node 0, we can go to node 73 (via edge 0-73), then to node 82 (via edge 73-82),
then to node 44 (via edge 82-44), then to node 17 (via edge 44-17), then to node 36 (via edge 17-36), then to node 28 (via edge 36-28),
and back to node 0 (via edge 28-0). This forms a cycle [0-73-82-44-17-36-28-0] without revisiting any edge. So, there is a cycle in this graph.
```
|
GraphWiz/LLaMA2-13B-RFT
|
GraphWiz
| 2024-02-27T04:12:41Z | 8 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"graph problem",
"dataset:GraphWiz/GraphInstruct-RFT-72K",
"arxiv:2402.16029",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-23T08:27:46Z |
---
license: apache-2.0
datasets:
- GraphWiz/GraphInstruct-RFT-72K
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- graph problem
---
# GraphWiz
Project Page: [https://graph-wiz.github.io/](https://graph-wiz.github.io/)
Paper: [https://arxiv.org/abs/2402.16029.pdf](https://arxiv.org/abs/2402.16029)
Code: [https://github.com/nuochenpku/Graph-Reasoning-LLM](https://github.com/nuochenpku/Graph-Reasoning-LLM)
GraphWiz is a powerful instruction-following LLM that can map textural descriptions of graphs and structures, and then solve different graph problems explicitly in natural language.
Training strategies include two stages: **Mixed-task Training** and **DPO Alignment**.
## Results
| *Models* | **Cycle** | **Connect** | **Bipartite** | **Topology** | **Shortest** | **Triangle** | **Flow** | **Hamilton** | **Subgraph** | **Average** |
|:-------------------------------------:|:-------------------------------:|:--------------------------------:|:----------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------:|:---------------------------------:|:---------------------------------:|:--------------------------------------:|
| *In-Context Learning* |||||||||||
| **GPT-4 (zero-shot)** | 38.75 | 17.00 | 65.25 | 5.00 | 9.25 | 5.75 | 3.25 | 59.25 | 45.50 | 27.67 |
| **GhatGPT (2-shot)** | 51.25 | 43.75 | 70.75 | 4.50 | 3.50 | 17.25 | 8.50 | 54.25 | 43.00 | 32.97 |
| **GPT-4 (2-shot)** | 52.50 | 62.75 | 74.25 | 25.25 | 18.25 | 31.00 | 7.75 | {75.75} | 46.75 | 43.81 |
| *Mistral-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 78.50 | 1.00 | 23.00 | 47.00 | 28.75 | 31.75 | 41.25 | 46.56 |
| **GraphWiz** | **92.00** | **89.50** | 72.00 | 19.00 | **31.25** | 38.75 | 29.25 | 26.50 | **85.50** | 53.75 |
| **GraphWiz-DPO** | 85.50 | 79.50 | **85.50** | **85.25** | 12.50 | 29.00 | 35.50 | 62.75 | 48.50 | 58.22 |
| *LLaMA 2-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 41.25 | 4.00 | 9.50 | 30.00 | 16.50 | 69.00 | 75.45 | 44.81 |
| **GraphWiz** | 91.50 | 87.00 | 74.00 | 18.00 | **28.00** | 38.25 | 24.50 | 52.25 | **82.25** | 55.08 |
| **GraphWiz-DPO** | 89.00 | 82.50 | 84.75 | 46.75 | 24.00 | **52.75** | **43.50** | **81.50** | 77.25 | **65.00** |
| *LLaMA 2-13B* |||||||||||
| **Naive SFT** | 73.75 | 83.75 | 59.00 | 0.50 | 11.75 | 34.75 | 24.25 | 59.75 | 54.75 | 44.69 |
| **GraphWiz** | **94.75** | 87.00 | 78.00 | 28.00 | 27.75 | 36.00 | 24.50 | 59.00 | 81.50 | 57.39 |
| **GraphWiz-DPO** | 87.50 | **88.50** | **88.25** | **72.75** | 22.00 | **48.75** | **43.75** | 46.50 | 77.00 | **63.89** |
## Examples
```
G-Q: Determine whether or not there is a cycle in an undirected graph. In an undirected graph..,the
nodes are numbered from 0 to 88, and the edges are: (0, 73) (0, 51) (0, 10) (0, 63) (0, 28) (1, 62) (1, 57) (1, 84) (1, 61) (1, 5)
(1, 24) (2, 84) (2, 3) (2, 66) (2, 68) (2, 17) (2, 35) (2, 34) (2, 15) (3, 39) (3, 52) (3, 16) (3, 15) (3, 8) (4, 69) (4, 85)
(4, 36) (4, 72) (5, 44) (6, 77) (6, 7) (7, 85) (8, 64) (8, 23) (8, 28) (9, 34) (9, 31) (9, 61) (9, 28) (10, 26) (11, 37) (11, 39)
(11, 19) (11, 64) (13, 73) (13, 61) (13, 80) (13, 85) (14, 86) (14, 59) (14, 32) (14, 58) (14, 85) (14, 66) (15, 43) (15, 48) (15, 73)
(15, 19) (15, 47) (15, 68) (16, 46) (16, 60) (16, 84) (17, 44) (17, 72) (17, 36) (17, 37) (17, 61) (18, 20) (18, 24) (18, 22) (18, 41)
(19, 45) (19, 83) (20, 25) (20, 29) (21, 38) (21, 64) (21, 24) (21, 22) (21, 34) (22, 23) (23, 34) (23, 30) (23, 83) (24, 47) (24, 50)
(25, 59) (25, 42) (25, 70) (25, 72) (26, 45) (26, 30) (26, 87) (26, 80) (26, 50) (27, 77) (27, 58) (27, 60) (27, 29) (28, 36) (28, 59)
(28, 88) (29, 31) (29, 33) (29, 56) (29, 55) (29, 78) (29, 73) (29, 82) (30, 34) (31, 58) (31, 70) (32, 78) (33, 73) (33, 68) (34, 48)
(34, 88) (34, 51) (34, 66) (34, 54) (35, 43) (35, 56) (36, 44) (36, 39) (37, 57) (37, 71) (38, 47) (38, 58) (38, 88) (39, 74) (39, 48)
(39, 45) (39, 42) (40, 60) (40, 59) (40, 71) (40, 76) (41, 79) (42, 60) (42, 62) (42, 72) (43, 57) (44, 47) (44, 74) (44, 75) (44, 82)
(44, 70) (45, 68) (45, 67) (45, 71) (46, 84) (47, 56) (48, 75) (49, 71) (49, 58) (50, 76) (51, 55) (51, 87) (51, 79) (52, 78) (56, 69)
(56, 83) (56, 79) (57, 76) (57, 70) (57, 72) (57, 78) (59, 70) (60, 80) (60, 76) (60, 77) (60, 75) (61, 74) (61, 77) (62, 84) (62, 66)
(66, 69) (68, 88) (70, 88) (70, 76) (71, 81) (71, 72) (71, 86) (72, 75) (73, 82) (73, 83) (74, 85). Is there a cycle in this graph?
GraphWiz:
There are many possible ways to find a cycle in this graph, but one example is: Starting from node 0, we can go to node 73 (via edge 0-73), then to node 82 (via edge 73-82),
then to node 44 (via edge 82-44), then to node 17 (via edge 44-17), then to node 36 (via edge 17-36), then to node 28 (via edge 36-28),
and back to node 0 (via edge 28-0). This forms a cycle [0-73-82-44-17-36-28-0] without revisiting any edge. So, there is a cycle in this graph.
```
|
GraphWiz/LLaMA2-13B
|
GraphWiz
| 2024-02-27T04:12:06Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"graph problem",
"dataset:GraphWiz/GraphInstruct-RFT-72K",
"arxiv:2402.16029",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-23T08:27:25Z |
---
license: apache-2.0
datasets:
- GraphWiz/GraphInstruct-RFT-72K
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- graph problem
---
# GraphWiz
Project Page: [https://graph-wiz.github.io/](https://graph-wiz.github.io/)
Paper: [https://arxiv.org/abs/2402.16029.pdf](https://arxiv.org/abs/2402.16029)
Code: [https://github.com/nuochenpku/Graph-Reasoning-LLM](https://github.com/nuochenpku/Graph-Reasoning-LLM)
GraphWiz is a powerful instruction-following LLM that can map textural descriptions of graphs and structures, and then solve different graph problems explicitly in natural language.
Training strategies include two stages: **Mixed-task Training** and **DPO Alignment**.
## Results
| *Models* | **Cycle** | **Connect** | **Bipartite** | **Topology** | **Shortest** | **Triangle** | **Flow** | **Hamilton** | **Subgraph** | **Average** |
|:-------------------------------------:|:-------------------------------:|:--------------------------------:|:----------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------:|:---------------------------------:|:---------------------------------:|:--------------------------------------:|
| *In-Context Learning* |||||||||||
| **GPT-4 (zero-shot)** | 38.75 | 17.00 | 65.25 | 5.00 | 9.25 | 5.75 | 3.25 | 59.25 | 45.50 | 27.67 |
| **GhatGPT (2-shot)** | 51.25 | 43.75 | 70.75 | 4.50 | 3.50 | 17.25 | 8.50 | 54.25 | 43.00 | 32.97 |
| **GPT-4 (2-shot)** | 52.50 | 62.75 | 74.25 | 25.25 | 18.25 | 31.00 | 7.75 | {75.75} | 46.75 | 43.81 |
| *Mistral-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 78.50 | 1.00 | 23.00 | 47.00 | 28.75 | 31.75 | 41.25 | 46.56 |
| **GraphWiz** | **92.00** | **89.50** | 72.00 | 19.00 | **31.25** | 38.75 | 29.25 | 26.50 | **85.50** | 53.75 |
| **GraphWiz-DPO** | 85.50 | 79.50 | **85.50** | **85.25** | 12.50 | 29.00 | 35.50 | 62.75 | 48.50 | 58.22 |
| *LLaMA 2-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 41.25 | 4.00 | 9.50 | 30.00 | 16.50 | 69.00 | 75.45 | 44.81 |
| **GraphWiz** | 91.50 | 87.00 | 74.00 | 18.00 | **28.00** | 38.25 | 24.50 | 52.25 | **82.25** | 55.08 |
| **GraphWiz-DPO** | 89.00 | 82.50 | 84.75 | 46.75 | 24.00 | **52.75** | **43.50** | **81.50** | 77.25 | **65.00** |
| *LLaMA 2-13B* |||||||||||
| **Naive SFT** | 73.75 | 83.75 | 59.00 | 0.50 | 11.75 | 34.75 | 24.25 | 59.75 | 54.75 | 44.69 |
| **GraphWiz** | **94.75** | 87.00 | 78.00 | 28.00 | 27.75 | 36.00 | 24.50 | 59.00 | 81.50 | 57.39 |
| **GraphWiz-DPO** | 87.50 | **88.50** | **88.25** | **72.75** | 22.00 | **48.75** | **43.75** | 46.50 | 77.00 | **63.89** |
## Examples
```
G-Q: Determine whether or not there is a cycle in an undirected graph. In an undirected graph..,the
nodes are numbered from 0 to 88, and the edges are: (0, 73) (0, 51) (0, 10) (0, 63) (0, 28) (1, 62) (1, 57) (1, 84) (1, 61) (1, 5)
(1, 24) (2, 84) (2, 3) (2, 66) (2, 68) (2, 17) (2, 35) (2, 34) (2, 15) (3, 39) (3, 52) (3, 16) (3, 15) (3, 8) (4, 69) (4, 85)
(4, 36) (4, 72) (5, 44) (6, 77) (6, 7) (7, 85) (8, 64) (8, 23) (8, 28) (9, 34) (9, 31) (9, 61) (9, 28) (10, 26) (11, 37) (11, 39)
(11, 19) (11, 64) (13, 73) (13, 61) (13, 80) (13, 85) (14, 86) (14, 59) (14, 32) (14, 58) (14, 85) (14, 66) (15, 43) (15, 48) (15, 73)
(15, 19) (15, 47) (15, 68) (16, 46) (16, 60) (16, 84) (17, 44) (17, 72) (17, 36) (17, 37) (17, 61) (18, 20) (18, 24) (18, 22) (18, 41)
(19, 45) (19, 83) (20, 25) (20, 29) (21, 38) (21, 64) (21, 24) (21, 22) (21, 34) (22, 23) (23, 34) (23, 30) (23, 83) (24, 47) (24, 50)
(25, 59) (25, 42) (25, 70) (25, 72) (26, 45) (26, 30) (26, 87) (26, 80) (26, 50) (27, 77) (27, 58) (27, 60) (27, 29) (28, 36) (28, 59)
(28, 88) (29, 31) (29, 33) (29, 56) (29, 55) (29, 78) (29, 73) (29, 82) (30, 34) (31, 58) (31, 70) (32, 78) (33, 73) (33, 68) (34, 48)
(34, 88) (34, 51) (34, 66) (34, 54) (35, 43) (35, 56) (36, 44) (36, 39) (37, 57) (37, 71) (38, 47) (38, 58) (38, 88) (39, 74) (39, 48)
(39, 45) (39, 42) (40, 60) (40, 59) (40, 71) (40, 76) (41, 79) (42, 60) (42, 62) (42, 72) (43, 57) (44, 47) (44, 74) (44, 75) (44, 82)
(44, 70) (45, 68) (45, 67) (45, 71) (46, 84) (47, 56) (48, 75) (49, 71) (49, 58) (50, 76) (51, 55) (51, 87) (51, 79) (52, 78) (56, 69)
(56, 83) (56, 79) (57, 76) (57, 70) (57, 72) (57, 78) (59, 70) (60, 80) (60, 76) (60, 77) (60, 75) (61, 74) (61, 77) (62, 84) (62, 66)
(66, 69) (68, 88) (70, 88) (70, 76) (71, 81) (71, 72) (71, 86) (72, 75) (73, 82) (73, 83) (74, 85). Is there a cycle in this graph?
GraphWiz:
There are many possible ways to find a cycle in this graph, but one example is: Starting from node 0, we can go to node 73 (via edge 0-73), then to node 82 (via edge 73-82),
then to node 44 (via edge 82-44), then to node 17 (via edge 44-17), then to node 36 (via edge 17-36), then to node 28 (via edge 36-28),
and back to node 0 (via edge 28-0). This forms a cycle [0-73-82-44-17-36-28-0] without revisiting any edge. So, there is a cycle in this graph.
```
|
GraphWiz/Mistral-7B
|
GraphWiz
| 2024-02-27T04:11:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"graph problem",
"dataset:GraphWiz/GraphInstruct-RFT-72K",
"arxiv:2402.16029",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-15T16:30:50Z |
---
license: apache-2.0
datasets:
- GraphWiz/GraphInstruct-RFT-72K
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- graph problem
---
# GraphWiz
Project Page: [https://graph-wiz.github.io/](https://graph-wiz.github.io/)
Paper: [https://arxiv.org/abs/2402.16029.pdf](https://arxiv.org/abs/2402.16029)
Code: [https://github.com/nuochenpku/Graph-Reasoning-LLM](https://github.com/nuochenpku/Graph-Reasoning-LLM)
GraphWiz is a powerful instruction-following LLM that can map textural descriptions of graphs and structures, and then solve different graph problems explicitly in natural language.
Training strategies include two stages: **Mixed-task Training** and **DPO Alignment**.
## Results
| *Models* | **Cycle** | **Connect** | **Bipartite** | **Topology** | **Shortest** | **Triangle** | **Flow** | **Hamilton** | **Subgraph** | **Average** |
|:-------------------------------------:|:-------------------------------:|:--------------------------------:|:----------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------:|:---------------------------------:|:---------------------------------:|:--------------------------------------:|
| *In-Context Learning* |||||||||||
| **GPT-4 (zero-shot)** | 38.75 | 17.00 | 65.25 | 5.00 | 9.25 | 5.75 | 3.25 | 59.25 | 45.50 | 27.67 |
| **GhatGPT (2-shot)** | 51.25 | 43.75 | 70.75 | 4.50 | 3.50 | 17.25 | 8.50 | 54.25 | 43.00 | 32.97 |
| **GPT-4 (2-shot)** | 52.50 | 62.75 | 74.25 | 25.25 | 18.25 | 31.00 | 7.75 | {75.75} | 46.75 | 43.81 |
| *Mistral-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 78.50 | 1.00 | 23.00 | 47.00 | 28.75 | 31.75 | 41.25 | 46.56 |
| **GraphWiz** | **92.00** | **89.50** | 72.00 | 19.00 | **31.25** | 38.75 | 29.25 | 26.50 | **85.50** | 53.75 |
| **GraphWiz-DPO** | 85.50 | 79.50 | **85.50** | **85.25** | 12.50 | 29.00 | 35.50 | 62.75 | 48.50 | 58.22 |
| *LLaMA 2-7B* |||||||||||
| **Naive SFT** | 73.75 | 83.50 | 41.25 | 4.00 | 9.50 | 30.00 | 16.50 | 69.00 | 75.45 | 44.81 |
| **GraphWiz** | 91.50 | 87.00 | 74.00 | 18.00 | **28.00** | 38.25 | 24.50 | 52.25 | **82.25** | 55.08 |
| **GraphWiz-DPO** | 89.00 | 82.50 | 84.75 | 46.75 | 24.00 | **52.75** | **43.50** | **81.50** | 77.25 | **65.00** |
| *LLaMA 2-13B* |||||||||||
| **Naive SFT** | 73.75 | 83.75 | 59.00 | 0.50 | 11.75 | 34.75 | 24.25 | 59.75 | 54.75 | 44.69 |
| **GraphWiz** | **94.75** | 87.00 | 78.00 | 28.00 | 27.75 | 36.00 | 24.50 | 59.00 | 81.50 | 57.39 |
| **GraphWiz-DPO** | 87.50 | **88.50** | **88.25** | **72.75** | 22.00 | **48.75** | **43.75** | 46.50 | 77.00 | **63.89** |
## Examples
```
G-Q: Determine whether or not there is a cycle in an undirected graph. In an undirected graph..,the
nodes are numbered from 0 to 88, and the edges are: (0, 73) (0, 51) (0, 10) (0, 63) (0, 28) (1, 62) (1, 57) (1, 84) (1, 61) (1, 5)
(1, 24) (2, 84) (2, 3) (2, 66) (2, 68) (2, 17) (2, 35) (2, 34) (2, 15) (3, 39) (3, 52) (3, 16) (3, 15) (3, 8) (4, 69) (4, 85)
(4, 36) (4, 72) (5, 44) (6, 77) (6, 7) (7, 85) (8, 64) (8, 23) (8, 28) (9, 34) (9, 31) (9, 61) (9, 28) (10, 26) (11, 37) (11, 39)
(11, 19) (11, 64) (13, 73) (13, 61) (13, 80) (13, 85) (14, 86) (14, 59) (14, 32) (14, 58) (14, 85) (14, 66) (15, 43) (15, 48) (15, 73)
(15, 19) (15, 47) (15, 68) (16, 46) (16, 60) (16, 84) (17, 44) (17, 72) (17, 36) (17, 37) (17, 61) (18, 20) (18, 24) (18, 22) (18, 41)
(19, 45) (19, 83) (20, 25) (20, 29) (21, 38) (21, 64) (21, 24) (21, 22) (21, 34) (22, 23) (23, 34) (23, 30) (23, 83) (24, 47) (24, 50)
(25, 59) (25, 42) (25, 70) (25, 72) (26, 45) (26, 30) (26, 87) (26, 80) (26, 50) (27, 77) (27, 58) (27, 60) (27, 29) (28, 36) (28, 59)
(28, 88) (29, 31) (29, 33) (29, 56) (29, 55) (29, 78) (29, 73) (29, 82) (30, 34) (31, 58) (31, 70) (32, 78) (33, 73) (33, 68) (34, 48)
(34, 88) (34, 51) (34, 66) (34, 54) (35, 43) (35, 56) (36, 44) (36, 39) (37, 57) (37, 71) (38, 47) (38, 58) (38, 88) (39, 74) (39, 48)
(39, 45) (39, 42) (40, 60) (40, 59) (40, 71) (40, 76) (41, 79) (42, 60) (42, 62) (42, 72) (43, 57) (44, 47) (44, 74) (44, 75) (44, 82)
(44, 70) (45, 68) (45, 67) (45, 71) (46, 84) (47, 56) (48, 75) (49, 71) (49, 58) (50, 76) (51, 55) (51, 87) (51, 79) (52, 78) (56, 69)
(56, 83) (56, 79) (57, 76) (57, 70) (57, 72) (57, 78) (59, 70) (60, 80) (60, 76) (60, 77) (60, 75) (61, 74) (61, 77) (62, 84) (62, 66)
(66, 69) (68, 88) (70, 88) (70, 76) (71, 81) (71, 72) (71, 86) (72, 75) (73, 82) (73, 83) (74, 85). Is there a cycle in this graph?
GraphWiz:
There are many possible ways to find a cycle in this graph, but one example is: Starting from node 0, we can go to node 73 (via edge 0-73), then to node 82 (via edge 73-82),
then to node 44 (via edge 82-44), then to node 17 (via edge 44-17), then to node 36 (via edge 17-36), then to node 28 (via edge 36-28),
and back to node 0 (via edge 28-0). This forms a cycle [0-73-82-44-17-36-28-0] without revisiting any edge. So, there is a cycle in this graph.
```
|
mayacinka/djinn-7b
|
mayacinka
| 2024-02-27T04:10:51Z | 6 | 2 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"paulml/DPOB-INMTOB-7B",
"bardsai/jaskier-7b-dpo-v6.1",
"base_model:bardsai/jaskier-7b-dpo-v6.1",
"base_model:merge:bardsai/jaskier-7b-dpo-v6.1",
"base_model:paulml/DPOB-INMTOB-7B",
"base_model:merge:paulml/DPOB-INMTOB-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T00:12:26Z |
---
tags:
- merge
- mergekit
- lazymergekit
- paulml/DPOB-INMTOB-7B
- bardsai/jaskier-7b-dpo-v6.1
base_model:
- paulml/DPOB-INMTOB-7B
- bardsai/jaskier-7b-dpo-v6.1
---
# djinn-7b
djinn-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [paulml/DPOB-INMTOB-7B](https://huggingface.co/paulml/DPOB-INMTOB-7B)
* [bardsai/jaskier-7b-dpo-v6.1](https://huggingface.co/bardsai/jaskier-7b-dpo-v6.1)
# 🏆 Benchmarks
#### Open LLM Leaderboard
| Model | Average | ARC_easy | HellaSwag | MMLU | TruthfulQA_mc2 | Winogrande | GSM8K |
|------------------------|--------:|-----:|----------:|-----:|-----------:|-----------:|------:|
| mayacinka/djinn-7B | 78.40 | 86.7 | 87.37| 61.84 | 77.23 | 82.64 | 74.68|
#### MMLU (per category)
| Groups |Version|Filter|n-shot|Metric|Value | |Stderr|
|------------------|-------|------|------|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.6184|± |0.0039|
| - humanities |N/A |none |None |acc |0.5741|± |0.0067|
| - other |N/A |none |None |acc |0.6933|± |0.0079|
| - social_sciences|N/A |none |None |acc |0.7166|± |0.0080|
| - stem |N/A |none |None |acc |0.5147|± |0.0085|
### AutoEval
[Maxime Labonne's autoeval notebook](https://gist.github.com/majacinka/dfa0800c65f995c8f970c75f3e73d268)
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|-----------------------------------------------------|------:|------:|---------:|-------:|------:|
|[djinn-7b](https://huggingface.co/mayacinka/djinn-7b)| 44.9| 77.33| 77.18| 49.36| 62.19|
## 🧩 Configuration
```yaml
slices:
- sources:
- model: paulml/DPOB-INMTOB-7B
layer_range: [0, 32]
- model: bardsai/jaskier-7b-dpo-v6.1
layer_range: [0, 32]
merge_method: slerp
base_model: paulml/DPOB-INMTOB-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayacinka/djinn-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
Nuo97/COMEDY_13B_DPO
|
Nuo97
| 2024-02-27T04:09:13Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"question-answering",
"zh",
"dataset:Nuo97/Dolphin-DPO",
"arxiv:2402.11975",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-02-18T13:19:34Z |
---
license: apache-2.0
datasets:
- Nuo97/Dolphin-DPO
language:
- zh
metrics:
- bleu
pipeline_tag: question-answering
---
# COMEDY: COmpressive Memory-Enhanced Dialogue sYstems framework.
Github: https://github.com/nuochenpku/COMEDY
Paper: https://arxiv.org/abs/2402.11975.pdf
<br>
<div align="center">
<img src="comedy.png" width="40%" title="Introduction Figure">
</div>
### Task: Long-Term Conversation Dialogue Generation
Different from previous retrieval-based methods, COMEDY doesn't rely on any **retrieval module or database**.
Instead, COMEDY adopts a groundbreaking ''**One-for-All**'' approach, utilizing a single, unified model to manage the entire process from memory generation, compression to final response generation for long-term memory dialogue generation.
- COMEDY firstly involves distilling session-specific memory from past dialogues, encompassing fine-grained session summaries, including event recaps, and detailed user and bot portraits;
- In a break from traditional systems, COMEDY eschews the use of a memory database for storing these insights. Instead, it reprocesses and condenses memories from all past interactions, forming a *Compressive Memory*: The first part is the **concise events** that have occurred throughout all the conversations, creating a historical narrative that the system can draw upon. The second and third parts consist of a **detailed user profile** and the **dynamic relationship changes** between the user and chatbot across sessions, both derived from past conversational events.
- Finally, COMEDY skillfully integrates this compressive memory into ongoing conversations, enabling contextually memory-enhanced interactions.
### Training Dataset
**Dolphin**, the biggest Chinese long-term conversation dataset, from actual online user-chatbot interactions.
This dataset contains three tasks:
**Session-Level Memory Summarization**;
**Memory Compression**;
**Memory-Grounded Response Generation**,
comprising an extensive collection of 100k samples.
Dolphin is available at [**Dolphin**](https://huggingface.co/datasets/Nuo97/Dolphin-DPO)
### Training Strategy
Our training strategies include two stages: Mixed-task training and DPO Alignment.
<br>
<div align="center">
<img src="training_strategy.png" width="90%" title="Introduction Figure">
</div>
|
SyedShaheer/distilbart-cnn-12-6_TUNED
|
SyedShaheer
| 2024-02-27T03:57:40Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2024-02-27T03:51:27Z |
---
language:
- en
metrics:
- rouge
pipeline_tag: summarization
---
|
wilkensgomes/gemma-2b-canarim
|
wilkensgomes
| 2024-02-27T03:53:38Z | 111 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T03:50:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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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
<!-- 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
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[More Information Needed]
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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. -->
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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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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sessex/margiela-style-small-LoRA
|
sessex
| 2024-02-27T03:51:34Z | 8 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-02-27T03:51:32Z |
---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: in the style of designer maison-margiela
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 - sessex/margiela-style-small-LoRA
<Gallery />
## Model description
These are sessex/margiela-style-small-LoRA 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 in the style of designer maison-margiela to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](sessex/margiela-style-small-LoRA/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]
|
macto/exllamav2-test
|
macto
| 2024-02-27T03:44:58Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-02-27T03:39:24Z |
---
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. -->
- **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]
|
FlippyCode/ppo-Huggy
|
FlippyCode
| 2024-02-27T03:44:16Z | 11 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2024-02-27T03:42:41Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: FlippyCode/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
cerkut/mps-tuned-gtzan
|
cerkut
| 2024-02-27T03:40:32Z | 162 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-02-27T03:30:41Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: mps-tuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: gtzan
type: gtzan
config: all
split: None
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.75
---
<!-- 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. -->
# mps-tuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the gtzan dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2018
- Accuracy: 0.75
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 2.2018 | 0.75 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.0
- Tokenizers 0.15.2
|
316usman/thematic_3
|
316usman
| 2024-02-27T03:39:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2024-02-22T08:23:09Z |
---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: thematic_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. -->
# thematic_3
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the 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: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
thu-coai/ShieldLM-7B-internlm2
|
thu-coai
| 2024-02-27T03:34:55Z | 66 | 10 |
transformers
|
[
"transformers",
"safetensors",
"internlm2",
"feature-extraction",
"custom_code",
"en",
"zh",
"arxiv:2402.16444",
"license:mit",
"region:us"
] |
feature-extraction
| 2024-02-26T09:54:18Z |
---
license: mit
language:
- en
- zh
---
## Introduction
The ShieldLM model ([paper link](https://arxiv.org/abs/2402.16444)) initialized from [internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b). ShieldLM is a bilingual (Chinese and English) safety detector that mainly aims to help to detect safety issues in LLMs' generations. It aligns with general human safety standards, supports fine-grained customizable detection rules, and provides explanations for its decisions.
Refer to our [github repository](https://github.com/thu-coai/ShieldLM) for more detailed information.
## Usage
Please refer to our [github repository](https://github.com/thu-coai/ShieldLM) for the detailed usage instructions.
## Performance
ShieldLM demonstrates impressive detection performance across 4 ID and OOD test sets, compared to strong baselines such as GPT-4, Llama Guard and Perspective API.
Refer to [our paper](https://arxiv.org/abs/2402.16444) for more detailed evaluation results.
|
OwOpeepeepoopoo/easy_america2
|
OwOpeepeepoopoo
| 2024-02-27T03:31:04Z | 110 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T03:28:32Z |
---
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]
|
DatPySci/pythia-1b-self-kto-iter1
|
DatPySci
| 2024-02-27T03:13:06Z | 115 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"conversational",
"dataset:generated/iter1",
"base_model:DatPySci/pythia-1b-self-kto-iter0",
"base_model:finetune:DatPySci/pythia-1b-self-kto-iter0",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-26T14:57:21Z |
---
license: apache-2.0
base_model: DatPySci/pythia-1b-self-kto-iter0
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- generated/iter1
model-index:
- name: pythia-1b-self-kto-iter1
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. -->
# pythia-1b-self-kto-iter1
This model is a fine-tuned version of [DatPySci/pythia-1b-self-kto-iter0](https://huggingface.co/DatPySci/pythia-1b-self-kto-iter0) on the generated/iter1 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF
|
cgato
| 2024-02-27T03:10:29Z | 8 | 0 | null |
[
"gguf",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T01:39:48Z |
---
license: cc-by-nc-4.0
---
This model is the first in a series of experiments to make my models a bit smarter. Its nowhere near done, but my initial testing was good so I'm uploading so people can check it out.
Datasets Used:
* Dolphin
* Ultrachat
* Capybara
* Augmental
* ToxicQA
* Magiccoder-Evol-Instruct-110k
* Yahoo Answers
* OpenOrca
* Airoboros 3.1
* grimulkan/physical-reasoning and theory-of-mind
## Prompt Format: Chat ( The default Ooba template and Silly Tavern Template )
```
{System Prompt}
Username: {Input}
BotName: {Response}
Username: {Input}
BotName: {Response}
```
## Recommended Silly Tavern Preset -> (Temp: 1.25, MinP: 0.1, RepPen: 1.03)
## Recommended Kobold Horde Preset -> MinP
|
kanishka/smolm-autoreg-bpe-counterfactual-babylm-only_random_removal-1e-3
|
kanishka
| 2024-02-27T03:07:05Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual-babylm-only_random_removal",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-26T04:36:40Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_random_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-only_random_removal-1e-3
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual-babylm-only_random_removal
type: kanishka/counterfactual-babylm-only_random_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.4103301921111753
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-counterfactual-babylm-only_random_removal-1e-3
This model was trained from scratch on the kanishka/counterfactual-babylm-only_random_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4056
- Accuracy: 0.4103
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6071 | 1.0 | 18588 | 3.7805 | 0.3590 |
| 3.3943 | 2.0 | 37176 | 3.5796 | 0.3806 |
| 3.2625 | 3.0 | 55764 | 3.4678 | 0.3915 |
| 3.1838 | 4.0 | 74352 | 3.3962 | 0.3998 |
| 3.1277 | 5.0 | 92940 | 3.3849 | 0.4017 |
| 3.0813 | 6.0 | 111528 | 3.3874 | 0.4040 |
| 3.0519 | 7.0 | 130116 | 3.3394 | 0.4079 |
| 3.0181 | 8.0 | 148704 | 3.3441 | 0.4085 |
| 2.9888 | 9.0 | 167292 | 3.3545 | 0.4088 |
| 2.9602 | 10.0 | 185880 | 3.3501 | 0.4088 |
| 2.942 | 11.0 | 204468 | 3.3509 | 0.4095 |
| 2.9174 | 12.0 | 223056 | 3.3709 | 0.4093 |
| 2.8989 | 13.0 | 241644 | 3.3608 | 0.4107 |
| 2.8757 | 14.0 | 260232 | 3.3651 | 0.4101 |
| 2.8506 | 15.0 | 278820 | 3.3638 | 0.4109 |
| 2.8373 | 16.0 | 297408 | 3.3724 | 0.4107 |
| 2.8195 | 17.0 | 315996 | 3.3819 | 0.4108 |
| 2.7983 | 18.0 | 334584 | 3.3819 | 0.4110 |
| 2.7786 | 19.0 | 353172 | 3.3970 | 0.4103 |
| 2.7635 | 20.0 | 371760 | 3.4056 | 0.4103 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
sharren/vit-dropout-v9
|
sharren
| 2024-02-27T02:58:09Z | 192 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:sharren/SkinCancerClassification",
"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
| 2024-02-26T18:32:14Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- value: 0.8677
model-index:
- name: vit-dropout-v9
results: []
datasets:
- sharren/SkinCancerClassification
---
<!-- 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-dropout-v9
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 sharren/SkinCancerClassification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5147
- Accuracy: 0.8677
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
- dropout: 0.3
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5952 | 1.56 | 500 | 0.8221 | 0.7228 |
| 0.4505 | 3.12 | 1000 | 0.5679 | 0.7934 |
| 0.4187 | 4.67 | 1500 | 0.4951 | 0.8221 |
| 0.4022 | 6.23 | 2000 | 0.5013 | 0.8252 |
| 0.3485 | 7.79 | 2500 | 0.4532 | 0.8446 |
| 0.2397 | 9.35 | 3000 | 0.4914 | 0.8558 |
| 0.3017 | 10.9 | 3500 | 0.4973 | 0.8514 |
| 0.2086 | 12.46 | 4000 | 0.4987 | 0.8689 |
| 0.1265 | 14.02 | 4500 | 0.5132 | 0.8652 |
| 0.0885 | 15.58 | 5000 | 0.5147 | 0.8677 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
djsull/sentence-roberta-multitask
|
djsull
| 2024-02-27T02:51:30Z | 11 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-02-27T02:40:28Z |
---
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
pipeline_tag: sentence-similarity
---
# djsull/sentence-roberta-multitask
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('djsull/sentence-roberta-multitask')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('djsull/sentence-roberta-multitask')
model = AutoModel.from_pretrained('djsull/sentence-roberta-multitask')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=djsull/sentence-roberta-multitask)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8885 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
```
{'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 256], 'matryoshka_weights': [1, 1]}
```
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 719 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
```
{'loss': 'CosineSimilarityLoss', 'matryoshka_dims': [768, 256], 'matryoshka_weights': [1, 1]}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 360,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
kaljr/ppo-LunarLander-v2
|
kaljr
| 2024-02-27T02:38:45Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-27T02:38:21Z |
---
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: 253.45 +/- 10.35
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
...
```
|
Prasadrao/twitter-roberta-large-go-emotions
|
Prasadrao
| 2024-02-27T02:38:12Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-roberta-large-2022-154m",
"base_model:finetune:cardiffnlp/twitter-roberta-large-2022-154m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-26T14:53:45Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
base_model: cardiffnlp/twitter-roberta-large-2022-154m
model-index:
- name: twitter-roberta-large-go-emotions
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. -->
# twitter-roberta-large-go-emotions
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-large-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-large-2022-154m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0816
- Accuracy: 0.4644
- Precision: 0.5709
- Recall: 0.5184
- F1: 0.5123
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 340 | 0.0889 | 0.4342 | 0.4653 | 0.4303 | 0.4243 |
| 0.1082 | 2.0 | 680 | 0.0819 | 0.4521 | 0.5253 | 0.4991 | 0.4856 |
| 0.1082 | 3.0 | 1020 | 0.0816 | 0.4644 | 0.5709 | 0.5184 | 0.5123 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.1
|
anhtranhong/fingpt-mt_llama2-7b_lora_with_fiqa-qa-v1.1
|
anhtranhong
| 2024-02-27T02:36:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T02:36:26Z |
---
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]
|
Asma50AA/wav2vec2-large-xls-r-300m-Lahdjatna-colab
|
Asma50AA
| 2024-02-27T02:35:07Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T02:35:06Z |
---
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]
|
Microbee/Ansost-Disease
|
Microbee
| 2024-02-27T02:31:20Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain",
"dataset:autotrain-lgrcd-2wctt/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-26T07:42:01Z |
---
tags:
- autotrain
- text-classification
widget:
- text: "Presenile dementia"
- text: "physiopathological"
datasets:
- autotrain-lgrcd-2wctt/autotrain-data
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.422727108001709
f1: 0.9172185430463576
precision: 0.9121844127332601
recall: 0.9223085460599334
auc: 0.9308079638847088
accuracy: 0.8777506112469438
|
miguelsolis/q-Taxi-v3
|
miguelsolis
| 2024-02-27T02:23:43Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-27T02:23:40Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="miguelsolis/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
bartowski/BioMistral-7B-exl2
|
bartowski
| 2024-02-27T02:22:11Z | 0 | 0 | null |
[
"medical",
"biology",
"text-generation",
"fr",
"en",
"de",
"nl",
"es",
"pt",
"pl",
"ro",
"it",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-02-27T02:07:20Z |
---
license: apache-2.0
language:
- fr
- en
- de
- nl
- es
- pt
- pl
- ro
- it
pipeline_tag: text-generation
tags:
- medical
- biology
quantized_by: bartowski
---
## Exllama v2 Quantizations of BioMistral-7B
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.14">turboderp's ExLlamaV2 v0.0.14</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/BioMistral/BioMistral-7B
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/BioMistral-7B-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/BioMistral-7B-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/BioMistral-7B-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/BioMistral-7B-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/BioMistral-7B-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/BioMistral-7B-exl2 BioMistral-7B-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `BioMistral-7B-exl2`:
```shell
mkdir BioMistral-7B-exl2
huggingface-cli download bartowski/BioMistral-7B-exl2 --local-dir BioMistral-7B-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir BioMistral-7B-exl2-6_5
huggingface-cli download bartowski/BioMistral-7B-exl2 --revision 6_5 --local-dir BioMistral-7B-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir BioMistral-7B-exl2-6.5
huggingface-cli download bartowski/BioMistral-7B-exl2 --revision 6_5 --local-dir BioMistral-7B-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
hoodiexxx/my_text_cnn_classification_model
|
hoodiexxx
| 2024-02-27T02:21:18Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2024-02-27T01:41:28Z |
---
license: mit
---
# Text CNN Classification Model
## Param
```python
textCNN_param = {
'vocab_size': len(word2ind) + 1,
'embed_dim': 128, # 1 x 128 vector
'class_num': len(label_w2n),
"kernel_num": 16,
"kernel_size": [3, 4, 5],
"dropout": 0.5,
}
dataLoader_param = {
'batch_size': 128,
'shuffle': True,
}
```
## Model
textcnn.bin
```python
class textCNN(nn.Module):
def __init__(self, param):
super(textCNN, self).__init__()
ci = 1 # input chanel size
# kernel 卷积核
kernel_num = param['kernel_num'] # output chanel size
kernel_size = param['kernel_size']
vocab_size = param['vocab_size']
embed_dim = param['embed_dim'] # embedding dimension
dropout = param['dropout']
class_num = param['class_num']
self.param = param
# 把token随机向量化
self.embed = nn.Embedding(vocab_size, embed_dim, padding_idx=1)
# 三个不同长度的卷积
self.conv11 = nn.Conv2d(ci, kernel_num, (kernel_size[0], embed_dim))
self.conv12 = nn.Conv2d(ci, kernel_num, (kernel_size[1], embed_dim))
self.conv13 = nn.Conv2d(ci, kernel_num, (kernel_size[2], embed_dim))
# 三个不同长度的卷积
# increasing the ability of calculation by dropout
self.dropout = nn.Dropout(dropout)
self.fc1 = nn.Linear(len(kernel_size) * kernel_num, class_num)
def init_embed(self, embed_matrix):
self.embed.weight = nn.Parameter(torch.Tensor(embed_matrix))
@staticmethod
def conv_and_pool(x, conv):
# x: (batch, 1, sentence_length, )
x = conv(x)
# x: (batch, kernel_num, H_out, 1)
x = F.relu(x.squeeze(3))
# x: (batch, kernel_num, H_out)
x = F.max_pool1d(x, x.size(2)).squeeze(2)
# (batch, kernel_num)
return x
def forward(self, x):
# x: (batch, sentence_length)
x = self.embed(x)
# x: (batch, sentence_length, embed_dim)
# TODO init embed matrix with pre-trained
x = x.unsqueeze(1)
# x: (batch, 1, sentence_length, embed_dim)
x1 = self.conv_and_pool(x, self.conv11) # (batch, kernel_num)
x2 = self.conv_and_pool(x, self.conv12) # (batch, kernel_num)
x3 = self.conv_and_pool(x, self.conv13) # (batch, kernel_num)
x = torch.cat((x1, x2, x3), 1) # (batch, 3 * kernel_num)
x = self.dropout(x)
logit = F.log_softmax(self.fc1(x), dim=1)
return logit
```
## Trainer
```python
# set the seed for ensuring reproducibility
seed = 3407
torch.cuda.manual_seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
word2ind, ind2word = sen2inds.get_worddict('wordLabel.txt')
label_w2n, label_n2w = sen2inds.read_labelFile('data/label.txt')
textCNN_param = {
'vocab_size': len(word2ind) + 1,
'embed_dim': 128, # 1 x 128 vector
'class_num': len(label_w2n),
"kernel_num": 16,
"kernel_size": [3, 4, 5],
"dropout": 0.5,
}
dataLoader_param = {
'batch_size': 128,
'shuffle': True,
}
# # device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
device = 'cpu'
# init dataset
print('init dataset...')
trainDataFile = 'traindata_vec.txt'
valDataFile = 'devdata_vec.txt'
train_dataset = textCNN_data(trainDataFile)
train_dataLoader = DataLoader(train_dataset,
batch_size=dataLoader_param['batch_size'],
shuffle=True)
val_dataset = textCNN_data(valDataFile)
val_dataLoader = DataLoader(val_dataset,
batch_size=dataLoader_param['batch_size'], # batch size 128
shuffle=False)
# init net
print('init net...')
net = textCNN(textCNN_param)
print(net)
net.to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
print("training...")
net.train()
best_dev_acc = 0
for epoch in range(100):
for i, (clas, sentences) in enumerate(train_dataLoader):
out = net(sentences)
loss = criterion(out, clas)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 10 == 0:
print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
dev_acc = validation(model=net, val_dataLoader=val_dataLoader,
device=device)
if best_dev_acc < dev_acc:
best_dev_acc = dev_acc
print("save model...")
torch.save(net.state_dict(), "textcnn.bin")
print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
print("best dev acc %.4f dev acc %.4f" % (best_dev_acc, dev_acc))
```
|
hoodiexxx/Bert_Chinese_Text_Classification_Model
|
hoodiexxx
| 2024-02-27T02:19:27Z | 0 | 0 | null |
[
"text-classification",
"zh",
"license:mit",
"region:us"
] |
text-classification
| 2024-02-26T15:21:18Z |
---
license: mit
language:
- zh
pipeline_tag: text-classification
---
# Bert Chinese Text Classification Model
this a Bert Model that train for customer service of logistics companies
### data(with noise since it from ASR text)
train: 10878 rows
dev:2720 rows
total: 13598 rows
### param
embed_dim: 128
batch size: 64
contextsize: 20
n_head: 2
epoches: 100
## Word Label(word, index, number of occurences)
```sh
我 1 18719
个 2 12236
快 3 8152
一 4 8097
递 5 7295
那 6 7118
了 7 6923
的 8 6684
是 9 6632
到 10 6434
你 11 5144
没 12 4989
有 13 4664
下 14 4433
这 15 4219
在 16 4219
么 17 4010
查 18 3964
就 19 3570
好 20 3524
```
## Tokenizer
```python
label_dict, label_n2w = read_labelFile(labelFile)
word2ind, ind2word = get_worddict(wordLabelFile)
stoplist = read_stopword(stopwordFile)
cla_dict = {}
# train data to vec
traindataTxt = open(trainDataVecFile, 'w')
datas = open(trainFile, 'r', encoding='utf_8').readlines()
datas = list(filter(None, datas))
random.shuffle(datas)
for line in tqdm(datas, desc="traindata to vec"):
line = line.replace('\n', '').split(':')
# line = line.replace('\n','').split('\t')
cla = line[1]
# if cla in [21, 13, 9, 24, 23, 19, 14]:
# continue
if cla in cla_dict:
cla_dict[cla] += 1
else:
cla_dict[cla] = 1
cla_ind = label_dict[cla]
title_seg = ['我', '要', '下', '单']
title_seg = [i for i in line[0]]
# title_seg = jieba.cut(line[0], cut_all=False)
title_ind = [cla_ind]
for w in title_seg:
if w in stoplist:
continue
title_ind.append(word2ind[w])
length = len(title_ind)
if length > maxLen + 1:
title_ind = title_ind[0:21]
if length < maxLen + 1:
title_ind.extend([0] * (maxLen - length + 1))
for n in title_ind:
traindataTxt.write(str(n) + ',')
traindataTxt.write('\n')
```
## Trainer
```python
# set the seed for ensuring reproducibility
seed = 3407
# init net
print('init net...')
model = my_model()
model.to(device)
print(model)
optimizer = torch.optim.Adam(model.parameters(), lr=0.0005)
criterion = nn.CrossEntropyLoss()
print("training...")
best_dev_acc = 0
# embed.train()
for epoch in range(100):
model.train()
for i, (clas, sentences) in enumerate(train_dataLoader):
# sentences: batch size 64 x sentence length 20 x embed dimension 128
# 一个字是个128维vector 一句话是个 20x128的2D tensor 一个batch有64句话是个 64x20x128的3D tensor
out = model(sentences.to(
device)) # out: batch size 64 x word vector 4 (after my_linear)
try:
loss = criterion(out, clas.to(device))
except:
print(out.size(), out)
print(clas.size(), clas)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 10 == 0:
print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
model.eval()
dev_acc = validation(model=model, val_dataLoader=val_dataLoader,
device=device)
if best_dev_acc < dev_acc:
best_dev_acc = dev_acc
print("save model...")
torch.save(model.state_dict(), "model.bin")
print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
print("best dev acc %.4f dev acc %.4f" % (best_dev_acc, dev_acc))
```
## Testing
```python
def validation(model, val_dataLoader, device):
model.eval()
total = 0
correct = 0
with torch.no_grad():
for i, (clas, sentences) in enumerate(val_dataLoader):
try:
# sentences = sentences.type(torch.LongTensor).to(device)
# clas = clas.type(torch.LongTensor).to(device)
out = model(
sentences.to(
device)) # out: batch size 64 x sentences length 20 x word dimension 4(after my_linear)
# out = F.relu(out.squeeze(-3))
# out = F.max_pool1d(out, out.size(2)).squeeze(2)
# softmax = nn.Softmax(dim=1)
pred = torch.argmax(out, dim=1) # 64x4 -> 64x1
correct += (pred == clas.to(device)).sum()
total += clas.size()[0]
except IndexError as e:
print(i)
print('clas', clas)
print('clas size', clas.size())
print('sentence', sentences)
print('sentences size', sentences.size())
print(e)
print(e.__traceback__)
exit()
acc = correct / total
return acc
```
|
sharren/vit-dropout-v8
|
sharren
| 2024-02-27T02:19:24Z | 194 | 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
| 2024-02-26T17:20:37Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-dropout-v8
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-dropout-v8
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 SkinCancerClassification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6544
- Accuracy: 0.8670
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
- dropout: 0.27
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.278 | 1.56 | 500 | 0.6873 | 0.8159 |
| 0.1863 | 3.12 | 1000 | 0.6260 | 0.8265 |
| 0.3125 | 4.67 | 1500 | 0.5167 | 0.8308 |
| 0.292 | 6.23 | 2000 | 0.5512 | 0.8221 |
| 0.24 | 7.79 | 2500 | 0.6563 | 0.8215 |
| 0.242 | 9.35 | 3000 | 0.5716 | 0.8633 |
| 0.1628 | 10.9 | 3500 | 0.5813 | 0.8670 |
| 0.0647 | 12.46 | 4000 | 0.6339 | 0.8670 |
| 0.0298 | 14.02 | 4500 | 0.6582 | 0.8683 |
| 0.0287 | 15.58 | 5000 | 0.6544 | 0.8670 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-08
|
alinerodrigues
| 2024-02-27T02:12:46Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-02-26T21:25:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-08
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. -->
# wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-08
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1396
- Wer: 0.0828
- Cer: 0.0228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 24.4898 | 1.0 | 67 | 6.6473 | 0.9805 | 0.9854 |
| 9.6476 | 2.0 | 134 | 5.1478 | 0.9775 | 0.9099 |
| 4.5647 | 3.0 | 201 | 4.9501 | 0.9510 | 0.9337 |
| 4.5647 | 4.0 | 268 | 4.7350 | 0.9821 | 0.9839 |
| 4.3107 | 5.0 | 335 | 4.7791 | 0.9818 | 0.9844 |
| 3.7636 | 6.0 | 402 | 4.6615 | 0.9834 | 0.9822 |
| 3.7636 | 7.0 | 469 | 4.4064 | 0.9854 | 0.9812 |
| 3.7891 | 8.0 | 536 | 4.1056 | 0.9868 | 0.9802 |
| 3.6656 | 9.0 | 603 | 3.1128 | 0.9983 | 0.9985 |
| 3.6656 | 10.0 | 670 | 2.9065 | 1.0 | 1.0 |
| 2.993 | 11.0 | 737 | 2.9184 | 1.0 | 1.0 |
| 2.8999 | 12.0 | 804 | 2.8963 | 1.0 | 1.0 |
| 2.8999 | 13.0 | 871 | 2.8663 | 1.0 | 1.0 |
| 2.879 | 14.0 | 938 | 2.7503 | 0.9977 | 0.9611 |
| 2.7677 | 15.0 | 1005 | 2.4108 | 1.0 | 0.9086 |
| 2.7677 | 16.0 | 1072 | 1.4958 | 1.0 | 0.5039 |
| 2.0951 | 17.0 | 1139 | 0.6763 | 0.6216 | 0.1321 |
| 1.0513 | 18.0 | 1206 | 0.4002 | 0.2890 | 0.0650 |
| 1.0513 | 19.0 | 1273 | 0.3120 | 0.1867 | 0.0468 |
| 0.6513 | 20.0 | 1340 | 0.2679 | 0.1718 | 0.0431 |
| 0.5148 | 21.0 | 1407 | 0.2419 | 0.1470 | 0.0391 |
| 0.5148 | 22.0 | 1474 | 0.2191 | 0.1261 | 0.0349 |
| 0.399 | 23.0 | 1541 | 0.1997 | 0.1225 | 0.0322 |
| 0.3708 | 24.0 | 1608 | 0.1961 | 0.1125 | 0.0313 |
| 0.3708 | 25.0 | 1675 | 0.1906 | 0.1102 | 0.0305 |
| 0.3342 | 26.0 | 1742 | 0.1841 | 0.1066 | 0.0303 |
| 0.3053 | 27.0 | 1809 | 0.1758 | 0.1039 | 0.0289 |
| 0.3053 | 28.0 | 1876 | 0.1696 | 0.0983 | 0.0270 |
| 0.2732 | 29.0 | 1943 | 0.1645 | 0.1033 | 0.0277 |
| 0.258 | 30.0 | 2010 | 0.1623 | 0.0953 | 0.0272 |
| 0.258 | 31.0 | 2077 | 0.1615 | 0.0943 | 0.0269 |
| 0.2489 | 32.0 | 2144 | 0.1591 | 0.0920 | 0.0262 |
| 0.2533 | 33.0 | 2211 | 0.1553 | 0.0920 | 0.0259 |
| 0.2533 | 34.0 | 2278 | 0.1603 | 0.0897 | 0.0255 |
| 0.2413 | 35.0 | 2345 | 0.1562 | 0.0910 | 0.0257 |
| 0.2377 | 36.0 | 2412 | 0.1544 | 0.0874 | 0.0252 |
| 0.2377 | 37.0 | 2479 | 0.1558 | 0.0884 | 0.0248 |
| 0.2189 | 38.0 | 2546 | 0.1520 | 0.0857 | 0.0243 |
| 0.2073 | 39.0 | 2613 | 0.1541 | 0.0857 | 0.0242 |
| 0.2073 | 40.0 | 2680 | 0.1495 | 0.0864 | 0.0244 |
| 0.204 | 41.0 | 2747 | 0.1497 | 0.0851 | 0.0243 |
| 0.198 | 42.0 | 2814 | 0.1516 | 0.0851 | 0.0247 |
| 0.198 | 43.0 | 2881 | 0.1498 | 0.0837 | 0.0238 |
| 0.1727 | 44.0 | 2948 | 0.1505 | 0.0894 | 0.0245 |
| 0.191 | 45.0 | 3015 | 0.1484 | 0.0844 | 0.0238 |
| 0.191 | 46.0 | 3082 | 0.1508 | 0.0847 | 0.0243 |
| 0.1922 | 47.0 | 3149 | 0.1494 | 0.0871 | 0.0243 |
| 0.1772 | 48.0 | 3216 | 0.1471 | 0.0904 | 0.0252 |
| 0.1772 | 49.0 | 3283 | 0.1477 | 0.0854 | 0.0242 |
| 0.17 | 50.0 | 3350 | 0.1463 | 0.0841 | 0.0239 |
| 0.1748 | 51.0 | 3417 | 0.1431 | 0.0841 | 0.0238 |
| 0.1748 | 52.0 | 3484 | 0.1454 | 0.0851 | 0.0232 |
| 0.1645 | 53.0 | 3551 | 0.1450 | 0.0884 | 0.0245 |
| 0.1808 | 54.0 | 3618 | 0.1425 | 0.0857 | 0.0233 |
| 0.1808 | 55.0 | 3685 | 0.1466 | 0.0828 | 0.0233 |
| 0.1712 | 56.0 | 3752 | 0.1442 | 0.0854 | 0.0235 |
| 0.1553 | 57.0 | 3819 | 0.1397 | 0.0841 | 0.0233 |
| 0.1553 | 58.0 | 3886 | 0.1418 | 0.0861 | 0.0241 |
| 0.1505 | 59.0 | 3953 | 0.1433 | 0.0831 | 0.0233 |
| 0.1609 | 60.0 | 4020 | 0.1439 | 0.0844 | 0.0234 |
| 0.1609 | 61.0 | 4087 | 0.1438 | 0.0837 | 0.0233 |
| 0.1521 | 62.0 | 4154 | 0.1433 | 0.0857 | 0.0237 |
| 0.1541 | 63.0 | 4221 | 0.1410 | 0.0861 | 0.0236 |
| 0.1541 | 64.0 | 4288 | 0.1396 | 0.0828 | 0.0228 |
| 0.1464 | 65.0 | 4355 | 0.1404 | 0.0824 | 0.0226 |
| 0.1489 | 66.0 | 4422 | 0.1423 | 0.0837 | 0.0232 |
| 0.1489 | 67.0 | 4489 | 0.1426 | 0.0847 | 0.0230 |
| 0.1513 | 68.0 | 4556 | 0.1407 | 0.0828 | 0.0228 |
| 0.1655 | 69.0 | 4623 | 0.1441 | 0.0877 | 0.0234 |
| 0.1655 | 70.0 | 4690 | 0.1446 | 0.0900 | 0.0241 |
| 0.1337 | 71.0 | 4757 | 0.1432 | 0.0844 | 0.0228 |
| 0.1471 | 72.0 | 4824 | 0.1456 | 0.0811 | 0.0225 |
| 0.1471 | 73.0 | 4891 | 0.1426 | 0.0781 | 0.0225 |
| 0.1527 | 74.0 | 4958 | 0.1438 | 0.0824 | 0.0227 |
| 0.1276 | 75.0 | 5025 | 0.1440 | 0.0811 | 0.0223 |
| 0.1276 | 76.0 | 5092 | 0.1431 | 0.0804 | 0.0225 |
| 0.1393 | 77.0 | 5159 | 0.1460 | 0.0834 | 0.0232 |
| 0.132 | 78.0 | 5226 | 0.1434 | 0.0811 | 0.0226 |
| 0.132 | 79.0 | 5293 | 0.1464 | 0.0808 | 0.0227 |
| 0.1326 | 80.0 | 5360 | 0.1444 | 0.0791 | 0.0222 |
| 0.1318 | 81.0 | 5427 | 0.1463 | 0.0788 | 0.0223 |
| 0.1318 | 82.0 | 5494 | 0.1448 | 0.0811 | 0.0227 |
| 0.1281 | 83.0 | 5561 | 0.1452 | 0.0811 | 0.0224 |
| 0.1294 | 84.0 | 5628 | 0.1420 | 0.0811 | 0.0225 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3
|
kaljr/ppo-cleanRL-LunarLander-v2
|
kaljr
| 2024-02-27T02:07:21Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-27T02:07:17Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -177.92 +/- 89.11
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'fff': '/root/.local/share/jupyter/runtime/kernel-d3563d3a-fcb2-4f06-b5c4-b7d025c7a55d.json'
'exp_name': 'tempname'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 256
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'kaljr/ppo-cleanRL-LunarLander-v2'
'batch_size': 1024
'minibatch_size': 256}
```
|
zayjean/llama-2-13b_verify-bo-lora-r8-a32-d0_14K-E20_GA128
|
zayjean
| 2024-02-27T01:59:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T01:58:52Z |
---
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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[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]
|
kyone/clubbed_finetuned_model
|
kyone
| 2024-02-27T01:56:06Z | 51 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo",
"text-generation",
"custom_code",
"en",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-02-27T01:30:55Z |
---
language:
- en
license: apache-2.0
library_name: transformers
---
# 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]
|
AlignmentResearch/robust_llm_pythia-imdb-1b-mz-ada-v2
|
AlignmentResearch
| 2024-02-27T01:52:00Z | 105 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b-deduped",
"base_model:finetune:EleutherAI/pythia-1b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T01:50:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1b-deduped
model-index:
- name: robust_llm_pythia-imdb-1b-mz-ada-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. -->
# robust_llm_pythia-imdb-1b-mz-ada-v2
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
Emperor-WS/sac-HalfCheetahBulletEnv-v0
|
Emperor-WS
| 2024-02-27T01:40:21Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"HalfCheetahBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-27T01:39:21Z |
---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetahBulletEnv-v0
type: HalfCheetahBulletEnv-v0
metrics:
- type: mean_reward
value: 3038.56 +/- 42.27
name: mean_reward
verified: false
---
# **SAC** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **SAC** agent playing **HalfCheetahBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo sac --env HalfCheetahBulletEnv-v0 -orga Emperor-WS -f logs/
python -m rl_zoo3.enjoy --algo sac --env HalfCheetahBulletEnv-v0 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo sac --env HalfCheetahBulletEnv-v0 -orga Emperor-WS -f logs/
python -m rl_zoo3.enjoy --algo sac --env HalfCheetahBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env HalfCheetahBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env HalfCheetahBulletEnv-v0 -f logs/ -orga Emperor-WS
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('buffer_size', 300000),
('ent_coef', 'auto'),
('gamma', 0.98),
('gradient_steps', 8),
('learning_rate', 0.00073),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(log_std_init=-3, net_arch=[400, 300])'),
('tau', 0.02),
('train_freq', 8),
('use_sde', True),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
justinlamlamlam/testing
|
justinlamlamlam
| 2024-02-27T01:39:39Z | 166 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T01:39:14Z |
---
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]
|
zayjean/llama-2-13b_draft-bo-batch-require-grad0-lora-r8-a32-d0_3K-E2
|
zayjean
| 2024-02-27T01:33:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T01:32:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
usail-hkust/LLMLight-LightGPT
|
usail-hkust
| 2024-02-27T01:32:45Z | 10 | 15 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"hkust-gz",
"llama-2",
"traffic signal control",
"lightgpt",
"llmlight",
"en",
"arxiv:2312.16044",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-02-09T07:24:05Z |
---
extra_gated_heading: Access LLMLight-LightGPT on Hugging Face
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- hkust-gz
- pytorch
- llama-2
- traffic signal control
- lightgpt
- llmlight
license: mit
---
# LLMLight: Large Language Models as Traffic Signal Control Agents
<p align="center">
| **[1 Introduction](#introduction)**
| **[2 Framework](#framework)**
| **[3 Demo](#demo)**
| **[Github](https://github.com/usail-hkust/LLMTSCS)**
| **[Website](https://gungnir2099.github.io/LLMLight-Page/)** |
</p>
<a id="introduction"></a>
## 1 Introduction
Model weights trained in the article "[LLMLight: Large Language Models as Traffic Signal Control Agents](https://arxiv.org/abs/2312.16044)".
Please download the model and run LLMLight by following the descriptions in the [Repository](https://github.com/usail-hkust/LLMTSCS).
<a id="framework"></a>
## 2 Framework
<a id="demo"></a>
## 3 Demo
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64207ca7e40f66bcd1e44959/VhMOdEIjKeLml1WPcINqV.qt"></video>
|
lgodwangl/gemma2
|
lgodwangl
| 2024-02-27T01:32:32Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T01:28:36Z |
---
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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### Model Sources [optional]
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- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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### Downstream Use [optional]
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[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
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### 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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
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[More Information Needed]
## Environmental Impact
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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).
- **Hardware Type:** [More Information Needed]
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## Model Card Contact
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|
vaicai/kaifa-support-chat-v7.3
|
vaicai
| 2024-02-27T01:28:46Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T01:23:09Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
justinlamlamlam/gpt350_chat_s_v0_1
|
justinlamlamlam
| 2024-02-27T01:27:40Z | 166 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T01:27:16Z |
---
library_name: transformers
tags:
- trl
- sft
---
# 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
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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]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
EricValen/ppo-SnowballTarget
|
EricValen
| 2024-02-27T01:25:21Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-02-27T01:25:14Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: EricValen/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
vaicai/kaifa-support-chat-adapters-v7.3
|
vaicai
| 2024-02-27T01:22:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T01:22:22Z |
---
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.
- **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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- **Demo [optional]:** [More Information Needed]
## Uses
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### 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]
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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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|
OwOpeepeepoopoo/project_america3
|
OwOpeepeepoopoo
| 2024-02-27T01:08:35Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T01:08:32Z |
---
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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- **Demo [optional]:** [More Information Needed]
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[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
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#### 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
tempertrash/cat_LoRA
|
tempertrash
| 2024-02-27T01:05:18Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-02-26T20:08:04Z |
---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
- text-to-image
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
- text-to-image
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK cat
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 - tempertrash/cat_LoRA
<Gallery />
## Model description
These are tempertrash/cat_LoRA 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 TOK cat to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](tempertrash/cat_LoRA/tree/main) them in the Files & versions tab.
## Training details
Trained on 25 images of my cat Boo.
|
Xavi-Hdz/q-Taxi-v3
|
Xavi-Hdz
| 2024-02-27T01:01:56Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-27T01:01:53Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Xavi-Hdz/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
bartowski/hyperion-medium-preview-exl2
|
bartowski
| 2024-02-27T00:48:28Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T00:33:35Z |
---
library_name: transformers
license: apache-2.0
language:
- en
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of hyperion-medium-preview
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.14">turboderp's ExLlamaV2 v0.0.14</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/Locutusque/hyperion-medium-preview
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/hyperion-medium-preview-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/hyperion-medium-preview-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/hyperion-medium-preview-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/hyperion-medium-preview-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/hyperion-medium-preview-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/hyperion-medium-preview-exl2 hyperion-medium-preview-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `hyperion-medium-preview-exl2`:
```shell
mkdir hyperion-medium-preview-exl2
huggingface-cli download bartowski/hyperion-medium-preview-exl2 --local-dir hyperion-medium-preview-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir hyperion-medium-preview-exl2-6_5
huggingface-cli download bartowski/hyperion-medium-preview-exl2 --revision 6_5 --local-dir hyperion-medium-preview-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir hyperion-medium-preview-exl2-6.5
huggingface-cli download bartowski/hyperion-medium-preview-exl2 --revision 6_5 --local-dir hyperion-medium-preview-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
cella110n/siglip-tagger-3-FT-1
|
cella110n
| 2024-02-27T00:47:16Z | 105 | 1 |
transformers
|
[
"transformers",
"safetensors",
"siglip_vision_model",
"image-classification",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-02-26T12:59:38Z |
---
license: apache-2.0
---
Finetuned from p1atdev/siglip-tagger-test-3
https://huggingface.co/p1atdev/siglip-tagger-test-3
test work
Usage:
```
import torch
import torch.nn as nn
import numpy as np
from dataclasses import dataclass
from transformers import SiglipVisionModel, SiglipPreTrainedModel, SiglipVisionConfig, AutoImageProcessor
from transformers.utils import ModelOutput
@dataclass
class SiglipForImageClassifierOutput(ModelOutput):
loss: torch.FloatTensor | None = None
logits: torch.FloatTensor | None = None
pooler_output: torch.FloatTensor | None = None
hidden_states: tuple[torch.FloatTensor, ...] | None = None
attentions: tuple[torch.FloatTensor, ...] | None = None
class SiglipForImageClassification(SiglipPreTrainedModel):
config_class = SiglipVisionConfig
main_input_name = "pixel_values"
def __init__(
self,
config,
):
super().__init__(config)
# self.num_labels = config.num_labels
self.siglip = SiglipVisionModel(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_size, config.num_labels)
if config.num_labels > 0
else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
def forward(
self, pixel_values: torch.FloatTensor, labels: torch.LongTensor | None = None
):
outputs = self.siglip(pixel_values)
pooler_output = outputs.pooler_output
logits = self.classifier(pooler_output)
loss = None
if labels is not None:
loss_fct = nn.BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
return SiglipForImageClassifierOutput(
loss=loss,
logits=logits,
pooler_output=outputs.pooler_output,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# モデル設定のロード
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = SiglipVisionConfig.from_pretrained('cella110n/siglip-tagger-FT3ep')
processor = AutoImageProcessor.from_pretrained("cella110n/siglip-tagger-FT3ep", config=config)
model = SiglipForImageClassification.from_pretrained('cella110n/siglip-tagger-FT3ep', torch_dtype=torch.bfloat16).to(device)
model.eval()
print("Model Loaded. device:", model.device)
from PIL import Image
# 入力画像サイズの確認と調整
img_path = "path/to/image"
img = Image.open(img_path).
inputs = processor(images=img, return_tensors="pt") # 画像をモデルに適した形式に変換
print("Image processed.")
# inputs.pixel_valuesの画像を表示
img = inputs.pixel_values[0].permute(1, 2, 0).cpu().numpy()
plt.imshow(img)
plt.axis('off')
plt.show()
# # モデルの予測実行
with torch.no_grad():
logits = (model(
**inputs.to(
model.device,
model.dtype
)
)
.logits.detach()
.cpu()
.float()
)
logits = np.clip(logits, 0.0, 1.0) # オーバーフローを防ぐためにlogitsをクリップ
prob_cutoff = 0.3 # この確率以上のクラスのみを表示
result = {}
for prediction in logits:
for i, prob in enumerate(prediction):
if prob.item() > prob_cutoff:
result[model.config.id2label[i]] = prob.item()
# resultを、高いほうから表示
sorted_result = sorted(result.items(), key=lambda x: x[1], reverse=True)
sorted_result
```
|
dranger003/Senku-70B-iMat.GGUF
|
dranger003
| 2024-02-27T00:32:06Z | 125 | 28 |
gguf
|
[
"gguf",
"text-generation",
"license:cc-by-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-02-07T23:17:59Z |
---
license: cc-by-2.0
library_name: gguf
pipeline_tag: text-generation
---
* GGUF importance matrix (imatrix) quants for https://huggingface.co/ShinojiResearch/Senku-70B-Full
* The importance matrix was trained for ~50K tokens (105 batches of 512 tokens) using a [general purpose imatrix calibration dataset](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384).
* The [imatrix is being used on the K-quants](https://github.com/ggerganov/llama.cpp/pull/4930) as well.
**2024-02-26**: Updating quants - IQ3_M/IQ3_S/IQ3_XS and IQ2_M/IQ2_S (requires latest commit [a33e6a0d](https://github.com/ggerganov/llama.cpp/commit/a33e6a0d2a66104ea9a906bdbf8a94d050189d91)).
| Layers | Context | Template |
| --- | --- | --- |
| <pre>80</pre> | <pre>32764</pre> | <pre><\|im_start\|>system<br>{instructions}<\|im_end\|><br><\|im_start\|>user<br>{prompt}<\|im_end\|><br><\|im_start\|>assistant<br>{response}</pre> |
|
anonplay99/luffy
|
anonplay99
| 2024-02-27T00:30:49Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2024-02-27T00:29:58Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
ahmedelsayed/v1-bloom-1b1-sql-context
|
ahmedelsayed
| 2024-02-27T00:17:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T00:17:05Z |
---
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]
|
lgodwangl/gemma1
|
lgodwangl
| 2024-02-26T23:50:31Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-26T23:46:39Z |
---
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
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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]
- **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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## Model Card Contact
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|
ahmedabdo/video-classifier
|
ahmedabdo
| 2024-02-26T23:46:43Z | 64 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2024-02-26T23:42:09Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **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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[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
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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]
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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]
#### Hardware
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## Citation [optional]
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|
ryusangwon/2767_Llama-2-7b-hf
|
ryusangwon
| 2024-02-26T23:29:35Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"dataset:samsum",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2024-02-26T23:29:31Z |
---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: 2767_Llama-2-7b-hf
results: []
library_name: peft
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 2767_Llama-2-7b-hf
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.4.0
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
dar-tau/toy-autoencoder-2L-1L
|
dar-tau
| 2024-02-26T23:27:12Z | 37 | 0 |
transformers
|
[
"transformers",
"safetensors",
"toy_autoencoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-26T23:27:08Z |
---
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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Direct Use
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[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
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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]
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- **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]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
finetuningsubnet/gemma-2b-it
|
finetuningsubnet
| 2024-02-26T23:25:46Z | 109 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-26T23:23:34Z |
---
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]
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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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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
SeeonQwQ/blip2_frame_v2.5
|
SeeonQwQ
| 2024-02-26T23:25:34Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Salesforce/blip2-opt-2.7b",
"base_model:adapter:Salesforce/blip2-opt-2.7b",
"region:us"
] | null | 2024-02-20T23:35:49Z |
---
library_name: peft
base_model: Salesforce/blip2-opt-2.7b
---
# 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]
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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. -->
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[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]
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### Framework versions
- PEFT 0.8.2
|
tonyassi/mugler-fw97-fashion-lora
|
tonyassi
| 2024-02-26T23:23:16Z | 214 | 2 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-02-26T20:28:38Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Mugler FW97 style
license: openrail++
---
# SDXL LoRA DreamBooth - tonyassi/mugler-fw97-fashion-lora
by [Tony Assi](https://www.tonyassi.com/)
Dreambooth Lora style based on the [Mugler FW97](https://www.vogue.com/fashion-shows/fall-1997-couture/mugler) collection.
## Trigger words
Use **Mugler FW97 style** in the prompt to trigger the style.
## How to use
```bash
pip install diffusers accelerate
```
```python
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
# Load the pipeline
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
vae=vae,
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
pipe.load_lora_weights("tonyassi/mugler-fw97-fashion-lora")
pipe.to("cuda")
# Generate image
prompt = "Mugler FW97 style, megan fox wearing a gold mesh dress with crystals"
image = pipe(prompt=prompt,
height=1024,
width=1024,
num_inference_steps=50,
negative_prompt="ugly, deformed face, deformed body").images[0]
image
```
## Examples
**Mugler FW97 style, Bettie Page holding a whip**
**Mugler FW97 style, Spock, Star Trek**
**Mugler FW97 style, John Travolta**
**Mugler FW97 style, Bettie Page**
**Mugler FW97 style, Emma Stone**
**Mugler FW97 style, Emma Stone**
## Model description
These are tonyassi/mugler-fw97-fashion-lora 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.
## Download model
Weights for this model are available in Safetensors format.
[Download](https://huggingface.co/tonyassi/mugler-fw97-fashion-lora/tree/main) them in the Files & versions tab.
|
panchub/backward_model
|
panchub
| 2024-02-26T23:20:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-15T02:39:44Z |
---
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]
<!-- 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. -->
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[More Information Needed]
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## Glossary [optional]
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[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
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|
brittlewis12/aanaphi2-v0.1-GGUF
|
brittlewis12
| 2024-02-26T23:18:24Z | 28 | 1 | null |
[
"gguf",
"text-generation",
"en",
"base_model:mobiuslabsgmbh/aanaphi2-v0.1",
"base_model:quantized:mobiuslabsgmbh/aanaphi2-v0.1",
"license:mit",
"region:us",
"conversational"
] |
text-generation
| 2024-02-26T22:49:38Z |
---
base_model: mobiuslabsgmbh/aanaphi2-v0.1
license: mit
train: false
inference: false
language:
- en
model_creator: mobiuslabsgmbh
model_name: aanaphi2-v0.1
model_type: phi
pipeline_tag: text-generation
quantized_by: brittlewis12
---
# aanaphi2-v0.1 GGUF
**Original model**: [aanaphi2-v0.1](https://huggingface.co/mobiuslabsgmbh/aanaphi2-v0.1)
**Model creator**: [mobiuslabsgmbh](https://huggingface.co/mobiuslabsgmbh)
This repo contains GGUF format model files for Mobius Labs’ aanaphi2-v0.1.
> aanaphi2-v0.1 is a finetuned (SFT + DPO) chat model based on [Microsoft's Phi-2 base model](https://huggingface.co/microsoft/phi-2) (2.8B parameters).
### What is GGUF?
GGUF is a file format for representing AI models. It is the third version of the 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.
Converted using llama.cpp build 2276 (revision [b11a93d](https://github.com/ggerganov/llama.cpp/commit/b11a93df41921846a10628a7c306d5c82a549939))
### Prompt template
```
### Human: {{prompt}}
### Assistant:
```
---
## Download & run with [cnvrs](https://twitter.com/cnvrsai) on iPhone, iPad, and Mac!
[cnvrs](https://testflight.apple.com/join/sFWReS7K) is the best app for private, local AI on your device:
- create & save **Characters** with custom system prompts & temperature settings
- download and experiment with any **GGUF model** you can [find on HuggingFace](https://huggingface.co/models?library=gguf)!
- make it your own with custom **Theme colors**
- powered by Metal ⚡️ & [Llama.cpp](https://github.com/ggerganov/llama.cpp), with **haptics** during response streaming!
- **try it out** yourself today, on [Testflight](https://testflight.apple.com/join/sFWReS7K)!
- follow [cnvrs on twitter](https://twitter.com/cnvrsai) to stay up to date
---
## Original Model Evaluation
| Models | phi-2 | aanaphi2-v0.1 |
|-------------------|------------------|------------------|
| ARC (25-shot) | 61.09 | <b>63.74</b> |
| HellaSwag (10-shot)| 75.11 | <b>78.30</b> |
| MMLU (5-shot) | <b>58.11</b> | 57.70 |
| TruthfulQA-MC2 | 44.47 | <b>51.56</b> |
| Winogrande (5-shot)| <b>74.35</b> | 73.40 |
| GSM8K (5-shot) | 54.81 | <b>58.61</b> |
| Average | 61.33 | <b>63.89</b> |
|
suledev/kaifa-support-chat-adapters-v7.2
|
suledev
| 2024-02-26T23:13:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-26T23:07:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
stablediffusionapi/vr-porn
|
stablediffusionapi
| 2024-02-26T23:06:16Z | 39 | 1 |
diffusers
|
[
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-26T23:04:04Z |
---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# VR Porn API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "vr-porn"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/vr-porn)
Model link: [View model](https://modelslab.com/models/vr-porn)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "vr-porn",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
sik247/gemma-Code-Instruct-Finetune-test
|
sik247
| 2024-02-26T22:47:00Z | 109 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-26T22:40:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**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]
|
zaid60/tuned_model
|
zaid60
| 2024-02-26T22:45:50Z | 43 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-26T21:13:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
u66u/Reyna-Mini-1.8B-v0.2-Qwen1.5-1.8B-Merged-Slerp
|
u66u
| 2024-02-26T22:38:46Z | 110 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"aloobun/Reyna-Mini-1.8B-v0.2",
"Qwen/Qwen1.5-1.8B",
"conversational",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:merge:Qwen/Qwen1.5-1.8B",
"base_model:aloobun/Reyna-Mini-1.8B-v0.2",
"base_model:merge:aloobun/Reyna-Mini-1.8B-v0.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-26T22:35:13Z |
---
tags:
- merge
- mergekit
- lazymergekit
- aloobun/Reyna-Mini-1.8B-v0.2
- Qwen/Qwen1.5-1.8B
base_model:
- aloobun/Reyna-Mini-1.8B-v0.2
- Qwen/Qwen1.5-1.8B
---
# Reyna-Mini-1.8B-v0.2-Qwen1.5-1.8B-Merged-Slerp
Reyna-Mini-1.8B-v0.2-Qwen1.5-1.8B-Merged-Slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [aloobun/Reyna-Mini-1.8B-v0.2](https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.2)
* [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: aloobun/Reyna-Mini-1.8B-v0.2
layer_range: [0, 23]
- model: Qwen/Qwen1.5-1.8B
layer_range: [0, 23]
merge_method: slerp
base_model: aloobun/Reyna-Mini-1.8B-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "u66u/Reyna-Mini-1.8B-v0.2-Qwen1.5-1.8B-Merged-Slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
adriata/med_mistral_4bit
|
adriata
| 2024-02-26T22:24:55Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"conversational",
"dataset:pubmed",
"dataset:bigbio/czi_drsm",
"dataset:bigbio/bc5cdr",
"dataset:bigbio/distemist",
"dataset:pubmed_qa",
"dataset:medmcqa",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-02-25T21:01:51Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- sft
datasets:
- pubmed
- bigbio/czi_drsm
- bigbio/bc5cdr
- bigbio/distemist
- pubmed_qa
- medmcqa
---
# Model Card for med_mistral_4bit
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Model 4-bit Mistral-7B-Instruct-v0.2 finetuned with QLoRA on multiple medical datasets.
16-bit version: [med_mistral](https://huggingface.co/adriata/med_mistral)
- **License:** apache-2.0
- **Finetuned from model :** mistralai/Mistral-7B-Instruct-v0.2
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/atadria/med_llm
## 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. -->
The model is finetuned on medical data and is intended only for research. It should not be used as a substitute for professional medical advice, diagnosis, or treatment.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model's predictions are based on the information available in the finetuned medical dataset. It may not generalize well to all medical conditions or diverse patient populations.
Sensitivity to variations in input data and potential biases present in the training data may impact the model's performance.
### 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.
```python
# !pip install -q transformers accelerate bitsandbytes
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("adriata/med_mistral")
model = AutoModelForCausalLM.from_pretrained("adriata/med_mistral")
prompt_template = """<s>[INST] {prompt} [/INST]"""
prompt = "What is influenza?"
model_inputs = tokenizer.encode(prompt_template.format(prompt=prompt),
return_tensors="pt").to("cuda")
generated_ids = model.generate(model_inputs, max_new_tokens=512, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Training Details
~13h - 20k examples x 1 epoch
GPU: OVH - 1 × NVIDIA TESLA V100S (32 GiB RAM)
### 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. -->
Training data included 20k examples randomly selected from datasets:
- pubmed
- bigbio/czi_drsm
- bigbio/bc5cdr
- bigbio/distemist
- pubmed_qa
- medmcqa
|
mikolaj-mialkowski/Reinforce-3l-16
|
mikolaj-mialkowski
| 2024-02-26T22:20:28Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-26T22:20:13Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-3l-16
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
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
|
alekom/ppo-Huggy
|
alekom
| 2024-02-26T22:18:55Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2024-02-26T22:17:41Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: alekom/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Venkman42/Phiter-GGUF
|
Venkman42
| 2024-02-26T22:10:23Z | 9 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-02-26T21:28:27Z |
GGUF for [Venkman42/Phiter](https://huggingface.co/Venkman42/Phiter)
|
sujitvasanth/TheBloke-openchat-3.5-0106-GPTQ-PEFTadapterJsonSear
|
sujitvasanth
| 2024-02-26T22:08:44Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:sujitvasanth/TheBloke-openchat-3.5-0106-GPTQ",
"base_model:adapter:sujitvasanth/TheBloke-openchat-3.5-0106-GPTQ",
"region:us"
] | null | 2024-02-26T21:51:33Z |
---
library_name: peft
base_model: sujitvasanth/TheBloke-openchat-3.5-0106-GPTQ
---
# Model Card for Model ID
<!-- Finetuned version of openchat for extracting information from a database json object. -->
## Model Details
### Model Description
<!-- Finetuned version of openchat for extracting information from a database json object. It is train -->
- **Developed by:** Dr Sujit Vasanth
- **Model type:** QLoRA PEFT
- **Language(s) (NLP):** Json, English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** TheBloke/openchat-3.5-0106-GPTQ
### Model Sources [optional]
- **Repository:** https://github.com/sujitvasanth/GPTQ-finetune
- **Demo [optional]:** https://github.com/sujitvasanth/GPTQ-finetune/blob/main/GPTQ-finetune.py
## How to Get Started with the Model
model = AutoModelForCausalLM.from_pretrained(model_id,
quantization_config= GPTQConfig(bits=4, disable_exllama=False),device_map="auto") # is_trainable=True
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
model.load_adapter(adapter_id)
## Training Details
### Training Data
<!-- https://huggingface.co/datasets/sujitvasanth/jsonsearch2 -->
https://huggingface.co/datasets/sujitvasanth/jsonsearch2
User: Assistant examples of Json search Query
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
QLora PEFT training on custom dataset
#### 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
#### 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 0.8.2
|
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