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gilson0156/lotto | gilson0156 | 2024-06-20T08:58:08Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-19T13:30:17Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: lotto
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. -->
# lotto
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3929
- Accuracy: 0.1383
- Precision: 0.1383
- Recall: 0.1383
- F1: 0.1383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4559 | 1.0 | 18 | 0.4474 | 0.1583 | 0.1583 | 0.1583 | 0.1583 |
| 0.4029 | 2.0 | 36 | 0.3972 | 0.1333 | 0.1333 | 0.1333 | 0.1333 |
| 0.3953 | 3.0 | 54 | 0.3924 | 0.135 | 0.135 | 0.135 | 0.135 |
| 0.3956 | 4.0 | 72 | 0.3926 | 0.1483 | 0.1483 | 0.1483 | 0.1483 |
| 0.3983 | 5.0 | 90 | 0.3933 | 0.1417 | 0.1417 | 0.1417 | 0.1417 |
| 0.3924 | 6.0 | 108 | 0.3926 | 0.1367 | 0.1367 | 0.1367 | 0.1367 |
| 0.3917 | 7.0 | 126 | 0.3926 | 0.1417 | 0.1417 | 0.1417 | 0.1417 |
| 0.3923 | 8.0 | 144 | 0.3924 | 0.1483 | 0.1483 | 0.1483 | 0.1483 |
| 0.3965 | 9.0 | 162 | 0.3929 | 0.1350 | 0.135 | 0.135 | 0.135 |
| 0.3939 | 10.0 | 180 | 0.3929 | 0.1383 | 0.1383 | 0.1383 | 0.1383 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.2
- Datasets 2.20.0
- Tokenizers 0.19.1
|
seamoke111/HTL-CodeLlama-7B | seamoke111 | 2024-06-20T08:55:48Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:TIGER-Lab/MathInstruct",
"arxiv:2402.15729",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-19T03:24:19Z | ---
datasets:
- TIGER-Lab/MathInstruct
language:
- en
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-generation
---
# How Do Humans Write Code? Large Models Do It the Same Way Too
Paper: [https://arxiv.org/pdf/2402.15729](https://arxiv.org/pdf/2402.15729)
Code: [https://github.com/seamoke/Human-Think-Language](https://github.com/seamoke/Human-Think-Language)
## Introduction
For this model, please sure your transformers>=4.39.2.
We introduce HTL, a model which utilizes the complete reasoning process of CoT to enhance PoT. This model was secondarily fine-tuned based on [MAmmoTH-Coder-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B)
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **GSM** |**GSM-Hard** | **NumGLUE** | **MATH** | **Sim** | **SVAMP** | **MAWPS** | **ASDiV** |
|---------------------------| ----------|---------------|---------------|-----------|----------|---------- |------------|---------------|
| **MAmmoTH-Coder-7B** | 59.4 |56.3 | 66.4 |33.4| 45.9 | 70.7 | 91.9 | 69.3 |
| **TORA** | **72.6** |56.0 | 46.2 |**44.6**| 48.5 | 70.4 | 91.3 | **78.7** |
| **MAmmoTH-Coder-7B** | 65.7 |**58.3** | **75.1** |34.9| **50.8** | **74.4** | **94.2** | 73.1 |
## Prompt Format
If you want to do HTL:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
I'd like you to solve this problem in 3 steps:
1.Answer the question in plain language without writing any code.\n
2.Output one line of *\n.
3.Write program code based on the solution process in step 1 to solve the problem.\n
### Instruction:
{query}
Let's write a program.
### Response:"
```
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{li2024humans,
title={How Do Humans Write Code? Large Models Do It the Same Way Too},
author={Li, Long},
journal={arXiv preprint arXiv:2402.15729},
year={2024}
}
``` |
mustang12/unity_ml | mustang12 | 2024-06-20T08:49:55Z | 15 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2024-06-20T08:47:02Z | ---
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: mustang12/unity_ml
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
MaziyarPanahi/firefunction-v2-GGUF | MaziyarPanahi | 2024-06-20T08:48:24Z | 950,057 | 16 | transformers | [
"transformers",
"gguf",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"conversational",
"function-calling",
"text-generation-inference",
"region:us",
"base_model:fireworks-ai/llama-3-firefunction-v2",
"base_model:quantized:fireworks-ai/llama-3-firefunction-v2",
"license:llama3",
"imatrix"
]
| text-generation | 2024-06-19T12:47:26Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- text-generation
- conversational
- function-calling
- text-generation-inference
- region:us
- text-generation
model_name: MaziyarPanahi/firefunction-v2-GGUF
base_model: fireworks-ai/firefunction-v2
inference: false
model_creator: fireworks-ai
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
license: llama3
---
# [MaziyarPanahi/firefunction-v2-GGUF](https://huggingface.co/MaziyarPanahi/firefunction-v2-GGUF)
- Model creator: [fireworks-ai](https://huggingface.co/fireworks-ai)
- Original model: [fireworks-ai/firefunction-v2](https://huggingface.co/fireworks-ai/firefunction-v2)
## Description
[MaziyarPanahi/firefunction-v2-GGUF](https://huggingface.co/MaziyarPanahi/firefunction-v2-GGUF) contains GGUF format model files for [fireworks-ai/firefunction-v2](https://huggingface.co/fireworks-ai/firefunction-v2).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
Original README
---
# FireFunction V2: Fireworks Function Calling Model
[**Try on Fireworks**](https://fireworks.ai/models/fireworks/firefunction-v2) | [**API Docs**](https://readme.fireworks.ai/docs/function-calling) | [**Demo App**](https://functional-chat.vercel.app/) | [**Discord**](https://discord.gg/mMqQxvFD9A)
<img src="https://cdn-uploads.huggingface.co/production/uploads/64b6f3a72f5a966b9722de88/nJNtxLzWswBDKK1iOZblb.png" alt="firefunction" width="400"/>
FireFunction is a state-of-the-art function calling model with a commercially viable license. View detailed info in our [announcement blog](https://fireworks.ai/blog/firefunction-v2-launch-post). Key info and highlights:
**Comparison with other models:**
- Competitive with GPT-4o at function-calling, scoring 0.81 vs 0.80 on a medley of public evaluations
- Trained on Llama 3 and retains Llama 3’s conversation and instruction-following capabilities, scoring 0.84 vs Llama 3’s 0.89 on MT bench
- Significant quality improvements over FireFunction v1 across the broad range of metrics
**General info:**
🐾 Successor of the [FireFunction](https://fireworks.ai/models/fireworks/firefunction-v1) model
🔆 Support of parallel function calling (unlike FireFunction v1) and good instruction following
💡 Hosted on the [Fireworks](https://fireworks.ai/models/fireworks/firefunction-v2) platform at < 10% of the cost of GPT 4o and 2x the speed
|
1231czx/2b_dpo_iter1_1250step | 1231czx | 2024-06-20T08:44:06Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T08:42:06Z | ---
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] |
juleo2323/distilbert-base-uncased-finetuned-emotion | juleo2323 | 2024-06-20T08:41:53Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T06:07:17Z | ---
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: 0.9215
- name: F1
type: f1
value: 0.9213810900686746
---
<!-- 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: 0.2168
- Accuracy: 0.9215
- F1: 0.9214
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8179 | 1.0 | 250 | 0.3113 | 0.91 | 0.9091 |
| 0.2498 | 2.0 | 500 | 0.2168 | 0.9215 | 0.9214 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.3.0
- Datasets 2.20.0
- Tokenizers 0.13.3
|
1231czx/2b_dpo_iter1_750step | 1231czx | 2024-06-20T08:37:33Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T08:34:55Z | ---
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] |
John6666/lyuyang-mix-vp-v198-sdxl | John6666 | 2024-06-20T08:32:38Z | 1,554 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"pony",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| text-to-image | 2024-06-20T08:28:03Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- pony
---
Original model is [here](https://civitai.com/models/458504?modelVersionId=585064).
|
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-100-percent-med-high-nv-embed | AdamKasumovic | 2024-06-20T08:25:15Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T08:22:59Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
erizakaria/llama3-8b-cosmic-fusion-dynamics-lora | erizakaria | 2024-06-20T08:23:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-20T03:50:37Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** erizakaria
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nadika/medical_jargons_simplifier2 | nadika | 2024-06-20T08:16:46Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:luqh/ClinicalT5-base",
"base_model:finetune:luqh/ClinicalT5-base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-06-20T07:34:20Z | ---
base_model: luqh/ClinicalT5-base
tags:
- generated_from_trainer
model-index:
- name: medical_jargons_simplifier2
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. -->
# medical_jargons_simplifier2
This model is a fine-tuned version of [luqh/ClinicalT5-base](https://huggingface.co/luqh/ClinicalT5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4641
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.6338 | 0.3378 | 50 | 5.9582 |
| 3.6156 | 0.6757 | 100 | 1.0741 |
| 1.3304 | 1.0135 | 150 | 0.8368 |
| 1.0096 | 1.3514 | 200 | 0.7519 |
| 0.933 | 1.6892 | 250 | 0.7019 |
| 0.8178 | 2.0270 | 300 | 0.6586 |
| 0.7714 | 2.3649 | 350 | 0.6188 |
| 0.7077 | 2.7027 | 400 | 0.5924 |
| 0.7406 | 3.0405 | 450 | 0.5673 |
| 0.6601 | 3.3784 | 500 | 0.5531 |
| 0.6637 | 3.7162 | 550 | 0.5388 |
| 0.6489 | 4.0541 | 600 | 0.5281 |
| 0.6369 | 4.3919 | 650 | 0.5187 |
| 0.5996 | 4.7297 | 700 | 0.5109 |
| 0.5816 | 5.0676 | 750 | 0.5028 |
| 0.5714 | 5.4054 | 800 | 0.4961 |
| 0.5826 | 5.7432 | 850 | 0.4910 |
| 0.5646 | 6.0811 | 900 | 0.4855 |
| 0.5379 | 6.4189 | 950 | 0.4827 |
| 0.5586 | 6.7568 | 1000 | 0.4785 |
| 0.5408 | 7.0946 | 1050 | 0.4751 |
| 0.5576 | 7.4324 | 1100 | 0.4727 |
| 0.5241 | 7.7703 | 1150 | 0.4710 |
| 0.5298 | 8.1081 | 1200 | 0.4695 |
| 0.5424 | 8.4459 | 1250 | 0.4677 |
| 0.5038 | 8.7838 | 1300 | 0.4665 |
| 0.5545 | 9.1216 | 1350 | 0.4653 |
| 0.523 | 9.4595 | 1400 | 0.4644 |
| 0.5029 | 9.7973 | 1450 | 0.4641 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
varun-v-rao/gpt2-bn-adapter-895K-squad-model3 | varun-v-rao | 2024-06-20T08:11:02Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"region:us"
]
| null | 2024-06-20T07:07:17Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- varun-v-rao/squad
model-index:
- name: gpt2-bn-adapter-895K-squad-model3
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. -->
# gpt2-bn-adapter-895K-squad-model3
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 87
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
RyuKT/DefSentPlus-bert-large-uncased | RyuKT | 2024-06-20T08:10:45Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2405.16153",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2024-06-20T08:07:23Z | BibTeX
@misc{liu2024defsent, title={DefSent+: Improving sentence embeddings of language models by projecting definition sentences into a quasi-isotropic or isotropic vector space of unlimited dictionary entries}, author={Xiaodong Liu}, year={2024}, eprint={2405.16153}, archivePrefix={arXiv} } |
RyuKT/DefSentPlus-sncse-bert-base-uncased | RyuKT | 2024-06-20T08:02:39Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2405.16153",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2024-06-20T08:01:27Z | BibTeX
@misc{liu2024defsent, title={DefSent+: Improving sentence embeddings of language models by projecting definition sentences into a quasi-isotropic or isotropic vector space of unlimited dictionary entries}, author={Xiaodong Liu}, year={2024}, eprint={2405.16153}, archivePrefix={arXiv} } |
Klevin/J.A.R.V.I.S-v2.0-Q4_K_M-GGUF | Klevin | 2024-06-20T08:00:40Z | 1 | 2 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Klevin/J.A.R.V.I.S-v2.0",
"base_model:quantized:Klevin/J.A.R.V.I.S-v2.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-06-20T08:00:19Z | ---
base_model: Klevin/J.A.R.V.I.S-v2.0
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
- llama-cpp
- gguf-my-repo
---
# Klevin/J.A.R.V.I.S-v2.0-Q4_K_M-GGUF
This model was converted to GGUF format from [`Klevin/J.A.R.V.I.S-v2.0`](https://huggingface.co/Klevin/J.A.R.V.I.S-v2.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Klevin/J.A.R.V.I.S-v2.0) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Klevin/J.A.R.V.I.S-v2.0-Q4_K_M-GGUF --hf-file j.a.r.v.i.s-v2.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Klevin/J.A.R.V.I.S-v2.0-Q4_K_M-GGUF --hf-file j.a.r.v.i.s-v2.0-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Klevin/J.A.R.V.I.S-v2.0-Q4_K_M-GGUF --hf-file j.a.r.v.i.s-v2.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Klevin/J.A.R.V.I.S-v2.0-Q4_K_M-GGUF --hf-file j.a.r.v.i.s-v2.0-q4_k_m.gguf -c 2048
```
|
xX-FANE-Xx/RAFT-mistral-v1-merged-Q5_K_M-GGUF | xX-FANE-Xx | 2024-06-20T07:57:55Z | 2 | 1 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:miha-kac/RAFT-mistral-v1-merged",
"base_model:quantized:miha-kac/RAFT-mistral-v1-merged",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-20T07:57:34Z | ---
base_model: miha-kac/RAFT-mistral-v1-merged
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# xX-FANE-Xx/RAFT-mistral-v1-merged-Q5_K_M-GGUF
This model was converted to GGUF format from [`miha-kac/RAFT-mistral-v1-merged`](https://huggingface.co/miha-kac/RAFT-mistral-v1-merged) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/miha-kac/RAFT-mistral-v1-merged) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo xX-FANE-Xx/RAFT-mistral-v1-merged-Q5_K_M-GGUF --hf-file raft-mistral-v1-merged-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo xX-FANE-Xx/RAFT-mistral-v1-merged-Q5_K_M-GGUF --hf-file raft-mistral-v1-merged-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo xX-FANE-Xx/RAFT-mistral-v1-merged-Q5_K_M-GGUF --hf-file raft-mistral-v1-merged-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo xX-FANE-Xx/RAFT-mistral-v1-merged-Q5_K_M-GGUF --hf-file raft-mistral-v1-merged-q5_k_m.gguf -c 2048
```
|
davidyu2023/Qwen-Qwen1.5-1.8B-1718870267 | davidyu2023 | 2024-06-20T07:57:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
]
| null | 2024-06-20T07:57:47Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
pavanavn/vit-base-patch16-224-9models | pavanavn | 2024-06-20T07:56:09Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2024-06-20T07:38:37Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-9models
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-9models
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0167
- Accuracy: 0.9959
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5952 | 0.9790 | 35 | 0.2206 | 0.9344 |
| 0.1228 | 1.9860 | 71 | 0.0889 | 0.9754 |
| 0.1133 | 2.9930 | 107 | 0.0701 | 0.9816 |
| 0.0877 | 4.0 | 143 | 0.0808 | 0.9754 |
| 0.0597 | 4.9790 | 178 | 0.0234 | 0.9939 |
| 0.0718 | 5.9860 | 214 | 0.0325 | 0.9898 |
| 0.0666 | 6.9930 | 250 | 0.0459 | 0.9836 |
| 0.0467 | 8.0 | 286 | 0.0162 | 0.9959 |
| 0.0446 | 8.9790 | 321 | 0.0155 | 0.9959 |
| 0.0391 | 9.7902 | 350 | 0.0167 | 0.9959 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
achamajames/openai-whisper-small-colab | achamajames | 2024-06-20T07:54:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-20T07:54:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
chahn85/Llama-3-8B-OPTN | chahn85 | 2024-06-20T07:53:26Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct",
"base_model:finetune:unsloth/llama-3-8b-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T07:44:13Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct
---
# Uploaded model
- **Developed by:** chahn85
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
qassim227/Auto-pharmacy-V5 | qassim227 | 2024-06-20T07:53:08Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"image-to-text",
"base_model:microsoft/trocr-small-stage1",
"base_model:finetune:microsoft/trocr-small-stage1",
"endpoints_compatible",
"region:us"
]
| image-to-text | 2024-06-19T21:29:43Z | ---
base_model: microsoft/trocr-small-stage1
tags:
- generated_from_trainer
model-index:
- name: Auto-pharmacy-V5
results: []
pipeline_tag: image-to-text
---
<!-- 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. -->
# Auto-pharmacy-V5
This model is a fine-tuned version of [microsoft/trocr-small-stage1](https://huggingface.co/microsoft/trocr-small-stage1) 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: 16
- eval_batch_size: 16
- 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: 15
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1 |
svjack/Qwen2-1_5B_Function_Call_tiny_lora | svjack | 2024-06-20T07:51:23Z | 7 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:adapter:Qwen/Qwen2-7B-Instruct",
"license:other",
"region:us"
]
| null | 2024-06-17T14:31:57Z | ---
base_model: Qwen/Qwen2-7B-Instruct
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: train_2024-06-17-19-49-05
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. -->
# Install some dependency
```bash
pip install peft transformers bitsandbytes
```
# Inference
```python
import json
import re
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any, Dict, List, Literal, Optional, Sequence, Set, Tuple, Union
def calculate_gpa(grades: Sequence[str], hours: Sequence[int]) -> float:
grade_to_score = {"A": 4, "B": 3, "C": 2}
total_score, total_hour = 0, 0
for grade, hour in zip(grades, hours):
total_score += grade_to_score[grade] * hour
total_hour += hour
return round(total_score / total_hour, 2)
tool_map = {"calculate_gpa": calculate_gpa}
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-1.5B-Instruct",
torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, "svjack/Qwen2-1_5B_Function_Call_tiny_lora")
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
DEFAULT_TOOL_PROMPT = (
"You have access to the following tools:\n{tool_text}"
"Use the following format if using a tool:\n"
"```\n"
"Action: tool name (one of [{tool_names}]).\n"
"Action Input: the input to the tool, in a JSON format representing the kwargs "
"""(e.g. ```{{"input": "hello world", "num_beams": 5}}```).\n"""
"```\n"
)
def default_tool_formatter(tools: List[Dict[str, Any]]) -> str:
tool_text = ""
tool_names = []
for tool in tools:
param_text = ""
for name, param in tool["parameters"]["properties"].items():
required = ", required" if name in tool["parameters"].get("required", []) else ""
enum = ", should be one of [{}]".format(", ".join(param["enum"])) if param.get("enum", None) else ""
items = (
", where each item should be {}".format(param["items"].get("type", "")) if param.get("items") else ""
)
param_text += " - {name} ({type}{required}): {desc}{enum}{items}\n".format(
name=name,
type=param.get("type", ""),
required=required,
desc=param.get("description", ""),
enum=enum,
items=items,
)
tool_text += "> Tool Name: {name}\nTool Description: {desc}\nTool Args:\n{args}\n".format(
name=tool["name"], desc=tool.get("description", ""), args=param_text
)
tool_names.append(tool["name"])
return DEFAULT_TOOL_PROMPT.format(tool_text=tool_text, tool_names=", ".join(tool_names))
def default_tool_extractor(content: str) -> Union[str, List[Tuple[str, str]]]:
regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+)\s*Action Input:\s*(.+?)(?=\s*Action:|\s*$)", re.DOTALL)
action_match: List[Tuple[str, str]] = re.findall(regex, content)
if not action_match:
return content
results = []
for match in action_match:
tool_name = match[0].strip()
tool_input = match[1].strip().strip('"').strip("```")
try:
arguments = json.loads(tool_input)
results.append((tool_name, json.dumps(arguments, ensure_ascii=False)))
except json.JSONDecodeError:
return content
return results
#### Function tool defination
tools = [
{
"type": "function",
"function": {
"name": "calculate_gpa",
"description": "Calculate the Grade Point Average (GPA) based on grades and credit hours",
"parameters": {
"type": "object",
"properties": {
"grades": {"type": "array", "items": {"type": "string"}, "description": "The grades"},
"hours": {"type": "array", "items": {"type": "integer"}, "description": "The credit hours"},
},
"required": ["grades", "hours"],
},
},
}
]
tools_input = list(map(lambda x: x["function"], tools))
system_tool_prompt = default_tool_formatter(tools_input)
#print(system_tool_prompt)
def qwen_hf_predict(messages, qw_model = model,
tokenizer = tokenizer, streamer = streamer,
do_sample = True,
top_p = 0.95,
top_k = 40,
max_new_tokens = 512,
max_input_length = 3500,
temperature = 0.9,
repetition_penalty = 1.0,
device = "cuda"):
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt",
add_generation_prompt=True
)
model_inputs = encodeds.to(device)
generated_ids = qw_model.generate(model_inputs, max_new_tokens=max_new_tokens,
do_sample=do_sample,
streamer = streamer,
top_p = top_p,
top_k = top_k,
temperature = temperature,
repetition_penalty = repetition_penalty,
)
out = tokenizer.batch_decode(generated_ids)[0].split("<|im_start|>assistant")[-1].replace("<|im_end|>", "").strip()
return out
messages = [
{
"role" :"system",
"content": system_tool_prompt
},
{"role": "user", "content": "My grades are A, A, B, and C. The credit hours are 3, 4, 3, and 2."}
]
out = qwen_hf_predict(messages)
tool_out = default_tool_extractor(out)
print(tool_out)
name, arguments = tool_out[0][0], json.loads(tool_out[0][1])
tool_result = tool_map[name](**arguments)
print(tool_result)
messages.append(
{
"role" :"assistant",
"content": out
}
)
messages.append({"role": "tool", "content": json.dumps({"gpa": tool_result}, ensure_ascii=False)})
final_out = qwen_hf_predict(messages)
print(final_out)
```
# Output
```
Action: calculate_gpa
Action Input: {"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}
[('calculate_gpa', '{"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}')]
3.42
Your calculated GPA is 3.42.
```
# Inference
```python
messages = [
{
"role" :"system",
"content": system_tool_prompt
},
{"role": "user", "content": "我的成绩分别是A,A,B,C学分分别是3, 4, 3,和2"}
]
out = qwen_hf_predict(messages)
tool_out = default_tool_extractor(out)
print(tool_out)
name, arguments = tool_out[0][0], json.loads(tool_out[0][1])
tool_result = tool_map[name](**arguments)
print(tool_result)
messages.append(
{
"role" :"assistant",
"content": out
}
)
messages.append({"role": "tool", "content": json.dumps({"gpa": tool_result}, ensure_ascii=False)})
final_out = qwen_hf_predict(messages)
print(final_out)
```
# Output
```
Action: calculate_gpa
Action Input: {"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}
[('calculate_gpa', '{"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}')]
3.42
你的GPA是3.42。
```
# train_2024-06-17-19-49-05
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the glaive_toolcall_zh and the glaive_toolcall_en datasets.
## 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
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
Bilal-Mamji/llama-3-8b-chat-doctor | Bilal-Mamji | 2024-06-20T07:47:22Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-19T22:52:56Z | ---
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] |
svjack/Qwen2-7B_Function_Call_tiny_lora | svjack | 2024-06-20T07:47:11Z | 4 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:adapter:Qwen/Qwen2-7B-Instruct",
"license:other",
"region:us"
]
| null | 2024-06-17T14:35:41Z | ---
base_model: Qwen/Qwen2-7B-Instruct
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: train_2024-06-17-19-49-05
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. -->
# Install some dependency
```bash
pip install peft transformers bitsandbytes
```
# Inference
```python
import json
import re
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any, Dict, List, Literal, Optional, Sequence, Set, Tuple, Union
def calculate_gpa(grades: Sequence[str], hours: Sequence[int]) -> float:
grade_to_score = {"A": 4, "B": 3, "C": 2}
total_score, total_hour = 0, 0
for grade, hour in zip(grades, hours):
total_score += grade_to_score[grade] * hour
total_hour += hour
return round(total_score / total_hour, 2)
tool_map = {"calculate_gpa": calculate_gpa}
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-7B-Instruct",
torch_dtype="auto", device_map="auto", load_in_8bit = True)
model = PeftModel.from_pretrained(model, "svjack/Qwen2-7B_Function_Call_tiny_lora")
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
DEFAULT_TOOL_PROMPT = (
"You have access to the following tools:\n{tool_text}"
"Use the following format if using a tool:\n"
"```\n"
"Action: tool name (one of [{tool_names}]).\n"
"Action Input: the input to the tool, in a JSON format representing the kwargs "
"""(e.g. ```{{"input": "hello world", "num_beams": 5}}```).\n"""
"```\n"
)
def default_tool_formatter(tools: List[Dict[str, Any]]) -> str:
tool_text = ""
tool_names = []
for tool in tools:
param_text = ""
for name, param in tool["parameters"]["properties"].items():
required = ", required" if name in tool["parameters"].get("required", []) else ""
enum = ", should be one of [{}]".format(", ".join(param["enum"])) if param.get("enum", None) else ""
items = (
", where each item should be {}".format(param["items"].get("type", "")) if param.get("items") else ""
)
param_text += " - {name} ({type}{required}): {desc}{enum}{items}\n".format(
name=name,
type=param.get("type", ""),
required=required,
desc=param.get("description", ""),
enum=enum,
items=items,
)
tool_text += "> Tool Name: {name}\nTool Description: {desc}\nTool Args:\n{args}\n".format(
name=tool["name"], desc=tool.get("description", ""), args=param_text
)
tool_names.append(tool["name"])
return DEFAULT_TOOL_PROMPT.format(tool_text=tool_text, tool_names=", ".join(tool_names))
def default_tool_extractor(content: str) -> Union[str, List[Tuple[str, str]]]:
regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+)\s*Action Input:\s*(.+?)(?=\s*Action:|\s*$)", re.DOTALL)
action_match: List[Tuple[str, str]] = re.findall(regex, content)
if not action_match:
return content
results = []
for match in action_match:
tool_name = match[0].strip()
tool_input = match[1].strip().strip('"').strip("```")
try:
arguments = json.loads(tool_input)
results.append((tool_name, json.dumps(arguments, ensure_ascii=False)))
except json.JSONDecodeError:
return content
return results
#### Function tool defination
tools = [
{
"type": "function",
"function": {
"name": "calculate_gpa",
"description": "Calculate the Grade Point Average (GPA) based on grades and credit hours",
"parameters": {
"type": "object",
"properties": {
"grades": {"type": "array", "items": {"type": "string"}, "description": "The grades"},
"hours": {"type": "array", "items": {"type": "integer"}, "description": "The credit hours"},
},
"required": ["grades", "hours"],
},
},
}
]
tools_input = list(map(lambda x: x["function"], tools))
system_tool_prompt = default_tool_formatter(tools_input)
#print(system_tool_prompt)
def qwen_hf_predict(messages, qw_model = model,
tokenizer = tokenizer, streamer = streamer,
do_sample = True,
top_p = 0.95,
top_k = 40,
max_new_tokens = 512,
max_input_length = 3500,
temperature = 0.9,
repetition_penalty = 1.0,
device = "cuda"):
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt",
add_generation_prompt=True
)
model_inputs = encodeds.to(device)
generated_ids = qw_model.generate(model_inputs, max_new_tokens=max_new_tokens,
do_sample=do_sample,
streamer = streamer,
top_p = top_p,
top_k = top_k,
temperature = temperature,
repetition_penalty = repetition_penalty,
)
out = tokenizer.batch_decode(generated_ids)[0].split("<|im_start|>assistant")[-1].replace("<|im_end|>", "").strip()
return out
messages = [
{
"role" :"system",
"content": system_tool_prompt
},
{"role": "user", "content": "My grades are A, A, B, and C. The credit hours are 3, 4, 3, and 2."}
]
out = qwen_hf_predict(messages)
tool_out = default_tool_extractor(out)
print(tool_out)
name, arguments = tool_out[0][0], json.loads(tool_out[0][1])
tool_result = tool_map[name](**arguments)
print(tool_result)
messages.append(
{
"role" :"assistant",
"content": out
}
)
messages.append({"role": "tool", "content": json.dumps({"gpa": tool_result}, ensure_ascii=False)})
final_out = qwen_hf_predict(messages)
print(final_out)
```
# Output
```
Action: calculate_gpa
Action Input: {"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}
[('calculate_gpa', '{"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}')]
3.42
Based on the grades and credit hours you provided, your Grade Point Average (GPA) is 3.42.
```
# Inference
```python
messages = [
{
"role" :"system",
"content": system_tool_prompt
},
{"role": "user", "content": "我的成绩分别是A,A,B,C学分分别是3, 4, 3,和2"}
]
out = qwen_hf_predict(messages)
tool_out = default_tool_extractor(out)
print(tool_out)
name, arguments = tool_out[0][0], json.loads(tool_out[0][1])
tool_result = tool_map[name](**arguments)
print(tool_result)
messages.append(
{
"role" :"assistant",
"content": out
}
)
messages.append({"role": "tool", "content": json.dumps({"gpa": tool_result}, ensure_ascii=False)})
final_out = qwen_hf_predict(messages)
print(final_out)
```
# Output
```
Action: calculate_gpa
Action Input: {"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}
[('calculate_gpa', '{"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}')]
3.42
您的绩点（GPA）是3.42。
```
# train_2024-06-17-19-49-05
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the glaive_toolcall_zh and the glaive_toolcall_en datasets.
## 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
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
srirag/bi-mntp-gemma-ind-adaptor | srirag | 2024-06-20T07:44:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-20T06:19:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
svjack/DPO_Genshin_Impact_Inst_ORPO_Qwen1_5_7B_Chat_lora_small | svjack | 2024-06-20T07:44:32Z | 4 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-7B-Chat",
"base_model:adapter:Qwen/Qwen1.5-7B-Chat",
"license:other",
"region:us"
]
| null | 2024-05-18T12:26:37Z | ---
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: Qwen/Qwen1.5-7B-Chat
model-index:
- name: train_2024-05-18-08-31-08
results: []
---
# 🤭 Please refer to https://github.com/svjack/Genshin-Impact-Character-Instruction to get more info
# Install
```bash
pip install peft transformers bitsandbytes
```
# Run by transformers
* Trained on single round instructions of Genshin Impact
```python
from transformers import TextStreamer, AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-7B-Chat",)
qw_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-7B-Chat", load_in_8bit = True)
qw_model = PeftModel.from_pretrained(qw_model, "svjack/DPO_Genshin_Impact_Inst_ORPO_Qwen1_5_7B_Chat_lora_small")
qw_model = qw_model.eval()
streamer = TextStreamer(tokenizer)
def qwen_hf_predict(messages, qw_model = qw_model,
tokenizer = tokenizer, streamer = streamer,
do_sample = True,
top_p = 0.95,
top_k = 40,
max_new_tokens = 512,
max_input_length = 3500,
temperature = 0.9,
repetition_penalty = 1.0,
device = "cuda"):
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt",
add_generation_prompt=True
)
model_inputs = encodeds.to(device)
generated_ids = qw_model.generate(model_inputs, max_new_tokens=max_new_tokens,
do_sample=do_sample,
streamer = streamer,
top_p = top_p,
top_k = top_k,
temperature = temperature,
repetition_penalty = repetition_penalty,
)
out = tokenizer.batch_decode(generated_ids)[0].split("<|im_start|>assistant")[-1].replace("<|im_end|>", "").strip()
return out
out = qwen_hf_predict([
{
"role": "user",
"content": '''
下面是柯莱的一些基本信息
性别:少女女性
国籍:须弥
身份:化城郭见习巡林员
性格特征:善解人意，乐于助人
这些是一段角色介绍
「乐于助人」、「阳光善良」、「热情洋溢」⋯在化城郭内外稍加了解，就能听到人们对这位见习巡林员的称赞。
只要身体允许，无论学业如何繁忙，柯莱都不会怠慢巡林工作，更不吝于向各色行人伸出饱含热情的援手。
只是如此热诚积极的柯莱，似乎也有着不愿为人所知的过往与心事。
假如在她经常巡逻的林间，发现贴满奇怪字条的树洞，或是类似碎碎念的声响。
无论看到听到了什么，还请善解人意地绕道而行，权当作兰那罗开的小小玩笑。
毕竟有些琐事，是只能说与树洞听的一一至少目前还是。
柯莱如何评价巡林员的工作？
'''
}
],
repetition_penalty = 1.0,
temperature = 0.01,
max_new_tokens=1024
)
print(out)
```
# Output
```
我热爱巡林员的工作，热爱大自然，热爱生活。
```
* Has limited chat capabilities
```python
out = qwen_hf_predict([
{
"role": "user",
"content": '''
下面是云堇的一些基本信息
性别:少女女性
国籍:璃月
身份:和裕茶馆、云翰社当家花旦
性格特征:痴迷戏腔
这些是一段角色介绍
「和裕茶馆」历来是璃月人工作之余的一大好去处。
和裕茶馆的生意之所以如此兴隆，一是老板范二爷经营得当，请的茶博士说起书来是一绝。
二是璃月知名的戏社「云翰社」正挂靠在此。云翰社如今的当家兼顶梁柱一名角云堇，有时会来登台开唱。
美味的小吃也好，说书人的故事也好，只要去对地方，随时都能享受。唯独听云堇唱戏的机会，实在不常有。
所以，云堇的戏迷们常常守在和裕茶馆，谈论云堇演唱过的戏，交流各自赏戏的体会。
茶馆里多了不少常客，十个里九个是云堇的戏迷。
范二爷对此很是满意。
一天旅行者到茶馆听戏。
云堇，你听说过荻花洲的传说吗？
'''
},
{
"role": "assistant",
"content": "传说中，荻花洲的芦苇丛中，藏着一位仙人。她用芦苇编织出的乐器，吹奏出的曲调，令人陶醉。"
},
{
"role": "user",
"content": "谈谈你对这个传说的看法。"
},
{
"role": "assistant",
"content": "我倒是觉得，芦苇编成的乐器…唔…听起来有点奇怪呢。"
},
{
"role": "user",
"content": "戏班中有哪些丝竹？"
}
],
repetition_penalty = 1.1,
temperature = 0.01,
max_new_tokens=1024
)
print(out)
```
# Output
```
琴、筝、琵琶、笛子、锣鼓…嗯，还有笙。。
```
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_2024-05-18-08-31-08
This model is a fine-tuned version of [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) on the dpo_genshin_impact 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: cosine
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
openvoid/Prox-Llama-3-8B-abliterated | openvoid | 2024-06-20T07:43:43Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"cybersecurity",
"penetration testing",
"hacking",
"uncensored",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-09T22:46:00Z | ---
license: apache-2.0
language:
- en
tags:
- code
- cybersecurity
- penetration testing
- hacking
- code
- uncensored
---
# Prox-Llama-3-8B-abliterated
By [OpenVoid](https://openvoid.ai)
<img src="https://cdn.openvoid.ai/images/prox-llama3.png" width="400" />
## Model Description
Prox-Llama-3-8B is a uncensored fine-tune of Meta-Llama-3-8B-Instruct, tailored for specialized applications in code generation and cybersecurity.
## Intended Uses & Limitations
Designed for tasks related to hacking and coding:
- Code generation
- Code explanation and documentation
- Answering questions on hacking techniques and cybersecurity
- Providing coding project insights
Review and verify outputs carefully, especially for critical applications. Expert validation is recommended to avoid biased or inconsistent content. Use responsibly and ethically, complying with applicable laws and regulations to prevent misuse for malicious purposes.
## Training Data
The model was fine-tuned on a proprietary dataset from OpenVoid, featuring high-quality text data related to coding, cybersecurity, and hacking. Extensive filtering and preprocessing ensured data quality and relevance.
## How to Use the Model
### Using Transformers
Example of using Prox-Llama-3-8B with the Transformers library:
```python
import transformers
import torch
model_id = "openvoid/Prox-Llama-3-8B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are Prox."},
{"role": "user", "content": "Who are you?"},
]
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][-1])
```
|
kyukyuswe/t5-small-finetuned-xsum | kyukyuswe | 2024-06-20T07:38:57Z | 14 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-06-20T03:55:13Z | ---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.2959
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4781
- Rouge1: 28.2959
- Rouge2: 7.7364
- Rougel: 22.2437
- Rougelsum: 22.2447
- Gen Len: 18.8252
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7071 | 1.0 | 12753 | 2.4781 | 28.2959 | 7.7364 | 22.2437 | 22.2447 | 18.8252 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
NaxGyumi/Reinforce1 | NaxGyumi | 2024-06-20T07:35:59Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-06-20T07:35:49Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce1
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
|
raicrits/BERT_ChangeOfTopic | raicrits | 2024-06-20T07:34:55Z | 0 | 0 | transformers | [
"transformers",
"LLM",
"Italian",
"Classification",
"BERT",
"Topics",
"text-classification",
"it",
"dataset:raicrits/YouTube_RAI_dataset",
"arxiv:1910.09700",
"license:other",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-04T12:46:10Z | ---
license: other
datasets:
- raicrits/YouTube_RAI_dataset
language:
- it
pipeline_tag: text-classification
tags:
- LLM
- Italian
- Classification
- BERT
- Topics
library_name: transformers
---
---
# Model Card raicrits/BERT_ChangeOfTopic
<!-- Provide a quick summary of what the model is/does. -->
[bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) finetuned to be capable of detecting
a change of topic in a given text.
### Model Description
The model is finetuned for the specific task of detecting a change of topic in a given text. Given a text the model answers with "1" in the case that it detects a change of topic and "0" otherwise.
The training has been done using the chapters in the Youtube videos contained in the train split of the dataset [raicrits/YouTube_RAI_dataset](https://huggingface.co/meta-llama/raicrits/YouTube_RAI_dataset).
- **Developed by:** Stefano Scotta ([email protected])
- **Model type:** LLM finetuned on the specific task of detect a change of topic in a given text
- **Language(s) (NLP):** Italian
- **License:** unknown
- **Finetuned from model [optional]:** [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
## Uses
The model can be used to check if in a given text occurs a change of topic or not.
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## How to Get Started with the Model
Use the code below to get started with the model.
**Usage:**
Use the code below to get started with the model.
``` python
import torch
from transformers import AutoTokenizer, BertForSequenceClassification, BertTokenizer, AutoModelForCausalLM, pipeline
model_bert = torch.load('raicrits/BERT_ChangeOfTopic')
model_bert = model_bert.to(device_bert)
tokenizer_bert = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')
encoded_dict = tokenizer_bert.encode_plus(
'<text>',
add_special_tokens = True,
max_length = 256,
# max_length = min(max_len, 512),
truncation = True,
padding='max_length',
return_attention_mask = True,
return_tensors = 'pt',
)
input_ids = encoded_dict['input_ids'].to(device_bert)
input_mask = encoded_dict['attention_mask'].to(device_bert)
with torch.no_grad():
output= model_bert(input_ids,
token_type_ids=None,
attention_mask=input_mask)
logits = output.logits
logits = logits.detach().cpu().numpy()
pred_flat = np.argmax(logits, axis=1).flatten()
print(pred_flat[0])
```
## Training Details
### Training Data
Chapters in the Youtube videos contained in the train split of the dataset [raicrits/YouTube_RAI_dataset](https://huggingface.co/meta-llama/raicrits/YouTube_RAI_dataset)
### Training Procedure
**Training setting:**
- train epochs=18,
- learning_rate=2e-05
## 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:** 1 NVIDIA A100/40Gb
- **Hours used:** 20
- **Cloud Provider:** Private Infrastructure
- **Carbon Emitted:** 2.38kg eq. CO2
## Model Card Authors
Stefano Scotta ([email protected])
## Model Card Contact
[email protected] |
raicrits/Llama3_ChangeOfTopic | raicrits | 2024-06-20T07:34:23Z | 0 | 0 | transformers, peft | [
"transformers, peft",
"safetensors",
"LLM",
"Italian",
"LoRa",
"Classification",
"LLama3",
"Topics",
"text2text-generation",
"it",
"dataset:raicrits/YouTube_RAI_dataset",
"arxiv:2106.09685",
"arxiv:1910.09700",
"license:other",
"region:us"
]
| text2text-generation | 2024-06-04T12:04:16Z | ---
license: other
datasets:
- raicrits/YouTube_RAI_dataset
language:
- it
pipeline_tag: text2text-generation
tags:
- LLM
- Italian
- LoRa
- Classification
- LLama3
- Topics
library_name: transformers, peft
---
---
# Model Card raicrits/Llama3_ChangeOfTopic
<!-- Provide a quick summary of what the model is/does. -->
LoRa adapters for [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) obtained through a finetuning process (using LoRA technique) aimed at making the model capable of detecting
a change of topic in a given text.
### Model Description
The model resulting from the application of the adapters in this repository to the base model [meta-llama/MMeta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) is optimized to perform the
specific task of detecting a change of topic in a given text. Given a text the model answers with "1" in the case that it detects a change of topic and "0" otherwise.
The training has been done using the chapters in the Youtube videos contained in the train split of the dataset [raicrits/YouTube_RAI_dataset](https://huggingface.co/meta-llama/raicrits/YouTube_RAI_dataset).
Because of the finetuning process it is important to respect the prompt template in order to get good results.
- **Developed by:** Stefano Scotta ([email protected])
- **Model type:** LLM finetuned on the specific task of detect a change of topic in a given text
- **Language(s) (NLP):** Italian
- **License:** unknown
- **Finetuned from model [optional]:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
## Uses
The model can be used to check if in a given text occurs a change of topic or not.
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Bias, Risks, and Limitations
As any other LLM it is possible that the model generates content which does not correspond to the reality as well as wrong, biased, offensive and inappropriate answers.
## How to Get Started with the Model
Use the code below to get started with the model.
**Usage:**
Use the code below to get started with the model.
``` python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
model_id = "meta-llama/Meta-Llama-3-8B"
lora_id = "raicrits/Llama3_ChangeOfTopic"
quantization_config = BitsAndBytesConfig(
load_in_8bit=True)
base_model = AutoModelForCausalLM.from_pretrained(model_id,
quantization_config=quantization_config,
device_map=device)
model = PeftModel.from_pretrained(base_model, lora_id)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
messages = [
{"role": "system", "content": "You are an AI assistant able to detect change of topics in given texts."},
{"role": "user", "content": f"""Analyze the following text written in italian and in case you detect a change of topic answer just with "1", otherwise, if the topic remains the same within all the given text answer just "0". do not add further text.
Text: {'<text>'}"""
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
input_ids,
max_new_tokens=1,
eos_token_id=terminators,
do_sample=True,
temperature=0.2
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=False))
```
## Training Details
### Training Data
Chapters in the Youtube videos contained in the train split of the dataset [raicrits/YouTube_RAI_dataset](https://huggingface.co/meta-llama/raicrits/YouTube_RAI_dataset)
### Training Procedure
The fine-tuning procedure was done using [LoRA](https://arxiv.org/abs/2106.09685) approach.
**Training setting:**
- train epochs=1,
- learning_rate=2e-05
- mixed precision training: int8
**LoRA configuration:**
- r= 8
- lora_alpha=16
- target_modules=["q_proj", "k_proj", "v_proj", "o_proj"]
- lora_dropout=0.1
- bias="none"
- task_type=CAUSAL_LM
## 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:** 1 NVIDIA A100/40Gb
- **Hours used:** 45
- **Cloud Provider:** Private Infrastructure
- **Carbon Emitted:** 4.86kg eq. CO2
## Model Card Authors
Stefano Scotta ([email protected])
## Model Card Contact
[email protected] |
V3N0M/Jenna-Unensored-GGUF-16-v2 | V3N0M | 2024-06-20T07:33:31Z | 19 | 1 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/tinyllama-chat-bnb-4bit",
"base_model:quantized:unsloth/tinyllama-chat-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-06-20T07:32:18Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/tinyllama-chat-bnb-4bit
---
# Uploaded model
- **Developed by:** V3N0M
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AlekseyElygin/mistral-7b-instruct-v0.3-4bit | AlekseyElygin | 2024-06-20T07:30:38Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
]
| text-generation | 2024-06-19T08:23:36Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
---
# Uploaded model
- **Developed by:** AlekseyElygin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
varun-v-rao/gpt2-lora-591K-squad-model2 | varun-v-rao | 2024-06-20T07:27:57Z | 11 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-06-20T06:59:42Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- varun-v-rao/squad
model-index:
- name: gpt2-lora-591K-squad-model2
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. -->
# gpt2-lora-591K-squad-model2
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 51
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
QiaoyuZheng/RadDiag | QiaoyuZheng | 2024-06-20T07:25:07Z | 0 | 3 | null | [
"license:mit",
"region:us"
]
| null | 2024-06-16T09:55:11Z | ---
license: mit
---
Two checkpoints are stored in this repository. For more information, please refer to our [Github repository](https://github.com/qiaoyu-zheng/RP3D-Diag) |
323danni/Qwen2-0.5B-GGUF | 323danni | 2024-06-20T07:23:49Z | 0 | 1 | null | [
"gguf",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| text-generation | 2024-06-19T21:02:56Z | ---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---
Provides GGUF files of Qwen2-0.5B, as well as the Float 16 format. |
AdrianKs/kgt5-context-descriptions-wikidata5m | AdrianKs | 2024-06-20T07:22:12Z | 5 | 0 | null | [
"pytorch",
"safetensors",
"region:us"
]
| null | 2023-11-14T16:59:42Z | # KGT5-context
Checkpoint for KGT5-context with description on the dataset Wikidata5M.
To see how to use and evaluate it see [KGT5-context codebase](https://github.com/uma-pi1/kgt5-context). |
Duakovui/viT5_uit_10_epochs | Duakovui | 2024-06-20T07:22:05Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-06-20T07:21:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
NNroc/SSGU-CD | NNroc | 2024-06-20T07:00:47Z | 0 | 0 | null | [
"biology",
"en",
"license:unknown",
"region:us"
]
| null | 2024-06-20T06:43:53Z | ---
license: unknown
language:
- en
tags:
- biology
--- |
varun-v-rao/gpt2-lora-591K-squad-model1 | varun-v-rao | 2024-06-20T06:59:39Z | 21 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-06-20T06:31:20Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- varun-v-rao/squad
model-index:
- name: gpt2-lora-591K-squad-model1
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. -->
# gpt2-lora-591K-squad-model1
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 88
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
V3N0M/Jenna-Uncensored-v2-16bit | V3N0M | 2024-06-20T06:53:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/tinyllama-chat-bnb-4bit",
"base_model:finetune:unsloth/tinyllama-chat-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T06:51:48Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/tinyllama-chat-bnb-4bit
---
# Uploaded model
- **Developed by:** V3N0M
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JamesSpray/llama-2-7b-chat-bnb-4bit-ift-dpo-001 | JamesSpray | 2024-06-20T06:51:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-20T06:47:31Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
V3N0M/Jenna-Uncensored-v2 | V3N0M | 2024-06-20T06:50:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-chat-bnb-4bit",
"base_model:finetune:unsloth/tinyllama-chat-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-20T06:50:03Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/tinyllama-chat-bnb-4bit
---
# Uploaded model
- **Developed by:** V3N0M
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-100-percent-low-high-nv-embed | AdamKasumovic | 2024-06-20T06:45:59Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T06:44:05Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Melanietrelaba/Test_question | Melanietrelaba | 2024-06-20T06:45:52Z | 0 | 0 | null | [
"fr",
"license:cc-by-2.0",
"region:us"
]
| null | 2024-06-20T06:45:01Z | ---
license: cc-by-2.0
language:
- fr
--- |
Anishproshort/llama3_ft | Anishproshort | 2024-06-20T06:40:21Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T06:01:53Z | ---
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] |
varun-v-rao/bart-base-bn-adapter-895K-squad-model1 | varun-v-rao | 2024-06-20T06:38:01Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"region:us"
]
| null | 2024-06-20T05:34:44Z | ---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
datasets:
- varun-v-rao/squad
model-index:
- name: bart-base-bn-adapter-895K-squad-model1
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. -->
# bart-base-bn-adapter-895K-squad-model1
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
shubh1410/si_distilBert_intent | shubh1410 | 2024-06-20T06:34:36Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-17T11:09:56Z | ---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_intent
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_intent
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2702 | 1.0 | 690 | 0.0219 | 0.9968 |
| 0.0172 | 2.0 | 1380 | 0.0084 | 0.9989 |
| 0.0045 | 3.0 | 2070 | 0.0044 | 0.9989 |
| 0.0021 | 4.0 | 2760 | 0.0033 | 0.9989 |
| 0.0015 | 5.0 | 3450 | 0.0025 | 0.9996 |
| 0.0007 | 6.0 | 4140 | 0.0011 | 0.9996 |
| 0.0004 | 7.0 | 4830 | 0.0008 | 0.9996 |
| 0.0002 | 8.0 | 5520 | 0.0006 | 1.0 |
| 0.0002 | 9.0 | 6210 | 0.0006 | 0.9996 |
| 0.0001 | 10.0 | 6900 | 0.0005 | 1.0 |
| 0.0001 | 11.0 | 7590 | 0.0004 | 1.0 |
| 0.0001 | 12.0 | 8280 | 0.0004 | 0.9996 |
| 0.0 | 13.0 | 8970 | 0.0006 | 0.9996 |
| 0.0 | 14.0 | 9660 | 0.0003 | 1.0 |
| 0.0 | 15.0 | 10350 | 0.0002 | 1.0 |
| 0.0 | 16.0 | 11040 | 0.0003 | 0.9996 |
| 0.0 | 17.0 | 11730 | 0.0003 | 0.9996 |
| 0.0 | 18.0 | 12420 | 0.0003 | 1.0 |
| 0.0 | 19.0 | 13110 | 0.0002 | 1.0 |
| 0.0 | 20.0 | 13800 | 0.0002 | 1.0 |
| 0.0 | 21.0 | 14490 | 0.0003 | 1.0 |
| 0.0 | 22.0 | 15180 | 0.0003 | 0.9996 |
| 0.0 | 23.0 | 15870 | 0.0002 | 1.0 |
| 0.0 | 24.0 | 16560 | 0.0004 | 0.9996 |
| 0.0 | 25.0 | 17250 | 0.0002 | 1.0 |
| 0.0 | 26.0 | 17940 | 0.0002 | 1.0 |
| 0.0 | 27.0 | 18630 | 0.0003 | 0.9996 |
| 0.0 | 28.0 | 19320 | 0.0001 | 1.0 |
| 0.0 | 29.0 | 20010 | 0.0002 | 1.0 |
| 0.0 | 30.0 | 20700 | 0.0002 | 1.0 |
| 0.0 | 31.0 | 21390 | 0.0002 | 1.0 |
| 0.0 | 32.0 | 22080 | 0.0001 | 1.0 |
| 0.0 | 33.0 | 22770 | 0.0001 | 1.0 |
| 0.0 | 34.0 | 23460 | 0.0001 | 1.0 |
| 0.0 | 35.0 | 24150 | 0.0001 | 1.0 |
| 0.0 | 36.0 | 24840 | 0.0001 | 1.0 |
| 0.0 | 37.0 | 25530 | 0.0001 | 1.0 |
| 0.0 | 38.0 | 26220 | 0.0001 | 1.0 |
| 0.0 | 39.0 | 26910 | 0.0001 | 1.0 |
| 0.0 | 40.0 | 27600 | 0.0001 | 1.0 |
| 0.0 | 41.0 | 28290 | 0.0001 | 1.0 |
| 0.0 | 42.0 | 28980 | 0.0001 | 1.0 |
| 0.0 | 43.0 | 29670 | 0.0001 | 1.0 |
| 0.0 | 44.0 | 30360 | 0.0001 | 1.0 |
| 0.0 | 45.0 | 31050 | 0.0001 | 1.0 |
| 0.0 | 46.0 | 31740 | 0.0001 | 1.0 |
| 0.0 | 47.0 | 32430 | 0.0001 | 1.0 |
| 0.0 | 48.0 | 33120 | 0.0001 | 1.0 |
| 0.0 | 49.0 | 33810 | 0.0001 | 1.0 |
| 0.0 | 50.0 | 34500 | 0.0001 | 1.0 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.19.2
- Tokenizers 0.19.1
|
tanliboy/zephyr-qwen2-7b-sft | tanliboy | 2024-06-20T06:32:24Z | 28 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:Qwen/Qwen2-7B",
"base_model:finetune:Qwen/Qwen2-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-10T06:02:44Z | ---
license: apache-2.0
base_model: Qwen/Qwen2-7B
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: zephyr-qwen2-7b-sft
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. -->
# zephyr-qwen2-7b-sft
This model is a fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0646
## 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
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0627 | 1.0 | 956 | 1.0646 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
varun-v-rao/gpt2-squad-model3 | varun-v-rao | 2024-06-20T06:29:45Z | 11 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-06-20T05:58:50Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- varun-v-rao/squad
model-index:
- name: gpt2-squad-model3
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. -->
# gpt2-squad-model3
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 79
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Swettha/qwen_10 | Swettha | 2024-06-20T06:25:45Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T06:24:42Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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] |
nerdthingz/a2c-PandaReachDense-v3 | nerdthingz | 2024-06-20T06:25:10Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-06-20T06:20:52Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.25 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-af-75-percent-low-med-high-nv-embed | AdamKasumovic | 2024-06-20T06:25:02Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T06:22:07Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mmpc/Qwen2-0.5B-Instruct-Singlish | mmpc | 2024-06-20T06:23:55Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T06:18:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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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
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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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<!-- 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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DBangshu/Base_gemma_e5_0_1 | DBangshu | 2024-06-20T06:20:43Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T06:15:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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#### Preprocessing [optional]
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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]
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<!-- 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
bihungba1101/Test_Adapter | bihungba1101 | 2024-06-20T06:19:36Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
]
| null | 2024-06-20T06:18:36Z | ---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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## Model Card Contact
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### Framework versions
- PEFT 0.11.1 |
pacoreyes/StanceFit | pacoreyes | 2024-06-20T06:19:20Z | 5 | 1 | setfit | [
"setfit",
"pytorch",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/all-mpnet-base-v2",
"base_model:finetune:sentence-transformers/all-mpnet-base-v2",
"doi:10.57967/hf/2618",
"region:us"
]
| text-classification | 2024-05-06T04:27:32Z | ---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: We will also discuss our deep concerns with actions by China, including in
Xinjiang, Hong Kong, Taiwan, cyber attacks on the United States, economic coercion
toward our allies.
- text: In the field of bilateral trade and investment, we have agreed that much can
be done to expand the present level of activity.
- text: We cannot allow the world's leading sponsor of terrorism to possess the planet's
most dangerous weapons.
- text: Because I do think this is not a function of whatever happened in Syria, I
think this is a function of the sanctions.
- text: One is to fight inflation, which has been hanging over our head and putting
a burden on the working people of this country for the last 10 years.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
---
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>'We in the United States believe if we can promote democracy around the world, there will be more peace.'</li><li>'We recognise the transformative power of technology, including digital public infrastructure, to support sustainable development in the Indo-Pacific and deliver economic and social benefits.'</li><li>'This program strengthens democracy, transparency, and the rule of law in developing nations, and I ask you to fully fund this important initiative.'</li></ul> |
| 1 | <ul><li>'I do not ever want to ever fight a war that is unconstitutional and I am the dangerous person.'</li><li>"And so, we are at a moment where I really think threats to our democracy, threats to our core freedoms are very much on people's minds."</li><li>'My views in opposition to the cancellation of the war debt are a matter of detailed record in many public statements and in a recent message to the Congress.'</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("We cannot allow the world's leading sponsor of terrorism to possess the planet's most dangerous weapons.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 23.4393 | 46 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 486 |
| 1 | 486 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (1.003444469523018e-06, 1.003444469523018e-06)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 37
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.3295 | - |
| 0.0017 | 50 | 0.3132 | - |
| 0.0034 | 100 | 0.274 | - |
| 0.0051 | 150 | 0.2774 | - |
| 0.0068 | 200 | 0.2578 | - |
| 0.0084 | 250 | 0.2536 | - |
| 0.0101 | 300 | 0.3353 | - |
| 0.0118 | 350 | 0.253 | - |
| 0.0135 | 400 | 0.2865 | - |
| 0.0152 | 450 | 0.2894 | - |
| 0.0169 | 500 | 0.2554 | 0.2632 |
| 0.0186 | 550 | 0.2487 | - |
| 0.0203 | 600 | 0.2713 | - |
| 0.0220 | 650 | 0.2841 | - |
| 0.0237 | 700 | 0.2251 | - |
| 0.0253 | 750 | 0.2534 | - |
| 0.0270 | 800 | 0.2489 | - |
| 0.0287 | 850 | 0.2297 | - |
| 0.0304 | 900 | 0.2288 | - |
| 0.0321 | 950 | 0.211 | - |
| 0.0338 | 1000 | 0.188 | 0.2073 |
| 0.0355 | 1050 | 0.1488 | - |
| 0.0372 | 1100 | 0.2103 | - |
| 0.0389 | 1150 | 0.1607 | - |
| 0.0406 | 1200 | 0.0793 | - |
| 0.0422 | 1250 | 0.0968 | - |
| 0.0439 | 1300 | 0.0987 | - |
| 0.0456 | 1350 | 0.0786 | - |
| 0.0473 | 1400 | 0.0267 | - |
| 0.0490 | 1450 | 0.0432 | - |
| 0.0507 | 1500 | 0.0262 | 0.064 |
| 0.0524 | 1550 | 0.1269 | - |
| 0.0541 | 1600 | 0.039 | - |
| 0.0558 | 1650 | 0.0266 | - |
| 0.0575 | 1700 | 0.0455 | - |
| 0.0591 | 1750 | 0.0175 | - |
| 0.0608 | 1800 | 0.0157 | - |
| 0.0625 | 1850 | 0.0063 | - |
| 0.0642 | 1900 | 0.0146 | - |
| 0.0659 | 1950 | 0.0046 | - |
| **0.0676** | **2000** | **0.0046** | **0.0464** |
| 0.0693 | 2050 | 0.0035 | - |
| 0.0710 | 2100 | 0.0073 | - |
| 0.0727 | 2150 | 0.0012 | - |
| 0.0744 | 2200 | 0.0025 | - |
| 0.0760 | 2250 | 0.0023 | - |
| 0.0777 | 2300 | 0.0017 | - |
| 0.0794 | 2350 | 0.0012 | - |
| 0.0811 | 2400 | 0.0017 | - |
| 0.0828 | 2450 | 0.0016 | - |
| 0.0845 | 2500 | 0.0014 | 0.0535 |
| 0.0862 | 2550 | 0.0011 | - |
| 0.0879 | 2600 | 0.0021 | - |
| 0.0896 | 2650 | 0.0009 | - |
| 0.0913 | 2700 | 0.0008 | - |
| 0.0929 | 2750 | 0.0006 | - |
| 0.0946 | 2800 | 0.0007 | - |
| 0.0963 | 2850 | 0.0012 | - |
| 0.0980 | 2900 | 0.001 | - |
| 0.0997 | 2950 | 0.0005 | - |
| 0.1014 | 3000 | 0.0006 | 0.0575 |
| 0.1031 | 3050 | 0.0006 | - |
| 0.1048 | 3100 | 0.0004 | - |
| 0.1065 | 3150 | 0.0006 | - |
| 0.1082 | 3200 | 0.0005 | - |
| 0.1098 | 3250 | 0.0006 | - |
| 0.1115 | 3300 | 0.0005 | - |
| 0.1132 | 3350 | 0.0008 | - |
| 0.1149 | 3400 | 0.0003 | - |
| 0.1166 | 3450 | 0.0005 | - |
| 0.1183 | 3500 | 0.0004 | 0.0642 |
| 0.1200 | 3550 | 0.0006 | - |
| 0.1217 | 3600 | 0.0003 | - |
| 0.1234 | 3650 | 0.0009 | - |
| 0.1251 | 3700 | 0.0002 | - |
| 0.1267 | 3750 | 0.0003 | - |
| 0.1284 | 3800 | 0.0005 | - |
| 0.1301 | 3850 | 0.0002 | - |
| 0.1318 | 3900 | 0.0002 | - |
| 0.1335 | 3950 | 0.0005 | - |
| 0.1352 | 4000 | 0.0003 | 0.0697 |
| 0.1369 | 4050 | 0.0002 | - |
| 0.1386 | 4100 | 0.0002 | - |
| 0.1403 | 4150 | 0.0004 | - |
| 0.1420 | 4200 | 0.0012 | - |
| 0.1436 | 4250 | 0.0002 | - |
| 0.1453 | 4300 | 0.0002 | - |
| 0.1470 | 4350 | 0.0001 | - |
| 0.1487 | 4400 | 0.0002 | - |
| 0.1504 | 4450 | 0.0002 | - |
| 0.1521 | 4500 | 0.0003 | 0.0718 |
| 0.1538 | 4550 | 0.0003 | - |
| 0.1555 | 4600 | 0.0002 | - |
| 0.1572 | 4650 | 0.0002 | - |
| 0.1589 | 4700 | 0.0003 | - |
| 0.1605 | 4750 | 0.0002 | - |
| 0.1622 | 4800 | 0.0002 | - |
| 0.1639 | 4850 | 0.0002 | - |
| 0.1656 | 4900 | 0.0002 | - |
| 0.1673 | 4950 | 0.0002 | - |
| 0.1690 | 5000 | 0.0002 | 0.0684 |
| 0.1707 | 5050 | 0.0002 | - |
| 0.1724 | 5100 | 0.0002 | - |
| 0.1741 | 5150 | 0.0002 | - |
| 0.1758 | 5200 | 0.0003 | - |
| 0.1774 | 5250 | 0.0002 | - |
| 0.1791 | 5300 | 0.0001 | - |
| 0.1808 | 5350 | 0.0002 | - |
| 0.1825 | 5400 | 0.0001 | - |
| 0.1842 | 5450 | 0.0001 | - |
| 0.1859 | 5500 | 0.0001 | 0.0731 |
| 0.1876 | 5550 | 0.0002 | - |
| 0.1893 | 5600 | 0.0002 | - |
| 0.1910 | 5650 | 0.0001 | - |
| 0.1927 | 5700 | 0.0001 | - |
| 0.1943 | 5750 | 0.0001 | - |
| 0.1960 | 5800 | 0.0002 | - |
| 0.1977 | 5850 | 0.0001 | - |
| 0.1994 | 5900 | 0.0003 | - |
| 0.2011 | 5950 | 0.0002 | - |
| 0.2028 | 6000 | 0.0002 | 0.0724 |
| 0.2045 | 6050 | 0.0001 | - |
| 0.2062 | 6100 | 0.0001 | - |
| 0.2079 | 6150 | 0.0001 | - |
| 0.2096 | 6200 | 0.0001 | - |
| 0.2112 | 6250 | 0.0001 | - |
| 0.2129 | 6300 | 0.0002 | - |
| 0.2146 | 6350 | 0.0001 | - |
| 0.2163 | 6400 | 0.0001 | - |
| 0.2180 | 6450 | 0.0001 | - |
| 0.2197 | 6500 | 0.0001 | 0.0784 |
| 0.2214 | 6550 | 0.0001 | - |
| 0.2231 | 6600 | 0.0001 | - |
| 0.2248 | 6650 | 0.0001 | - |
| 0.2265 | 6700 | 0.0001 | - |
| 0.2281 | 6750 | 0.0001 | - |
| 0.2298 | 6800 | 0.0001 | - |
| 0.2315 | 6850 | 0.0001 | - |
| 0.2332 | 6900 | 0.0001 | - |
| 0.2349 | 6950 | 0.0002 | - |
| 0.2366 | 7000 | 0.0001 | 0.0672 |
| 0.2383 | 7050 | 0.0001 | - |
| 0.2400 | 7100 | 0.0001 | - |
| 0.2417 | 7150 | 0.0001 | - |
| 0.2434 | 7200 | 0.0001 | - |
| 0.2450 | 7250 | 0.0001 | - |
| 0.2467 | 7300 | 0.0001 | - |
| 0.2484 | 7350 | 0.0001 | - |
| 0.2501 | 7400 | 0.0001 | - |
| 0.2518 | 7450 | 0.0001 | - |
| 0.2535 | 7500 | 0.0001 | 0.0627 |
| 0.2552 | 7550 | 0.0001 | - |
| 0.2569 | 7600 | 0.0001 | - |
| 0.2586 | 7650 | 0.0 | - |
| 0.2603 | 7700 | 0.0001 | - |
| 0.2619 | 7750 | 0.0 | - |
| 0.2636 | 7800 | 0.0001 | - |
| 0.2653 | 7850 | 0.0001 | - |
| 0.2670 | 7900 | 0.0001 | - |
| 0.2687 | 7950 | 0.0001 | - |
| 0.2704 | 8000 | 0.0 | 0.0754 |
| 0.2721 | 8050 | 0.0001 | - |
| 0.2738 | 8100 | 0.0001 | - |
| 0.2755 | 8150 | 0.0 | - |
| 0.2772 | 8200 | 0.0 | - |
| 0.2788 | 8250 | 0.0 | - |
| 0.2805 | 8300 | 0.0001 | - |
| 0.2822 | 8350 | 0.0001 | - |
| 0.2839 | 8400 | 0.0001 | - |
| 0.2856 | 8450 | 0.0 | - |
| 0.2873 | 8500 | 0.0 | 0.0748 |
| 0.2890 | 8550 | 0.0 | - |
| 0.2907 | 8600 | 0.0 | - |
| 0.2924 | 8650 | 0.0 | - |
| 0.2941 | 8700 | 0.0 | - |
| 0.2957 | 8750 | 0.0001 | - |
| 0.2974 | 8800 | 0.0001 | - |
| 0.2991 | 8850 | 0.0001 | - |
| 0.3008 | 8900 | 0.0 | - |
| 0.3025 | 8950 | 0.0001 | - |
| 0.3042 | 9000 | 0.0001 | 0.057 |
| 0.3059 | 9050 | 0.0 | - |
| 0.3076 | 9100 | 0.0 | - |
| 0.3093 | 9150 | 0.0002 | - |
| 0.3110 | 9200 | 0.0 | - |
| 0.3126 | 9250 | 0.0 | - |
| 0.3143 | 9300 | 0.0 | - |
| 0.3160 | 9350 | 0.0001 | - |
| 0.3177 | 9400 | 0.0002 | - |
| 0.3194 | 9450 | 0.0 | - |
| 0.3211 | 9500 | 0.0 | 0.0781 |
| 0.3228 | 9550 | 0.0 | - |
| 0.3245 | 9600 | 0.0 | - |
| 0.3262 | 9650 | 0.0 | - |
| 0.3279 | 9700 | 0.0 | - |
| 0.3295 | 9750 | 0.0 | - |
| 0.3312 | 9800 | 0.0 | - |
| 0.3329 | 9850 | 0.0 | - |
| 0.3346 | 9900 | 0.0001 | - |
| 0.3363 | 9950 | 0.0 | - |
| 0.3380 | 10000 | 0.0 | 0.0698 |
| 0.3397 | 10050 | 0.0 | - |
| 0.3414 | 10100 | 0.0 | - |
| 0.3431 | 10150 | 0.0 | - |
| 0.3448 | 10200 | 0.0 | - |
| 0.3464 | 10250 | 0.0022 | - |
| 0.3481 | 10300 | 0.0 | - |
| 0.3498 | 10350 | 0.0001 | - |
| 0.3515 | 10400 | 0.0 | - |
| 0.3532 | 10450 | 0.0 | - |
| 0.3549 | 10500 | 0.0 | 0.0698 |
| 0.3566 | 10550 | 0.0 | - |
| 0.3583 | 10600 | 0.0 | - |
| 0.3600 | 10650 | 0.0 | - |
| 0.3617 | 10700 | 0.0 | - |
| 0.3633 | 10750 | 0.0 | - |
| 0.3650 | 10800 | 0.0 | - |
| 0.3667 | 10850 | 0.0 | - |
| 0.3684 | 10900 | 0.0001 | - |
| 0.3701 | 10950 | 0.0 | - |
| 0.3718 | 11000 | 0.0 | 0.0746 |
| 0.3735 | 11050 | 0.0 | - |
| 0.3752 | 11100 | 0.0 | - |
| 0.3769 | 11150 | 0.0001 | - |
| 0.3786 | 11200 | 0.0 | - |
| 0.3802 | 11250 | 0.0 | - |
| 0.3819 | 11300 | 0.0 | - |
| 0.3836 | 11350 | 0.0 | - |
| 0.3853 | 11400 | 0.0 | - |
| 0.3870 | 11450 | 0.0 | - |
| 0.3887 | 11500 | 0.0 | 0.0753 |
| 0.3904 | 11550 | 0.0 | - |
| 0.3921 | 11600 | 0.0001 | - |
| 0.3938 | 11650 | 0.0 | - |
| 0.3955 | 11700 | 0.0 | - |
| 0.3971 | 11750 | 0.0 | - |
| 0.3988 | 11800 | 0.0 | - |
| 0.4005 | 11850 | 0.0 | - |
| 0.4022 | 11900 | 0.0 | - |
| 0.4039 | 11950 | 0.0 | - |
| 0.4056 | 12000 | 0.0 | 0.0743 |
| 0.4073 | 12050 | 0.0 | - |
| 0.4090 | 12100 | 0.0 | - |
| 0.4107 | 12150 | 0.0 | - |
| 0.4124 | 12200 | 0.0 | - |
| 0.4140 | 12250 | 0.0 | - |
| 0.4157 | 12300 | 0.0 | - |
| 0.4174 | 12350 | 0.0 | - |
| 0.4191 | 12400 | 0.0 | - |
| 0.4208 | 12450 | 0.0 | - |
| 0.4225 | 12500 | 0.0 | 0.0733 |
| 0.4242 | 12550 | 0.0 | - |
| 0.4259 | 12600 | 0.0 | - |
| 0.4276 | 12650 | 0.0 | - |
| 0.4293 | 12700 | 0.0 | - |
| 0.4309 | 12750 | 0.0 | - |
| 0.4326 | 12800 | 0.0 | - |
| 0.4343 | 12850 | 0.0 | - |
| 0.4360 | 12900 | 0.0 | - |
| 0.4377 | 12950 | 0.0 | - |
| 0.4394 | 13000 | 0.0 | 0.072 |
| 0.4411 | 13050 | 0.0 | - |
| 0.4428 | 13100 | 0.0 | - |
| 0.4445 | 13150 | 0.0 | - |
| 0.4462 | 13200 | 0.0 | - |
| 0.4478 | 13250 | 0.0 | - |
| 0.4495 | 13300 | 0.0 | - |
| 0.4512 | 13350 | 0.0 | - |
| 0.4529 | 13400 | 0.0 | - |
| 0.4546 | 13450 | 0.0 | - |
| 0.4563 | 13500 | 0.0 | 0.0753 |
| 0.4580 | 13550 | 0.0 | - |
| 0.4597 | 13600 | 0.0 | - |
| 0.4614 | 13650 | 0.0 | - |
| 0.4631 | 13700 | 0.0 | - |
| 0.4647 | 13750 | 0.0 | - |
| 0.4664 | 13800 | 0.0 | - |
| 0.4681 | 13850 | 0.0 | - |
| 0.4698 | 13900 | 0.0 | - |
| 0.4715 | 13950 | 0.0 | - |
| 0.4732 | 14000 | 0.0 | 0.0756 |
| 0.4749 | 14050 | 0.0 | - |
| 0.4766 | 14100 | 0.0 | - |
| 0.4783 | 14150 | 0.0 | - |
| 0.4800 | 14200 | 0.0 | - |
| 0.4816 | 14250 | 0.0 | - |
| 0.4833 | 14300 | 0.0 | - |
| 0.4850 | 14350 | 0.0 | - |
| 0.4867 | 14400 | 0.0 | - |
| 0.4884 | 14450 | 0.0 | - |
| 0.4901 | 14500 | 0.0 | 0.0622 |
| 0.4918 | 14550 | 0.0 | - |
| 0.4935 | 14600 | 0.0 | - |
| 0.4952 | 14650 | 0.0 | - |
| 0.4969 | 14700 | 0.0 | - |
| 0.4985 | 14750 | 0.0 | - |
| 0.5002 | 14800 | 0.0 | - |
| 0.5019 | 14850 | 0.0 | - |
| 0.5036 | 14900 | 0.0 | - |
| 0.5053 | 14950 | 0.0 | - |
| 0.5070 | 15000 | 0.0 | 0.0676 |
| 0.5087 | 15050 | 0.0 | - |
| 0.5104 | 15100 | 0.0 | - |
| 0.5121 | 15150 | 0.0 | - |
| 0.5138 | 15200 | 0.0 | - |
| 0.5154 | 15250 | 0.0 | - |
| 0.5171 | 15300 | 0.0 | - |
| 0.5188 | 15350 | 0.0 | - |
| 0.5205 | 15400 | 0.0 | - |
| 0.5222 | 15450 | 0.0 | - |
| 0.5239 | 15500 | 0.0 | 0.0668 |
| 0.5256 | 15550 | 0.0 | - |
| 0.5273 | 15600 | 0.0 | - |
| 0.5290 | 15650 | 0.0 | - |
| 0.5307 | 15700 | 0.0 | - |
| 0.5323 | 15750 | 0.0 | - |
| 0.5340 | 15800 | 0.0 | - |
| 0.5357 | 15850 | 0.0 | - |
| 0.5374 | 15900 | 0.0 | - |
| 0.5391 | 15950 | 0.0 | - |
| 0.5408 | 16000 | 0.0 | 0.0707 |
| 0.5425 | 16050 | 0.0 | - |
| 0.5442 | 16100 | 0.0 | - |
| 0.5459 | 16150 | 0.0 | - |
| 0.5476 | 16200 | 0.0 | - |
| 0.5492 | 16250 | 0.0 | - |
| 0.5509 | 16300 | 0.0 | - |
| 0.5526 | 16350 | 0.0 | - |
| 0.5543 | 16400 | 0.0 | - |
| 0.5560 | 16450 | 0.0 | - |
| 0.5577 | 16500 | 0.0 | 0.0644 |
| 0.5594 | 16550 | 0.0 | - |
| 0.5611 | 16600 | 0.0 | - |
| 0.5628 | 16650 | 0.0 | - |
| 0.5645 | 16700 | 0.0 | - |
| 0.5661 | 16750 | 0.0 | - |
| 0.5678 | 16800 | 0.0 | - |
| 0.5695 | 16850 | 0.0 | - |
| 0.5712 | 16900 | 0.0 | - |
| 0.5729 | 16950 | 0.0 | - |
| 0.5746 | 17000 | 0.0 | 0.0742 |
| 0.5763 | 17050 | 0.0 | - |
| 0.5780 | 17100 | 0.0 | - |
| 0.5797 | 17150 | 0.0 | - |
| 0.5814 | 17200 | 0.0 | - |
| 0.5830 | 17250 | 0.0 | - |
| 0.5847 | 17300 | 0.0 | - |
| 0.5864 | 17350 | 0.0 | - |
| 0.5881 | 17400 | 0.0 | - |
| 0.5898 | 17450 | 0.0 | - |
| 0.5915 | 17500 | 0.0 | 0.0738 |
| 0.5932 | 17550 | 0.0 | - |
| 0.5949 | 17600 | 0.0 | - |
| 0.5966 | 17650 | 0.0 | - |
| 0.5983 | 17700 | 0.0 | - |
| 0.5999 | 17750 | 0.0 | - |
| 0.6016 | 17800 | 0.0 | - |
| 0.6033 | 17850 | 0.0 | - |
| 0.6050 | 17900 | 0.0 | - |
| 0.6067 | 17950 | 0.0 | - |
| 0.6084 | 18000 | 0.0 | 0.0725 |
| 0.6101 | 18050 | 0.0 | - |
| 0.6118 | 18100 | 0.0 | - |
| 0.6135 | 18150 | 0.0 | - |
| 0.6152 | 18200 | 0.0 | - |
| 0.6168 | 18250 | 0.0 | - |
| 0.6185 | 18300 | 0.0 | - |
| 0.6202 | 18350 | 0.0 | - |
| 0.6219 | 18400 | 0.0 | - |
| 0.6236 | 18450 | 0.0 | - |
| 0.6253 | 18500 | 0.0 | 0.0724 |
| 0.6270 | 18550 | 0.0 | - |
| 0.6287 | 18600 | 0.0 | - |
| 0.6304 | 18650 | 0.0 | - |
| 0.6321 | 18700 | 0.0 | - |
| 0.6337 | 18750 | 0.0 | - |
| 0.6354 | 18800 | 0.0 | - |
| 0.6371 | 18850 | 0.0 | - |
| 0.6388 | 18900 | 0.0 | - |
| 0.6405 | 18950 | 0.0 | - |
| 0.6422 | 19000 | 0.0 | 0.0622 |
| 0.6439 | 19050 | 0.0 | - |
| 0.6456 | 19100 | 0.0 | - |
| 0.6473 | 19150 | 0.0 | - |
| 0.6490 | 19200 | 0.0 | - |
| 0.6506 | 19250 | 0.0 | - |
| 0.6523 | 19300 | 0.0 | - |
| 0.6540 | 19350 | 0.0 | - |
| 0.6557 | 19400 | 0.0 | - |
| 0.6574 | 19450 | 0.0 | - |
| 0.6591 | 19500 | 0.0 | 0.0754 |
| 0.6608 | 19550 | 0.0 | - |
| 0.6625 | 19600 | 0.0 | - |
| 0.6642 | 19650 | 0.0 | - |
| 0.6659 | 19700 | 0.0 | - |
| 0.6675 | 19750 | 0.0 | - |
| 0.6692 | 19800 | 0.0 | - |
| 0.6709 | 19850 | 0.0 | - |
| 0.6726 | 19900 | 0.0 | - |
| 0.6743 | 19950 | 0.0 | - |
| 0.6760 | 20000 | 0.0 | 0.0723 |
| 0.6777 | 20050 | 0.0 | - |
| 0.6794 | 20100 | 0.0 | - |
| 0.6811 | 20150 | 0.0 | - |
| 0.6828 | 20200 | 0.0 | - |
| 0.6844 | 20250 | 0.0 | - |
| 0.6861 | 20300 | 0.0 | - |
| 0.6878 | 20350 | 0.0 | - |
| 0.6895 | 20400 | 0.0 | - |
| 0.6912 | 20450 | 0.0 | - |
| 0.6929 | 20500 | 0.0 | 0.0741 |
| 0.6946 | 20550 | 0.0 | - |
| 0.6963 | 20600 | 0.0 | - |
| 0.6980 | 20650 | 0.0 | - |
| 0.6997 | 20700 | 0.0 | - |
| 0.7013 | 20750 | 0.0 | - |
| 0.7030 | 20800 | 0.0 | - |
| 0.7047 | 20850 | 0.0 | - |
| 0.7064 | 20900 | 0.0 | - |
| 0.7081 | 20950 | 0.0 | - |
| 0.7098 | 21000 | 0.0 | 0.0733 |
| 0.7115 | 21050 | 0.0 | - |
| 0.7132 | 21100 | 0.0 | - |
| 0.7149 | 21150 | 0.0 | - |
| 0.7166 | 21200 | 0.0 | - |
| 0.7182 | 21250 | 0.0 | - |
| 0.7199 | 21300 | 0.0 | - |
| 0.7216 | 21350 | 0.0 | - |
| 0.7233 | 21400 | 0.0 | - |
| 0.7250 | 21450 | 0.0 | - |
| 0.7267 | 21500 | 0.0 | 0.0757 |
| 0.7284 | 21550 | 0.0 | - |
| 0.7301 | 21600 | 0.0 | - |
| 0.7318 | 21650 | 0.0 | - |
| 0.7335 | 21700 | 0.0 | - |
| 0.7351 | 21750 | 0.0 | - |
| 0.7368 | 21800 | 0.0 | - |
| 0.7385 | 21850 | 0.0 | - |
| 0.7402 | 21900 | 0.0 | - |
| 0.7419 | 21950 | 0.0 | - |
| 0.7436 | 22000 | 0.0 | 0.0766 |
| 0.7453 | 22050 | 0.0 | - |
| 0.7470 | 22100 | 0.0 | - |
| 0.7487 | 22150 | 0.0 | - |
| 0.7504 | 22200 | 0.0 | - |
| 0.7520 | 22250 | 0.0 | - |
| 0.7537 | 22300 | 0.0 | - |
| 0.7554 | 22350 | 0.0 | - |
| 0.7571 | 22400 | 0.0 | - |
| 0.7588 | 22450 | 0.0 | - |
| 0.7605 | 22500 | 0.0 | 0.0757 |
| 0.7622 | 22550 | 0.0 | - |
| 0.7639 | 22600 | 0.0 | - |
| 0.7656 | 22650 | 0.0 | - |
| 0.7673 | 22700 | 0.0 | - |
| 0.7689 | 22750 | 0.0 | - |
| 0.7706 | 22800 | 0.0 | - |
| 0.7723 | 22850 | 0.0 | - |
| 0.7740 | 22900 | 0.0 | - |
| 0.7757 | 22950 | 0.0 | - |
| 0.7774 | 23000 | 0.0 | 0.0755 |
| 0.7791 | 23050 | 0.0 | - |
| 0.7808 | 23100 | 0.0 | - |
| 0.7825 | 23150 | 0.0 | - |
| 0.7842 | 23200 | 0.0 | - |
| 0.7858 | 23250 | 0.0 | - |
| 0.7875 | 23300 | 0.0 | - |
| 0.7892 | 23350 | 0.0 | - |
| 0.7909 | 23400 | 0.0 | - |
| 0.7926 | 23450 | 0.0 | - |
| 0.7943 | 23500 | 0.0 | 0.076 |
| 0.7960 | 23550 | 0.0 | - |
| 0.7977 | 23600 | 0.0 | - |
| 0.7994 | 23650 | 0.0 | - |
| 0.8011 | 23700 | 0.0 | - |
| 0.8027 | 23750 | 0.0 | - |
| 0.8044 | 23800 | 0.0 | - |
| 0.8061 | 23850 | 0.0 | - |
| 0.8078 | 23900 | 0.0 | - |
| 0.8095 | 23950 | 0.0 | - |
| 0.8112 | 24000 | 0.0 | 0.0756 |
| 0.8129 | 24050 | 0.0 | - |
| 0.8146 | 24100 | 0.0 | - |
| 0.8163 | 24150 | 0.0 | - |
| 0.8180 | 24200 | 0.0 | - |
| 0.8196 | 24250 | 0.0 | - |
| 0.8213 | 24300 | 0.0 | - |
| 0.8230 | 24350 | 0.0 | - |
| 0.8247 | 24400 | 0.0 | - |
| 0.8264 | 24450 | 0.0 | - |
| 0.8281 | 24500 | 0.0 | 0.0759 |
| 0.8298 | 24550 | 0.0 | - |
| 0.8315 | 24600 | 0.0 | - |
| 0.8332 | 24650 | 0.0 | - |
| 0.8349 | 24700 | 0.0 | - |
| 0.8365 | 24750 | 0.0 | - |
| 0.8382 | 24800 | 0.0 | - |
| 0.8399 | 24850 | 0.0 | - |
| 0.8416 | 24900 | 0.0 | - |
| 0.8433 | 24950 | 0.0 | - |
| 0.8450 | 25000 | 0.0 | 0.0762 |
| 0.8467 | 25050 | 0.0 | - |
| 0.8484 | 25100 | 0.0 | - |
| 0.8501 | 25150 | 0.0 | - |
| 0.8518 | 25200 | 0.0 | - |
| 0.8534 | 25250 | 0.0 | - |
| 0.8551 | 25300 | 0.0 | - |
| 0.8568 | 25350 | 0.0 | - |
| 0.8585 | 25400 | 0.0 | - |
| 0.8602 | 25450 | 0.0 | - |
| 0.8619 | 25500 | 0.0 | 0.0733 |
| 0.8636 | 25550 | 0.0 | - |
| 0.8653 | 25600 | 0.0 | - |
| 0.8670 | 25650 | 0.0 | - |
| 0.8687 | 25700 | 0.0 | - |
| 0.8703 | 25750 | 0.0 | - |
| 0.8720 | 25800 | 0.0 | - |
| 0.8737 | 25850 | 0.0 | - |
| 0.8754 | 25900 | 0.0 | - |
| 0.8771 | 25950 | 0.0 | - |
| 0.8788 | 26000 | 0.0 | 0.0742 |
| 0.8805 | 26050 | 0.0 | - |
| 0.8822 | 26100 | 0.0 | - |
| 0.8839 | 26150 | 0.0 | - |
| 0.8856 | 26200 | 0.0 | - |
| 0.8872 | 26250 | 0.0 | - |
| 0.8889 | 26300 | 0.0 | - |
| 0.8906 | 26350 | 0.0 | - |
| 0.8923 | 26400 | 0.0 | - |
| 0.8940 | 26450 | 0.0 | - |
| 0.8957 | 26500 | 0.0 | 0.0756 |
| 0.8974 | 26550 | 0.0 | - |
| 0.8991 | 26600 | 0.0 | - |
| 0.9008 | 26650 | 0.0 | - |
| 0.9025 | 26700 | 0.0 | - |
| 0.9041 | 26750 | 0.0 | - |
| 0.9058 | 26800 | 0.0 | - |
| 0.9075 | 26850 | 0.0 | - |
| 0.9092 | 26900 | 0.0 | - |
| 0.9109 | 26950 | 0.0 | - |
| 0.9126 | 27000 | 0.0 | 0.0751 |
| 0.9143 | 27050 | 0.0 | - |
| 0.9160 | 27100 | 0.0 | - |
| 0.9177 | 27150 | 0.0 | - |
| 0.9194 | 27200 | 0.0 | - |
| 0.9210 | 27250 | 0.0 | - |
| 0.9227 | 27300 | 0.0 | - |
| 0.9244 | 27350 | 0.0 | - |
| 0.9261 | 27400 | 0.0 | - |
| 0.9278 | 27450 | 0.0 | - |
| 0.9295 | 27500 | 0.0 | 0.075 |
| 0.9312 | 27550 | 0.0 | - |
| 0.9329 | 27600 | 0.0 | - |
| 0.9346 | 27650 | 0.0 | - |
| 0.9363 | 27700 | 0.0 | - |
| 0.9379 | 27750 | 0.0 | - |
| 0.9396 | 27800 | 0.0 | - |
| 0.9413 | 27850 | 0.0 | - |
| 0.9430 | 27900 | 0.0 | - |
| 0.9447 | 27950 | 0.0 | - |
| 0.9464 | 28000 | 0.0 | 0.0725 |
| 0.9481 | 28050 | 0.0 | - |
| 0.9498 | 28100 | 0.0 | - |
| 0.9515 | 28150 | 0.0 | - |
| 0.9532 | 28200 | 0.0 | - |
| 0.9548 | 28250 | 0.0 | - |
| 0.9565 | 28300 | 0.0 | - |
| 0.9582 | 28350 | 0.0 | - |
| 0.9599 | 28400 | 0.0 | - |
| 0.9616 | 28450 | 0.0 | - |
| 0.9633 | 28500 | 0.0 | 0.0761 |
| 0.9650 | 28550 | 0.0 | - |
| 0.9667 | 28600 | 0.0 | - |
| 0.9684 | 28650 | 0.0 | - |
| 0.9701 | 28700 | 0.0 | - |
| 0.9717 | 28750 | 0.0 | - |
| 0.9734 | 28800 | 0.0 | - |
| 0.9751 | 28850 | 0.0 | - |
| 0.9768 | 28900 | 0.0 | - |
| 0.9785 | 28950 | 0.0 | - |
| 0.9802 | 29000 | 0.0 | 0.0759 |
| 0.9819 | 29050 | 0.0 | - |
| 0.9836 | 29100 | 0.0 | - |
| 0.9853 | 29150 | 0.0 | - |
| 0.9870 | 29200 | 0.0 | - |
| 0.9886 | 29250 | 0.0 | - |
| 0.9903 | 29300 | 0.0 | - |
| 0.9920 | 29350 | 0.0 | - |
| 0.9937 | 29400 | 0.0 | - |
| 0.9954 | 29450 | 0.0 | - |
| 0.9971 | 29500 | 0.0 | 0.0761 |
| 0.9988 | 29550 | 0.0 | - |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.11
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.25.1
- PyTorch: 2.1.2
- Datasets: 2.15.0
- Tokenizers: 0.13.3
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
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duyntnet/BioMistral-7B-imatrix-GGUF | duyntnet | 2024-06-20T06:18:47Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"imatrix",
"BioMistral-7B",
"text-generation",
"en",
"license:other",
"region:us",
"conversational"
]
| text-generation | 2024-06-20T03:58:49Z | ---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- BioMistral-7B
---
Quantizations of https://huggingface.co/BioMistral/BioMistral-7B
# From original readme
## BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
### 2. Using BioMistral
You can use BioMistral with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow.
Loading the model and tokenizer :
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B")
model = AutoModel.from_pretrained("BioMistral/BioMistral-7B")
``` |
ANGKJ1995/distilbert-base-uncased-checkthat | ANGKJ1995 | 2024-06-20T06:14:26Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T02:40:41Z | ---
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. -->
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LongSafari/evo-1-8k-crispr | LongSafari | 2024-06-20T06:12:51Z | 131 | 2 | transformers | [
"transformers",
"safetensors",
"stripedhyena",
"text-generation",
"long context",
"deep signal processing",
"hybrid",
"biology",
"genomics",
"custom_code",
"arxiv:2302.10866",
"arxiv:2203.14343",
"arxiv:2310.18780",
"arxiv:2206.11893",
"arxiv:2303.06349",
"arxiv:2102.02611",
"arxiv:2210.09298",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
]
| text-generation | 2024-06-20T04:13:38Z | ---
license: apache-2.0
tags:
- stripedhyena
- long context
- deep signal processing
- hybrid
- biology
- genomics
---
## Evo-1 (CRISPR-Cas)
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/62a1306bbe7fa896d2c8de44/JoEHcvLTUlHoMcgh3mmAz.png" width="70%" />
</p>
### News
We identified and fixed an issue related to a wrong permutation of some projections, which affects generation quality. To use the new model revision, please load as follows:
```python
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True, revision="1.1_fix")
model = AutoModelForCausalLM.from_pretrained(
model_name,
config=config,
trust_remote_code=True,
revision="1.1_fix"
)
```
### About
Evo is a biological foundation model capable of long-context modeling and design.
Evo uses the [StripedHyena architecture](https://github.com/togethercomputer/stripedhyena) to enable modeling of sequences at a single-nucleotide, byte-level resolution with near-linear scaling of compute and memory relative to context length.
Evo has 7 billion parameters and is trained on OpenGenome, a prokaryotic whole-genome dataset containing ~300 billion tokens.
Technical details about Evo can be found in our preprint and our accompanying blog posts. Evo was collaboratively developed by the [Arc Institute](https://arcinstitute.org/) and TogetherAI.
As part of our commitment to open science, we release **weights of 15 intermediate pretraining checkpoints** for phase 1 and phase 2 of pretraining. The checkpoints are available as branches of the corresponding HuggingFace repository.
**Evo-1 (CRISPR-Cas)** is our fine-tuned model used to generate CRISPR-Cas systems, trained at a context length of 8k.
| Checkpoint Name | Description |
|----------------------------------------|-------------|
| `evo-1-8k-base` | A model pretrained with 8,192 context. We use this model as the base model for molecular-scale finetuning tasks. |
| `evo-1-131k-base` | A model pretrained with 131,072 context using `evo-1-8k-base` as the initialization. We use this model to reason about and generate sequences at the genome scale. |
| `evo-1-8k-crispr` | A model fine-tuned on `evo-1-8k-base` specifically on CRISPR-Cas systems. We use this model to generate Cas9/12/13 systems. |
| `evo-1-8k-transposon` | A model fine-tuned on `evo-1-8k-base` specifically on transposons. We use this to generate IS200/IS605. |
### Model Architecture
StripedHyena is a deep signal processing, hybrid architecture composed of multi-head attention and gated convolutions arranged in [Hyena](https://arxiv.org/abs/2302.10866) blocks, improving over decoder-only Transformers.
StripedHyena is designed to leverage the specialization of each of its layer classes, with Hyena layers implementing the bulk of the computation required for sequence processing and attention layers supplementing the ability to perform targeted pattern recall.
Some highlights of the architecture:
- **Efficient autoregressive generation** via a recurrent mode (>500k generation with a single 80GB GPU)
- **Significantly faster training and finetuning** at long context (>3x at 131k)
- **Improved scaling laws over state-of-the-art architectures** (e.g., Transformer++) on both natural language and biological sequences.
- **Robust to training beyond the compute-optimal frontier** e.g., training way beyond Chinchilla-optimal token amounts (see preprint for details -- more details to come)
### How to use Evo
Example usage is provided in the [standalone repo](https://github.com/evo-design/evo).
#### Parametrization for Inference and Finetuning
One of the advantages of deep signal processing models is their flexibility. Different parametrizations of convolutions can be used depending on the memory, expressivity and causality requirements of pretraining, finetuning or inference workloads.
The main classes are:
- Modal canonical: unconstrained poles ([reference](https://arxiv.org/pdf/2203.14343.pdf), [reference](https://arxiv.org/abs/2310.18780)), or constrained poles ([reference](https://arxiv.org/abs/2206.11893), [reference](https://arxiv.org/pdf/2303.06349.pdf)).
- Companion canonical / rational: TBA.
- Hypernetworks: hypernetwork ([reference](https://arxiv.org/abs/2102.02611)), modulated hypernetwork ([reference](https://arxiv.org/abs/2302.10866)).
- Explicit: modulated explicit ([reference](https://arxiv.org/pdf/2210.09298.pdf)).
StripedHyena is a mixed precision model. Make sure to keep your `poles` and `residues` in `float32` precision, especially for longer prompts or training.
### Disclaimer
To use StripedHyena outside of the playground, you will need to install custom kernels. Please follow the instructions from the [standalone repository](https://github.com/togethercomputer/stripedhyena).
## Cite
```
@article{nguyen2024sequence,
author = {Eric Nguyen and Michael Poli and Matthew G. Durrant and Armin W. Thomas and Brian Kang and Jeremy Sullivan and Madelena Y. Ng and Ashley Lewis and Aman Patel and Aaron Lou and Stefano Ermon and Stephen A. Baccus and Tina Hernandez-Boussard and Christopher Ré and Patrick D. Hsu and Brian L. Hie},
journal = {Arc Institute manuscripts},
title = {Sequence modeling and design from molecular to genome scale with Evo},
url = {https://arcinstitute.org/manuscripts/Evo},
year = {2024},
}
``` |
AbidHasan95/smsner_model2 | AbidHasan95 | 2024-06-20T06:11:19Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-20T05:31:12Z | ---
license: mit
language:
- en
library_name: transformers
--- |
absl2024/phi-3-mini-QLoRA | absl2024 | 2024-06-20T06:08:44Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
]
| null | 2024-06-20T06:08:28Z | ---
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: phi-3-mini-QLoRA
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. -->
# phi-3-mini-QLoRA
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5761
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1336 | 0.1809 | 100 | 0.6788 |
| 0.6283 | 0.3618 | 200 | 0.6030 |
| 0.5944 | 0.5427 | 300 | 0.5931 |
| 0.5953 | 0.7237 | 400 | 0.5879 |
| 0.5793 | 0.9046 | 500 | 0.5852 |
| 0.5908 | 1.0855 | 600 | 0.5832 |
| 0.5717 | 1.2664 | 700 | 0.5812 |
| 0.5748 | 1.4473 | 800 | 0.5802 |
| 0.5876 | 1.6282 | 900 | 0.5787 |
| 0.5725 | 1.8091 | 1000 | 0.5778 |
| 0.5749 | 1.9900 | 1100 | 0.5772 |
| 0.5646 | 2.1710 | 1200 | 0.5769 |
| 0.5806 | 2.3519 | 1300 | 0.5764 |
| 0.5679 | 2.5328 | 1400 | 0.5762 |
| 0.5683 | 2.7137 | 1500 | 0.5761 |
| 0.5715 | 2.8946 | 1600 | 0.5761 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
varun-v-rao/gpt2-bn-adapter-895K-squad-model1 | varun-v-rao | 2024-06-20T06:03:22Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"region:us"
]
| null | 2024-06-20T04:59:23Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- varun-v-rao/squad
model-index:
- name: gpt2-bn-adapter-895K-squad-model1
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. -->
# gpt2-bn-adapter-895K-squad-model1
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-af-100-percent-med-high-nv-embed | AdamKasumovic | 2024-06-20T06:01:39Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T05:59:24Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
hilmiatha/resnet18-flower-classifier | hilmiatha | 2024-06-20T06:01:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"biology",
"image-classification",
"id",
"dataset:miladfa7/5-Flower-Types-Classification-Dataset",
"endpoints_compatible",
"region:us"
]
| image-classification | 2024-06-20T05:44:59Z | ---
datasets:
- miladfa7/5-Flower-Types-Classification-Dataset
language:
- id
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- biology
---
metrics:
- name: Accuracy
type: Accuracy
value: 0.8980
# ResNet18 Flower Classifier
This model classifies images into one of five flower types.
## Usage
```python
from torchvision import transforms
from PIL import Image
import torch
from torchvision.models import resnet18
model = resnet18(weights=None)
model.load_state_dict(torch.load('path_to_model/pytorch_model.bin'))
model.eval()
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image = Image.open('path_to_image.jpg')
image = transform(image).unsqueeze(0)
with torch.no_grad():
output = model(image)
_, predicted = torch.max(output.data, 1)
print(predicted.item())
```
|
Shokouhi/YTFineTuneBert | Shokouhi | 2024-06-20T05:58:58Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T05:58:36Z | ---
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]
- **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. -->
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AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-75-percent-low-high-nv-embed | AdamKasumovic | 2024-06-20T05:57:16Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T05:54:13Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
wobskna/fine-tuned-roberta | wobskna | 2024-06-20T05:57:11Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T05:56:57Z | ---
library_name: transformers
tags: []
---
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dannykm/MixT5forQA | dannykm | 2024-06-20T05:57:03Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-06-20T05:54:53Z | ---
library_name: transformers
tags: []
---
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AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-af-50-percent-low-med-high-nv-embed | AdamKasumovic | 2024-06-20T05:55:08Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T05:52:56Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
BoburAmirov/test-llama-uz | BoburAmirov | 2024-06-20T05:50:19Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"region:us"
]
| null | 2024-06-20T05:49:12Z | ---
library_name: peft
base_model: NousResearch/Llama-2-7b-chat-hf
---
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djsull/roberta-spam-v3 | djsull | 2024-06-20T05:48:58Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T05:48:43Z | ---
library_name: transformers
tags: []
---
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indra-a/baseline_model_ft_v3 | indra-a | 2024-06-20T05:41:19Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T05:41:06Z | ---
library_name: transformers
tags: []
---
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<!-- 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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
RichardErkhov/fhai50032_-_BeagleLake-7B-gguf | RichardErkhov | 2024-06-20T05:38:28Z | 2 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-06-20T00:29:28Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
BeagleLake-7B - GGUF
- Model creator: https://huggingface.co/fhai50032/
- Original model: https://huggingface.co/fhai50032/BeagleLake-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [BeagleLake-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q2_K.gguf) | Q2_K | 2.53GB |
| [BeagleLake-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [BeagleLake-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [BeagleLake-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [BeagleLake-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [BeagleLake-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q3_K.gguf) | Q3_K | 3.28GB |
| [BeagleLake-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [BeagleLake-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [BeagleLake-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [BeagleLake-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q4_0.gguf) | Q4_0 | 3.83GB |
| [BeagleLake-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [BeagleLake-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [BeagleLake-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q4_K.gguf) | Q4_K | 4.07GB |
| [BeagleLake-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [BeagleLake-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q4_1.gguf) | Q4_1 | 4.24GB |
| [BeagleLake-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q5_0.gguf) | Q5_0 | 4.65GB |
| [BeagleLake-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [BeagleLake-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q5_K.gguf) | Q5_K | 4.78GB |
| [BeagleLake-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [BeagleLake-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q5_1.gguf) | Q5_1 | 5.07GB |
| [BeagleLake-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q6_K.gguf) | Q6_K | 5.53GB |
| [BeagleLake-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/fhai50032_-_BeagleLake-7B-gguf/blob/main/BeagleLake-7B.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- fhai50032/RolePlayLake-7B
- mlabonne/NeuralBeagle14-7B
base_model:
- fhai50032/RolePlayLake-7B
- mlabonne/NeuralBeagle14-7B
model-index:
- name: BeagleLake-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 70.39
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.38
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.25
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 64.92
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 83.19
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.91
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
name: Open LLM Leaderboard
---
# BeagleLake-7B
BeagleLake-7B is a merge of the following models :
* [fhai50032/RolePlayLake-7B](https://huggingface.co/fhai50032/RolePlayLake-7B)
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)
Merging models are not powerful but are helpful in the case that it can work like Transfer Learning similar idk.. But they perform high on Leaderboard
For ex. NeuralBeagle is powerful model with lot of potential to grow and RolePlayLake is Suitable for RP (No-Simping) and is significantly uncensored and nice obligations
Fine-tuning a Merged model as a base model is surely a way to look forward and see a lot of potential going forward..
Much thanks to [Charles Goddard](https://huggingface.co/chargoddard) for making simple interface ['mergekit' ](https://github.com/cg123/mergekit)
## 🧩 Configuration
```yaml
models:
- model: mlabonne/NeuralBeagle14-7B
# no params for base model
- model: fhai50032/RolePlayLake-7B
parameters:
weight: 0.8
density: 0.6
- model: mlabonne/NeuralBeagle14-7B
parameters:
weight: 0.3
density: [0.1,0.3,0.5,0.7,1]
merge_method: dare_ties
base_model: mlabonne/NeuralBeagle14-7B
parameters:
normalize: true
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "fhai50032/BeagleLake-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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fhai50032__BeagleLake-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.34|
|AI2 Reasoning Challenge (25-Shot)|70.39|
|HellaSwag (10-Shot) |87.38|
|MMLU (5-Shot) |64.25|
|TruthfulQA (0-shot) |64.92|
|Winogrande (5-shot) |83.19|
|GSM8k (5-shot) |63.91|
|
gg232/CartPole-v1 | gg232 | 2024-06-20T05:32:19Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-06-20T05:32:10Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 488.40 +/- 34.80
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Kitajiang/push_exam2-Q4_K_M-GGUF | Kitajiang | 2024-06-20T05:30:31Z | 1 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Kitajiang/push_exam2",
"base_model:quantized:Kitajiang/push_exam2",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-06-20T05:30:11Z | ---
base_model: Kitajiang/push_exam2
tags:
- llama-cpp
- gguf-my-repo
---
# Kitajiang/push_exam2-Q4_K_M-GGUF
This model was converted to GGUF format from [`Kitajiang/push_exam2`](https://huggingface.co/Kitajiang/push_exam2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Kitajiang/push_exam2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Kitajiang/push_exam2-Q4_K_M-GGUF --hf-file push_exam2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Kitajiang/push_exam2-Q4_K_M-GGUF --hf-file push_exam2-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Kitajiang/push_exam2-Q4_K_M-GGUF --hf-file push_exam2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Kitajiang/push_exam2-Q4_K_M-GGUF --hf-file push_exam2-q4_k_m.gguf -c 2048
```
|
indra-a/baseline_model | indra-a | 2024-06-20T05:28:19Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T02:42:27Z | ---
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] |
varun-v-rao/gpt2-squad-model1 | varun-v-rao | 2024-06-20T05:27:46Z | 11 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-06-20T04:56:59Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- varun-v-rao/squad
model-index:
- name: gpt2-squad-model1
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. -->
# gpt2-squad-model1
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
a2ran/news_press_trigram_classification | a2ran | 2024-06-20T05:26:31Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T05:26:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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## Training Details
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metta-ai/baseline.v0.5.4 | metta-ai | 2024-06-20T05:13:46Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
]
| reinforcement-learning | 2024-06-20T05:11:37Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
---
A(n) **APPO** model trained on the **GDY-MettaGrid** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r metta-ai/baseline.v0.5.4
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.5.4
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.5.4 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
0xfaskety/Qwen-Qwen1.5-7B-1718860352 | 0xfaskety | 2024-06-20T05:12:39Z | 3 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
]
| null | 2024-06-20T05:12:32Z | ---
library_name: peft
base_model: Qwen/Qwen1.5-7B
---
# 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]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
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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. -->
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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]
**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.11.1 |
QuantFactory/Llama-3-8B-ShareGPT-112K-GGUF | QuantFactory | 2024-06-20T05:10:47Z | 37 | 0 | transformers | [
"transformers",
"gguf",
"axolotl",
"generated_from_trainer",
"text-generation",
"base_model:Magpie-Align/Llama-3-8B-ShareGPT-112K",
"base_model:quantized:Magpie-Align/Llama-3-8B-ShareGPT-112K",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2024-06-20T03:22:15Z | ---
license: llama3
base_model: Magpie-Align/Llama-3-8B-ShareGPT-112K
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Llama-3-8B-ShareGPT
results: []
library_name: transformers
pipeline_tag: text-generation
---
# QuantFactory/Llama-3-8B-ShareGPT-112K-GGUF
This is quantized version of [Magpie-Align/Llama-3-8B-ShareGPT-112K](https://huggingface.co/Magpie-Align/Llama-3-8B-ShareGPT-112K) created using llama.cpp
# Model Description
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: flydust/ShareGPT-Vicuna-unfiltered
type: sharegpt
conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./out_Llama-8B-sharegpt-vicuna
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama-3-8B-Sharegpt-vicuna
wandb_log_model:
hub_model_id: SynDa/Llama-3-8B-ShareGPT
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# Llama-3-8B-ShareGPT
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7768 | 0.0012 | 1 | 0.8449 |
| 0.6441 | 0.3331 | 288 | 0.5582 |
| 0.5294 | 0.6662 | 576 | 0.5212 |
| 0.5777 | 0.9993 | 864 | 0.4849 |
| 0.4499 | 1.3218 | 1152 | 0.4766 |
| 0.4507 | 1.6549 | 1440 | 0.4752 |
| 0.4856 | 1.9880 | 1728 | 0.4747 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-100-percent-low-med-nv-embed | AdamKasumovic | 2024-06-20T05:08:00Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T05:06:16Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
longxia/google-gemma-2b-1718859994 | longxia | 2024-06-20T05:06:45Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
]
| null | 2024-06-20T05:06:36Z | ---
library_name: peft
base_model: google/gemma-2b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
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#### Preprocessing [optional]
[More Information Needed]
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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]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
indra-a/baseline_model_ft_v2 | indra-a | 2024-06-20T05:06:26Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T05:06:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- 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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longxia/Qwen-Qwen1.5-7B-1718859933 | longxia | 2024-06-20T05:05:41Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
]
| null | 2024-06-20T05:05:36Z | ---
library_name: peft
base_model: Qwen/Qwen1.5-7B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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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### Framework versions
- PEFT 0.11.1 |
longxia/Qwen-Qwen1.5-0.5B-1718859767 | longxia | 2024-06-20T05:02:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
]
| null | 2024-06-20T05:02:49Z | ---
library_name: peft
base_model: Qwen/Qwen1.5-0.5B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **Developed by:** [More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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### Framework versions
- PEFT 0.11.1 |
Chuanming/sd-class-butterflies-32 | Chuanming | 2024-06-20T05:01:41Z | 9 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2024-06-20T05:01:35Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Chuanming/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
VenkyPas/llama38binstruct_summarize | VenkyPas | 2024-06-20T04:59:08Z | 1 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"base_model:adapter:NousResearch/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
]
| null | 2024-06-20T04:58:57Z | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: NousResearch/Meta-Llama-3-8B-Instruct
datasets:
- generator
model-index:
- name: llama38binstruct_summarize
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. -->
# llama38binstruct_summarize
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8328
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3955 | 1.3158 | 25 | 1.3893 |
| 0.4287 | 2.6316 | 50 | 1.5275 |
| 0.2387 | 3.9474 | 75 | 1.6572 |
| 0.0876 | 5.2632 | 100 | 1.8328 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
indra-a/baseline_model_ft_v1 | indra-a | 2024-06-20T04:55:34Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T04:55:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### 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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ANGKJ1995/conv-bert-base-checkthat | ANGKJ1995 | 2024-06-20T04:37:10Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"convbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-06-20T04:36:22Z | ---
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]
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[More Information Needed]
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<!-- 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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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-75-percent-low-med-nv-embed | AdamKasumovic | 2024-06-20T04:35:12Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T04:32:11Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
cdofitas/roberta-finetuned-subjqa-movies_2 | cdofitas | 2024-06-20T04:31:52Z | 29 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:deepset/roberta-base-squad2",
"base_model:finetune:deepset/roberta-base-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-06-18T06:59:20Z | ---
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-subjqa-movies_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-finetuned-subjqa-movies_2
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
zhaorui-nb/Qwen1.5-7B-Chat._.lora_ft._.Setting2 | zhaorui-nb | 2024-06-20T04:30:32Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T04:07:00Z | ---
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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
Slyne/funcodec_codecSuperb | Slyne | 2024-06-20T04:27:37Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-20T04:17:24Z | # Setup Environment
```
git clone https://github.com/Slyne/FunCodec
cd FunCodec && git checkout slyne_fix && cd ..
```
The tested environment for the below part is based on docker `nvcr.io/nvidia/pytorch:24.04-py3` OR a conda environment should be good as well.
```
# mount the current directory to /ws; You can put your data in your current
# directory as well.
docker run --gpus all -it -v $PWD:/ws nvcr.io/nvidia/pytorch:24.04-py3
Or
conda create -n funcodec python=3.10
```
### Install packages
```
cd /ws/FunCodec;
pip install --editable ./ ; pip install torchaudio;
```
### Prepare dataset
Please prepare your dataset similar to `${sampling_rate}_wav.scp` and put them in `/ws/test_wavscp/`
```
44100_wav.scp
48000_wav.scp
16000_wav.scp
```
Each `wav.scp` file looks like below:
```
<wavid> <absolute_path>
WAbHmvQ9zME_00002 /raid/slyne/codec_evaluation/Codec-SUPERB/data/vox1_test_wav/wav/id10302/WAbHmvQ9zME/00002.wav
```
**Example**
Please follow [here](https://github.com/voidful/Codec-SUPERB/tree/SLT_Challenge?tab=readme-ov-file#2-data-download) to download `Codec-SUPERB` test datasets.
```
# suppose the unzip data dir is /ws/data
python3 generate_wavscp.py --input_dir=/ws/data
```
### Download models
Download models from [here](https://huggingface.co/Slyne/funcodec_codecSuperb). And put them under `FunCodec/egs/codecSuperb/models`
### Do inference
Please refer to `FunCodec/egs/codecSuperb/do_codecSuperb_infer.sh` to do inference.
```
# set model to the default model trained with 16khz data
model_dir=models/16k/
model_name=8epoch.pth
sample_rates=(16000 44100 48000) # the input wavscp sample rate ca be 16khz, 44.1khz or 48khz
```
Run:
```
cd FunCodec/egs/codecSuperb/
# modify the ref_audio_dir and syn_audio_dir
bash do_codecSuperb_infer.sh
```
|
Jaidchen/Llama3-German-8B-IQ4_NL-GGUF | Jaidchen | 2024-06-20T04:26:47Z | 2 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"de",
"base_model:DiscoResearch/Llama3-German-8B",
"base_model:quantized:DiscoResearch/Llama3-German-8B",
"license:llama3",
"endpoints_compatible",
"region:us",
"imatrix"
]
| null | 2024-06-20T04:26:23Z | ---
base_model: DiscoResearch/Llama3-German-8B
language:
- de
library_name: transformers
license: llama3
tags:
- llama-cpp
- gguf-my-repo
---
# Jaidchen/Llama3-German-8B-IQ4_NL-GGUF
This model was converted to GGUF format from [`DiscoResearch/Llama3-German-8B`](https://huggingface.co/DiscoResearch/Llama3-German-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DiscoResearch/Llama3-German-8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Jaidchen/Llama3-German-8B-IQ4_NL-GGUF --hf-file llama3-german-8b-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Jaidchen/Llama3-German-8B-IQ4_NL-GGUF --hf-file llama3-german-8b-iq4_nl-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Jaidchen/Llama3-German-8B-IQ4_NL-GGUF --hf-file llama3-german-8b-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Jaidchen/Llama3-German-8B-IQ4_NL-GGUF --hf-file llama3-german-8b-iq4_nl-imat.gguf -c 2048
```
|
ShadNygren/FineTuneTest-DrugAdverseEffects-SIDER-Diego1-10epochs | ShadNygren | 2024-06-20T04:23:26Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T04:16:31Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: meta-llama/Meta-Llama-3-8B-Instruct
---
# Uploaded model
- **Developed by:** ShadNygren
- **License:** apache-2.0
- **Finetuned from model :** meta-llama/Meta-Llama-3-8B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
longxia/Qwen-Qwen1.5-7B-1718857320 | longxia | 2024-06-20T04:22:07Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
]
| null | 2024-06-20T04:22:02Z | ---
library_name: peft
base_model: Qwen/Qwen1.5-7B
---
# 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]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
longxia/Qwen-Qwen1.5-0.5B-1718857179 | longxia | 2024-06-20T04:19:45Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
]
| null | 2024-06-20T04:19:41Z | ---
library_name: peft
base_model: Qwen/Qwen1.5-0.5B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
qannisa/Llama-2-7b-finetuned-utbuddy-ver-2 | qannisa | 2024-06-20T04:18:21Z | 6 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-20T04:14:24Z | ---
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]
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