README.md

---
library_name: transformers
license: other
datasets:
- Locutusque/hercules-v4.0
language:
- en
inference:
  parameters:
    do_sample: true
    temperature: 1
    top_p: 0.7
    top_k: 4
    max_new_tokens: 250
    repetition_penalty: 1.1
---

# Hercules-Mini-1.8B 

<!-- Provide a quick summary of what the model is/does. -->
We fine-tuned Qwen1.5-1.8B on Locutusque's Hercules-v4.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This model has capabilities in math, coding, function calling, roleplay, and more. We fine-tuned it using 700,000 examples of Hercules-v4. 

- **Developed by:** M4-ai
- **Language(s) (NLP):** English and maybe Chinese
- **License:** tongyi-qianwen license
- **Finetuned from model:** [Qwen1.5-1.8B](https://huggingface.co./Qwen/Qwen1.5-1.8B)

## 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. -->

General purpose assistant, question answering, chain-of-thought, etc..

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The eos token was not setup properly, so to prevent infinite generation you'll need to implement a stopping criteria when the model generates the <|im_end|> token. 

### 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.

## Evaluation
Coming soon


## 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. -->
https://huggingface.co./datasets/Locutusque/hercules-v4.0


#### Training Hyperparameters

- **Training regime:** bf16 non-mixed precision
## Technical Specifications

#### Hardware

We used 8 Kaggle TPUs, and we trained at a global batch size of 256 and sequence length of 1536

## Contributions

Thanks to @Tonic, @aloobun, @fhai50032, and @Locutusque for their contributions to this model.