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---
base_model: M4-ai/Hercules-Mini-1.8B
datasets:
- Locutusque/hercules-v4.0
inference: true
language:
- en
library_name: transformers
license: other
model_creator: M4-ai
model_name: Hercules-Mini-1.8B
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
---
# Hercules-Mini-1.8B-GGUF
Quantized GGUF model files for [Hercules-Mini-1.8B](https://huggingface.co./M4-ai/Hercules-Mini-1.8B) from [M4-ai](https://huggingface.co./M4-ai)
## Original Model Card:
# 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.