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Hercules-5.0-Qwen2-1.5B-GGUF

Text Generation
GGUF
English
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Hercules-5.0-Qwen2-1.5B-GGUF
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---
license: apache-2.0
datasets:
- Locutusque/hercules-v5.0
base_model: M4-ai/Hercules-5.0-Qwen2-1.5B
language:
- en
inference:
  parameters:
    do_sample: true
    temperature: 0.8
    top_p: 0.95
    top_k: 40
    min_p: 0.1
    max_new_tokens: 250
    repetition_penalty: 1.1
pipeline_tag: text-generation
---


# Hercules-5.0-Qwen2-1.5B-GGUF
This is quantized version of [M4-ai/Hercules-5.0-Qwen2-1.5B](https://huggingface.co/M4-ai/Hercules-5.0-Qwen2-1.5B) created using llama.cpp


# Model Description

<!-- Provide a quick summary of what the model is/does. -->
We fine-tuned qwen2-1.5B on a high quality mix for general-purpose assistants. A DPO version of this will be released soon. We use the ChatML prompt format.


## Model Details

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

This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants.

- **Developed by:** M4-ai
- **Language(s) (NLP):** English and maybe Chinese
- **License:** apache-2.0
- **Finetuned from model:** [qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B)

## 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..

This language model made an impressive achievement, and correctly implemented a Multi Head Attention for use in a transformer neural network.

### 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.

## Training Details

### Training Data

- Locutusque/hercules-v5.0

## Evaluations

coming soon

#### 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.