Uw U 7 B Instruct G G U F

Introduction

The UWU-7B-INSTRUCT-GGUF model is a text generation model hosted on Hugging Face. It is designed for conversational AI applications and is compatible with various inference endpoints. The model is quantized using llama.cpp to optimize performance.

Architecture

The UWU-7B-INSTRUCT-GGUF model is built on the base model qingy2024/UwU-7B-Instruct. It is available in multiple quantized formats, each optimized for different performance and resource needs. The quantization uses the llama.cpp framework, specifically the imatrix option, to enhance efficiency.

Training

The model was trained using the FineQwQ-142k dataset, which is pertinent to its conversational focus. Quantization was applied to compress the model while maintaining performance, resulting in various versions like Q8_0, Q6_K_L, and others, each providing different balances of quality and size.

Guide: Running Locally

To run the UWU-7B-INSTRUCT-GGUF model locally:

1. Install Dependencies: Ensure you have Python and the huggingface_hub package installed. Use the command:

   pip install -U "huggingface_hub[cli]"
   

2. Download the Model: Use the Hugging Face CLI to download the desired quantized model file. For example:

   huggingface-cli download bartowski/UwU-7B-Instruct-GGUF --include "UwU-7B-Instruct-Q4_K_M.gguf" --local-dir ./
   

3. Select Quantization: Choose a quant version based on your hardware's RAM or VRAM. Use a file size 1-2GB smaller than your available memory.

4. Run the Model: Use compatible frameworks or engines, such as LM Studio, to load and run the model.

For optimal performance, consider using cloud GPUs, such as those from AWS or Azure, to handle larger models efficiently.

License

The UWU-7B-INSTRUCT-GGUF model is licensed under the Apache 2.0 License, allowing for both personal and commercial use with proper attribution.