Qwentile2.5 32 B Instruct G G U F

Introduction

The Qwentile2.5-32B-Instruct-GGUF model is a text generation model designed for English conversational and chat applications. It is quantized using llama.cpp, specifically optimized with the imatrix option for improved performance.

Architecture

The model utilizes the llama.cpp architecture and features various quantization formats to optimize for different hardware configurations. These include Q8_0 for high-quality embeddings and output weights, and innovative formats like IQ4_XS, which offer a balance between performance and size.

Training

The model was trained using datasets specifically curated for the imatrix calibration, which enhances the model's precision in text generation tasks. The quantization process was tailored to maximize efficiency across different computing platforms, from CPUs to GPUs.

Guide: Running Locally

1. Install Prerequisites: Ensure you have huggingface_hub installed.

   pip install -U "huggingface_hub[cli]"
   

2. Download the Model: Use the Hugging Face CLI to download the desired quantization format. For example:

   huggingface-cli download bartowski/Qwentile2.5-32B-Instruct-GGUF --include "Qwentile2.5-32B-Instruct-Q4_K_M.gguf" --local-dir ./
   

3. Select the Appropriate Quantization: Choose based on your hardware's RAM/VRAM capabilities. For maximum performance, fit the model on your GPU's VRAM.

4. Run the Model: Load in an inference engine like LM Studio for execution.

Suggested Cloud GPUs: Consider using cloud services that provide NVIDIA or AMD GPUs, which support the necessary cuBLAS or rocBLAS libraries for optimal performance.

License

The Qwentile2.5-32B-Instruct-GGUF model is released under the Apache-2.0 license, allowing for broad use and modification within the license's terms.