14 B Qwen2.5 Freya x1 G G U F

Introduction

The 14B-Qwen2.5-Freya-x1-GGUF model is a quantized version of the 14B-Qwen2.5-Freya-x1 model, designed for efficient performance in various applications. It is part of the Hugging Face Transformers library and is ideal for conversational AI tasks.

Architecture

The model is built on the Sao10K/14B-Qwen2.5-Freya-x1 base model, employing the GGUF quantization format. This format supports different quantization levels to optimize performance and storage, ranging from Q2 to Q8 quant types.

Training

The model was generated using the Trainer API from the Transformers library. This approach ensures standardized training procedures and compatibility with various deployment endpoints.

Guide: Running Locally

1. Prerequisites:

   • Install the Hugging Face Transformers library and its dependencies.
   • Ensure you have access to suitable hardware, preferably a GPU.

2. Download the Model:

   • Visit the model's Hugging Face page and choose the desired quantization level (e.g., Q4_K_S for balance between speed and quality).

3. Set Up Environment:

   • Use Python virtual environments or Anaconda to manage dependencies.
   • Install the model using pip install transformers.

4. Run Inference:

   • Load the model using the Transformers library and run inference tasks.

5. Cloud GPUs:

   • For enhanced performance, consider using cloud GPU services such as AWS EC2, Google Cloud, or Azure.

License

The model is released under the Qwen license. For detailed licensing terms, refer to the license document provided on the model's page.