llama 3 chinese 8b instruct v3 gguf

Introduction

The Llama-3-Chinese-8B-Instruct-v3-GGUF is a quantized instruction-based model designed for conversation and question-answering tasks. It is compatible with platforms like llama.cpp, ollama, and tgw. This model supports both Chinese and English languages.

Architecture

This model is a quantized version of the Llama-3-Chinese-8B-Instruct-v3, optimized for performance and memory usage. It utilizes various quantization levels to balance between model size and performance, with metrics provided in terms of perplexity (PPL).

Training

The model supports quantization levels ranging from Q2_K to F16, with corresponding model sizes and perplexity scores:

• Q2_K: 2.96 GB, PPL 10.0534
• Q3_K: 3.74 GB, PPL 6.3295
• Q4_0: 4.34 GB, PPL 6.3200
• Q4_K: 4.58 GB, PPL 6.0042
• Q5_0: 5.21 GB, PPL 6.0437
• Q5_K: 5.34 GB, PPL 5.9484
• Q6_K: 6.14 GB, PPL 5.9469
• Q8_0: 7.95 GB, PPL 5.8933
• F16: 14.97 GB, PPL 5.8902

For optimal performance, use Q8_0 or Q6_K, unless memory constraints are a concern.

Guide: Running Locally

1. Clone the Repository:
   Clone the model repository from Hugging Face.

2. Setup Environment:
   Install the necessary dependencies for running the model. Ensure that your environment supports either Chinese or English language processing.

3. Select Quantization Level:
   Choose a quantization level based on your memory capacity and performance requirements.

4. Run the Model:
   Execute the model on your local machine or deploy it using compatible platforms like llama.cpp.

5. Consider Using Cloud GPUs:
   For better performance, consider using cloud-based GPU services such as AWS, GCP, or Azure.

License

The model is released under the Apache 2.0 license, allowing for wide usage and modification with adherence to the license terms.