llama-65b-4bit
| Property | Value |
|---|---|
| Model Type | Transformer |
| Language | English |
| Framework | Transformers |
| Author | maderix |
What is llama-65b-4bit?
llama-65b-4bit is a highly optimized 4-bit quantized version of the LLaMA 65B model, converted using the GPTQ-for-LLaMa framework. This model represents a significant achievement in model compression while maintaining performance, making it more accessible for deployment in resource-constrained environments.
Implementation Details
The model utilizes GPTQ quantization techniques and requires substantial computational resources for conversion, specifically more than 120GB of RAM. It has been tested on A100-80G GPUs and implements the Transformers library architecture.
- Requires Python 3.8 environment
- Compatible with PyTorch 1.13 (cuda116)
- Needs latest Transformers library with specific PR integration
- Implements sentencepiece tokenization
Core Capabilities
- Efficient inference with 4-bit quantization
- Optimal performance with repetition penalty (~1/0.85)
- Temperature control (recommended 0.7) for generation
- Compatible with Hugging Face's inference endpoints
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization of the full LLaMA 65B model, making it possible to run inference on more accessible hardware while maintaining performance.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient inference of large language models, particularly when working with limited computational resources while still needing the capabilities of a 65B parameter model.
