Meta-Llama-3.1-70B-Instruct-GPTQ-INT4
| Property | Value |
|---|---|
| Parameter Count | 70B |
| Precision | INT4 (4-bit quantization) |
| Languages | 8 (en, de, fr, it, pt, hi, es, th) |
| License | LLaMA 3.1 |
| Required VRAM | ~35GB |
What is Meta-Llama-3.1-70B-Instruct-GPTQ-INT4?
This is a community-driven quantized version of Meta's LLaMA 3.1 70B Instruct model, optimized for efficient deployment while maintaining performance. The model utilizes GPTQ quantization to reduce the model from FP16 to INT4 precision, significantly decreasing memory requirements while preserving model capabilities.
Implementation Details
The model employs AutoGPTQ quantization with zero-point quantization and a group size of 128. It's designed for multilingual dialogue use cases and can be deployed using various frameworks including transformers, AutoGPTQ, or text-generation-inference.
- Utilizes GPTQ kernels for efficient 4-bit quantization
- Requires approximately 35GB of VRAM for model loading
- Supports 8 different languages for multilingual applications
- Compatible with multiple deployment options including TGI and vLLM
Core Capabilities
- Multilingual dialogue generation across 8 languages
- Efficient memory usage through INT4 quantization
- Support for context length up to 4096 tokens
- Integration with popular frameworks and deployment solutions
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization of the powerful LLaMA 3.1 70B architecture, making it more accessible for deployment while maintaining multilingual capabilities across 8 languages. The quantization reduces the memory footprint significantly while preserving model performance.
Q: What are the recommended use cases?
The model is optimized for multilingual dialogue applications and can be used for various text generation tasks. It's particularly suitable for deployments where memory efficiency is crucial but high-quality multilingual performance is required. The model can be effectively used in production environments using TGI or vLLM for optimized inference.
