Meta-Llama-3.1-405B-Instruct-GPTQ-INT4
| Property | Value |
|---|---|
| Parameter Count | 58.5B parameters |
| License | Llama 3.1 |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
| Quantization | INT4 (GPTQ) |
| VRAM Requirement | 203+ GB |
What is Meta-Llama-3.1-405B-Instruct-GPTQ-INT4?
This is a community-driven quantized version of Meta's flagship Llama 3.1 405B model, compressed from FP16 to INT4 precision using GPTQ quantization. It maintains the powerful capabilities of the original model while significantly reducing the memory footprint, though still requiring substantial computational resources.
Implementation Details
The model employs GPTQ kernels with zero-point quantization and a group size of 128, optimized for efficient inference while preserving model quality. It's built on the transformers framework and supports multiple deployment options including AutoGPTQ, Text Generation Inference (TGI), and vLLM.
- Optimized for multilingual dialogue use cases
- Supports context length up to 4096 tokens
- Implements INT4 quantization for efficiency
- Compatible with multiple deployment frameworks
Core Capabilities
- Multilingual understanding and generation across 8 languages
- Advanced dialogue and instruction following
- Efficient inference with reduced precision
- Support for both CPU and GPU deployment
Frequently Asked Questions
Q: What makes this model unique?
This model represents a carefully optimized version of the massive 405B parameter Llama 3.1, using INT4 quantization to make it more deployable while maintaining performance. It's particularly notable for its multilingual capabilities and optimization for dialogue tasks.
Q: What are the recommended use cases?
The model excels in multilingual dialogue applications, instruction following, and general language understanding tasks. However, users should note the significant hardware requirements (203+ GB VRAM) for deployment.
