Meta-Llama-3.1-8B-Instruct-GPTQ-INT4
| Property | Value |
|---|---|
| Parameter Count | 1.99B |
| License | LLaMA 3.1 |
| Supported Languages | 8 (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai) |
| Precision | INT4 (GPTQ Quantized) |
| Required VRAM | ~4 GB |
What is Meta-Llama-3.1-8B-Instruct-GPTQ-INT4?
This is a community-driven quantized version of Meta's Llama 3.1 8B model, specifically optimized for efficient deployment while maintaining performance. The model has been quantized from FP16 to INT4 precision using AutoGPTQ technology, making it accessible for deployment on hardware with limited resources.
Implementation Details
The model utilizes GPTQ kernels with zero-point quantization and a group size of 128, enabling efficient inference while preserving model quality. It's built on the transformers framework and supports multiple deployment options including TGI and vLLM.
- Optimized for multilingual dialogue use cases
- Supports 8 different languages
- Requires only 4GB VRAM for base model loading
- Compatible with popular inference frameworks
Core Capabilities
- Multilingual text generation and dialogue
- Efficient deployment through INT4 quantization
- Support for context length up to 4096 tokens
- Integration with major deployment platforms (TGI, vLLM, transformers)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient INT4 quantization while maintaining the capabilities of the original Llama 3.1 8B model, making it accessible for deployment on consumer-grade hardware with limited VRAM.
Q: What are the recommended use cases?
The model is ideal for multilingual dialogue applications, chatbots, and text generation tasks where resource efficiency is crucial. It's particularly suitable for deployment scenarios with limited GPU resources while requiring support for multiple languages.
