Meta-Llama-3.1-8B-Instruct-AWQ-INT4
| Property | Value |
|---|---|
| Parameter Count | 1.98B (Quantized) |
| Model Type | Instruction-tuned LLM |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
| License | Llama 3.1 |
| Quantization | 4-bit AWQ |
What is Meta-Llama-3.1-8B-Instruct-AWQ-INT4?
This is a community-driven quantized version of Meta's Llama 3.1 8B model, optimized for efficient deployment while maintaining performance. The model has been quantized from FP16 to INT4 using AutoAWQ, significantly reducing its memory footprint to require only 4GB of VRAM for inference.
Implementation Details
The model utilizes GEMM kernels with zero-point quantization and a group size of 128. It's built on the transformers architecture and supports multiple inference frameworks including Transformers, AutoAWQ, Text Generation Inference (TGI), and vLLM.
- Optimized for multilingual dialogue use cases
- Supports 8 different languages
- Requires approximately 4GB VRAM for model loading
- Compatible with various deployment options
Core Capabilities
- Efficient multilingual text generation
- Reduced memory footprint through 4-bit quantization
- Support for chat-based applications
- Integration with popular inference frameworks
- Batch processing and streaming capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization while maintaining the multilingual capabilities of the original Llama 3.1 model. It offers a practical balance between performance and resource requirements, making it accessible for deployment on consumer-grade hardware.
Q: What are the recommended use cases?
The model is particularly well-suited for multilingual dialogue applications, chatbots, and text generation tasks where resource efficiency is crucial. It's ideal for deployments where VRAM is limited but multilingual capability is required.
