Meta-Llama-3.3-70B-Instruct-AWQ-INT4
| Property | Value |
|---|---|
| Original Model Size | 70B Parameters |
| Quantization | 4-bit AWQ |
| VRAM Requirement | ~35 GiB |
| Model Hub | Hugging Face |
What is Meta-Llama-3.3-70B-Instruct-AWQ-INT4?
This is a quantized version of Meta's Llama 3.3 70B Instruct model, compressed from FP16 to INT4 using AWQ (Activation-aware Weight Quantization) technology. The model preserves the multilingual capabilities and instruction-following abilities of the original while significantly reducing its memory footprint.
Implementation Details
The quantization was performed using AutoAWQ with GEMM kernels, featuring zero-point quantization and a group size of 128. The model was quantized on hardware consisting of an Intel Xeon CPU E5-2699A v4, 256GB RAM, and dual NVIDIA RTX 3090 GPUs.
- Supports multiple inference frameworks: Transformers, AutoAWQ, TGI, and vLLM
- Requires approximately 35 GiB VRAM for model loading
- Implements chat templating for structured conversations
- Optimized for multilingual dialogue applications
Core Capabilities
- Efficient multilingual text generation and dialogue
- Instruction following with reduced memory footprint
- Compatible with major deployment frameworks
- Supports both chat and completion-style interactions
- Maintains performance while reducing resource requirements
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization of the powerful Llama 3.3 70B model, making it accessible on consumer-grade hardware while maintaining performance. The AWQ quantization method ensures minimal quality degradation compared to the original model.
Q: What are the recommended use cases?
The model is ideal for production deployments requiring multilingual capabilities where memory efficiency is crucial. It's particularly well-suited for applications in dialogue systems, content generation, and other NLP tasks where the full precision model would be too resource-intensive.
