Meta-Llama-3.1-70B-Instruct-quantized.w4a16
| Property | Value |
|---|---|
| Parameter Count | 70B |
| Quantization | INT4 (4-bit precision) |
| License | Llama3.1 |
| Paper | GPTQ Paper |
| Languages Supported | 8 (en, de, fr, it, pt, hi, es, th) |
What is Meta-Llama-3.1-70B-Instruct-quantized.w4a16?
This is a highly optimized version of Meta's Llama 3.1 70B model, specifically designed for efficient deployment while maintaining near-original performance. The model employs 4-bit weight quantization, reducing disk and GPU memory requirements by approximately 75% compared to the original model.
Implementation Details
The model utilizes GPTQ quantization algorithm with symmetric per-channel quantization, applied specifically to linear operators within transformer blocks. The implementation achieved remarkable performance recovery across multiple benchmarks, including 100% recovery on Arena-Hard evaluation and 99.4% on OpenLLM v1.
- Quantization uses 1% damping factor
- Calibrated on 512 sequences of 8,192 tokens
- Supports deployment via vLLM backend
- Compatible with OpenAI-style serving
Core Capabilities
- Multiple-choice reasoning with 99.5% recovery on MMLU
- Mathematical reasoning with 99% recovery on GSM-8K
- Code generation with 101% recovery on HumanEval pass@1
- Supports 8 different languages for text generation
- Optimized for assistant-like chat applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for achieving extreme efficiency through 4-bit quantization while maintaining practically identical performance to the original 70B model. It's particularly noteworthy for its consistent performance across diverse tasks, from mathematical reasoning to code generation.
Q: What are the recommended use cases?
The model is best suited for commercial and research applications requiring assistant-like chat capabilities in English. It's particularly effective for tasks involving multiple-choice reasoning, mathematical problem-solving, and code generation, while requiring significantly less computational resources than the original model.
