Llama-3.2-3B-Instruct-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 1.85B |
| License | Llama 3.2 Community License |
| Author | Unsloth |
| Quantization | 4-bit precision |
| Release Date | September 25, 2024 |
What is Llama-3.2-3B-Instruct-bnb-4bit?
Llama-3.2-3B-Instruct-bnb-4bit is a quantized version of Meta's Llama 3.2 model, optimized for efficient deployment while maintaining performance. This version uses 4-bit quantization through bitsandbytes, achieving 2.4x faster inference and 58% reduced memory usage compared to the original model.
Implementation Details
The model utilizes an optimized transformer architecture with Grouped-Query Attention (GQA) for improved inference scalability. It's specifically designed for multilingual dialogue applications and has been instruction-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
- 4-bit precision quantization for efficient deployment
- Optimized transformer architecture with GQA
- Supports multiple tensor types: F32, BF16, U8
- Compatible with text-generation-inference endpoints
Core Capabilities
- Multilingual support for 8 primary languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
- Optimized for dialogue use cases and agentic tasks
- Enhanced performance in retrieval and summarization
- Significantly reduced memory footprint while maintaining model quality
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its efficient 4-bit quantization while maintaining the capabilities of the original Llama 3.2 architecture. It offers significant speed improvements and memory savings, making it more accessible for deployment on resource-constrained systems.
Q: What are the recommended use cases?
The model is particularly well-suited for multilingual dialogue applications, chatbots, content summarization, and retrieval tasks. It's optimized for deployment in production environments where resource efficiency is crucial while maintaining high-quality outputs.
