Llama-3.2-3B-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Base Model | Meta Llama 3.2 (3B) |
| Release Date | September 25, 2024 |
| License | Llama 3.2 Community License |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
| Optimization | 4-bit quantization with Dynamic Quants |
What is Llama-3.2-3B-unsloth-bnb-4bit?
This is an optimized version of Meta's Llama 3.2 3B model, specifically quantized using Unsloth's Dynamic 4-bit Quantization technique. The model maintains high accuracy while significantly reducing memory footprint and increasing training speed. It's particularly notable for its selective quantization approach, where certain critical parameters are preserved at higher precision.
Implementation Details
The model utilizes an optimized transformer architecture with Grouped-Query Attention (GQA) for improved inference scalability. Unsloth's implementation achieves 2.4x faster training speeds while using 58% less memory compared to the original model. It's designed for both text completion and conversational tasks, supporting multiple fine-tuning approaches including supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
- Dynamic 4-bit quantization that selectively preserves important parameters
- Integrated support for ShareGPT ChatML and Vicuna templates
- Compatible with GGUF, vLLM exports and Hugging Face uploads
- Optimized for Google Colab Tesla T4 environments
Core Capabilities
- Multilingual dialogue processing across 8 officially supported languages
- Agentic retrieval and summarization tasks
- High-performance chat completion and text generation
- Efficient fine-tuning with reduced resource requirements
Frequently Asked Questions
Q: What makes this model unique?
The model combines Meta's Llama 3.2 architecture with Unsloth's innovative Dynamic 4-bit Quantization, offering significant performance improvements while maintaining model quality. The selective quantization approach sets it apart from standard 4-bit quantization methods.
Q: What are the recommended use cases?
The model is ideal for multilingual dialogue applications, text completion tasks, and scenarios requiring efficient fine-tuning with limited computational resources. It's particularly well-suited for deployment in environments where memory optimization is crucial while maintaining high-quality output.
