Llama-3.2-1B-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 765M parameters |
| License | Llama 3.2 Community License |
| Author | Unsloth |
| Release Date | September 25, 2024 |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
What is Llama-3.2-1B-bnb-4bit?
Llama-3.2-1B-bnb-4bit is a 4-bit quantized version of Meta's Llama 3.2 language model, optimized by Unsloth for efficient inference and fine-tuning. This model represents a significant advancement in making large language models more accessible and resource-efficient, offering 2.4x faster performance with 58% less memory usage compared to standard implementations.
Implementation Details
The model leverages bitsandbytes quantization techniques to compress the original Llama 3.2 architecture while maintaining performance. It uses Grouped-Query Attention (GQA) for improved inference scalability and supports multiple tensor types including F32, BF16, and U8.
- Optimized for 4-bit precision using bitsandbytes
- Implements Grouped-Query Attention mechanism
- Supports fine-tuning with 70% less memory usage
- Compatible with GGUF and vLLM export options
Core Capabilities
- Multilingual text generation and dialogue
- Agentic retrieval and summarization tasks
- Efficient fine-tuning on custom datasets
- Optimized for resource-constrained environments
- Compatible with various deployment options
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its optimized 4-bit quantization, which enables significant performance improvements while maintaining model quality. It achieves 2.4x faster operation with 58% less memory usage, making it ideal for resource-constrained environments.
Q: What are the recommended use cases?
The model is particularly well-suited for multilingual dialogue applications, text generation tasks, and scenarios requiring efficient resource utilization. It's ideal for developers looking to fine-tune on custom datasets while maintaining low computational overhead.
