meta-Llama-3.1-8B-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Base Model | Llama 3.1 8B |
| Context Length | 128k tokens |
| License | Llama 3.1 Community License |
| Training Data | 15T+ tokens |
| Knowledge Cutoff | December 2023 |
What is meta-Llama-3.1-8B-unsloth-bnb-4bit?
This is an optimized version of Meta's Llama 3.1 8B parameter model, quantized to 4-bit precision using unsloth's efficient implementation. The model maintains the powerful capabilities of the original while significantly reducing memory usage and increasing inference speed. It's designed for both research and commercial applications, supporting multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Implementation Details
The model uses 4-bit quantization with binary neural networks (BNB) optimization, making it highly efficient for deployment. It leverages the transformer architecture with Grouped-Query Attention (GQA) for improved inference scalability. The implementation includes special optimizations that result in up to 2.4x faster inference and 58% less memory usage compared to standard implementations.
- Optimized for both transformers and original llama codebase compatibility
- Supports various deployment options including torch.compile() and quantized inference
- Includes comprehensive tool use capabilities with multiple formats
- Features built-in chat templates and efficient text generation pipeline
Core Capabilities
- Multilingual text generation in 8 supported languages
- 128k context length for handling long documents
- Advanced tool use and function calling capabilities
- Strong performance on benchmarks including MMLU, GSM-8K, and HumanEval
- Efficient memory usage through 4-bit quantization
Frequently Asked Questions
Q: What makes this model unique?
The model combines Meta's latest Llama 3.1 architecture with unsloth's efficient 4-bit quantization, offering state-of-the-art performance while being significantly more resource-efficient. It's especially notable for its balance of capability and efficiency.
Q: What are the recommended use cases?
The model is well-suited for commercial and research applications including chatbots, code generation, tool integration, and multilingual text processing. It's particularly effective for deployments where resource efficiency is crucial while maintaining high performance.
