Llama-3.1-8B-Instruct-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Base Model | Meta Llama 3.1 8B |
| Context Length | 128k tokens |
| License | Llama 3.1 Community License |
| Knowledge Cutoff | December 2023 |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
What is Llama-3.1-8B-Instruct-unsloth-bnb-4bit?
This is an optimized version of Meta's Llama 3.1 8B instruction-tuned model, implementing 4-bit quantization and the unsloth acceleration framework. The model achieves significant performance improvements, running 2.4x faster while using 58% less memory compared to the standard implementation. It maintains the core capabilities of Llama 3.1 while making it more accessible for deployment on consumer hardware.
Implementation Details
The model utilizes advanced optimization techniques including 4-bit quantization and the unsloth framework for improved efficiency. It's designed for easy integration with the transformers library and supports both regular text generation and tool use capabilities.
- 4-bit quantization for reduced memory footprint
- Unsloth acceleration framework integration
- Compatible with transformers library ≥ 4.43.0
- Supports multiple tool use formats
- 128k context window
Core Capabilities
- Multilingual text generation in 8 supported languages
- Instruction-following and chat applications
- Tool use and function calling
- Code generation and completion
- Long-context understanding
Frequently Asked Questions
Q: What makes this model unique?
The combination of Meta's Llama 3.1 architecture with unsloth's optimization techniques creates a highly efficient model that maintains performance while significantly reducing computational requirements. The 2.4x speed improvement and 58% memory reduction make it particularly suitable for deployment on consumer hardware.
Q: What are the recommended use cases?
The model is well-suited for chat applications, coding assistance, tool integration, and multilingual text generation. It's particularly valuable for developers looking to deploy large language models with limited computational resources while maintaining high performance.
