Llama-3.1-8B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8 Billion |
| Context Length | 128k tokens |
| Training Data | 15T+ tokens |
| Knowledge Cutoff | December 2023 |
| License | Llama 3.1 Community License |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
What is Llama-3.1-8B-Instruct-GGUF?
Llama-3.1-8B-Instruct-GGUF is Meta's latest 8B parameter instruction-tuned language model, optimized for multilingual dialogue and converted to the efficient GGUF format. It represents a significant advancement in accessible AI, offering strong performance across reasoning, tool use, and multilingual tasks while maintaining efficient resource usage.
Implementation Details
The model utilizes an optimized transformer architecture with Grouped-Query Attention (GQA) for improved inference scalability. It's been extensively trained on a diverse dataset of publicly available online content and fine-tuned with over 25M synthetic examples for instruction-following capabilities.
- Optimized for 8 primary languages with proven effectiveness
- 128k context window for handling long-form content
- GGUF format optimization for efficient deployment
- Comprehensive safety measures and responsible AI practices
Core Capabilities
- Strong performance in code generation (72.6% pass@1 on HumanEval)
- Advanced mathematical reasoning (84.5% accuracy on GSM-8K)
- Robust tool use capabilities (82.6% accuracy on API-Bank)
- Multilingual proficiency across supported languages
- Enhanced safety features and content filtering
Frequently Asked Questions
Q: What makes this model unique?
The model strikes an exceptional balance between size and capability, offering strong performance across multiple languages while maintaining efficient resource usage. Its GGUF format makes it particularly suitable for deployment in resource-constrained environments.
Q: What are the recommended use cases?
The model excels in assistant-like chat applications, code generation, mathematical reasoning, and tool-based interactions. It's particularly well-suited for multilingual applications requiring strong reasoning capabilities while maintaining efficiency.
