Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503
| Property | Value |
|---|---|
| Parameter Count | 24 Billion |
| Context Window | 128,000 tokens |
| License | Apache 2.0 |
| Quantization | 6-bit |
| Model URL | https://huggingface.co/ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 |
What is Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503?
Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503 is an advanced language model that builds upon Mistral Small 3.1, incorporating innovative 6-bit quantization technology. This optimization enables efficient deployment on consumer hardware while maintaining high performance across both text and vision tasks. The model represents a significant advancement in making large language models more accessible for local deployment.
Implementation Details
The model employs a knowledge-dense architecture that can run on a single RTX 4090 or a 32GB RAM MacBook after quantization. It utilizes a Tekken tokenizer with a 131k vocabulary size and supports an impressive 128k token context window.
- 6-bit quantization for optimal memory efficiency
- Multilingual support for dozens of languages
- Native function calling and JSON output capabilities
- Vision analysis capabilities
- System prompt adherence
Core Capabilities
- Fast-response conversational interactions
- Low-latency function calling
- Extended document processing with 128k context
- Visual content analysis
- Programming and mathematical reasoning
- Multilingual processing across 24+ languages
- Local deployment for sensitive data handling
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its combination of 6-bit quantization technology with a large 24B parameter count, making it both powerful and efficient. It can run on consumer hardware while maintaining high performance across multiple domains including vision tasks.
Q: What are the recommended use cases?
The model excels in conversational AI, document processing, visual analysis, and programming tasks. It's particularly suitable for organizations handling sensitive data that requires local deployment, and for developers needing efficient large-model capabilities on consumer hardware.
