Yarn-Mistral-7B-128k-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.24B |
| License | Apache 2.0 |
| Paper | Research Paper |
| Context Length | 128,000 tokens |
What is Yarn-Mistral-7B-128k-GGUF?
Yarn-Mistral-7B-128k-GGUF is a quantized version of the NousResearch Yarn-Mistral model, specifically optimized for extended context processing up to 128,000 tokens. This model represents a significant advancement in long-context language models, built upon the Mistral-7B architecture and enhanced using the YaRN extension method.
Implementation Details
The model comes in various quantization formats (Q2_K through Q8_0) to accommodate different hardware configurations and use cases. The quantization allows for efficient deployment while maintaining performance, with file sizes ranging from 3.08GB to 7.70GB depending on the chosen format.
- Multiple quantization options for different performance/size tradeoffs
- Supports both CPU and GPU inference
- Compatible with popular frameworks like llama.cpp
- Optimized for long-context processing with minimal quality degradation
Core Capabilities
- Extended context processing up to 128k tokens
- Strong performance on long-context tasks with competitive perplexity scores
- Maintains reasonable performance on standard benchmarks (ARC-c, Hellaswag, MMLU)
- Efficient deployment options through various quantization levels
Frequently Asked Questions
Q: What makes this model unique?
This model's distinctive feature is its ability to handle extremely long contexts (up to 128k tokens) while maintaining good performance metrics. It achieves this through the YaRN extension method while offering various quantization options for efficient deployment.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring long-context understanding such as document analysis, extended conversations, and complex reasoning tasks. The Q4_K_M quantization is recommended for a good balance of quality and resource usage.
