OpenChat 3.5 GGUF
| Property | Value |
|---|---|
| Base Model Size | 7B Parameters |
| Context Length | 8192 tokens |
| License | Apache License 2.0 |
| Paper | arXiv:2309.11235 |
| MT-Bench Score | 7.81 |
What is openchat_3.5-GGUF?
OpenChat 3.5 GGUF is a quantized version of the OpenChat 3.5 7B model, optimized for efficient deployment across various hardware configurations. This model represents a significant achievement in open-source language models, matching ChatGPT's capabilities while maintaining a relatively small 7B parameter size. The GGUF format enables flexible deployment options with multiple quantization levels from 2-bit to 8-bit precision.
Implementation Details
The model comes in various quantization formats optimized for different use cases, from the lightweight Q2_K (3.08 GB) to the high-fidelity Q8_0 (7.70 GB). It supports GPU acceleration through libraries like llama.cpp and can be integrated with popular frameworks including text-generation-webui, KoboldCpp, and LM Studio.
- Multiple quantization options (Q2_K to Q8_0) for different size/quality trade-offs
- GPU acceleration support with layer offloading
- Compatible with major LLM frameworks and interfaces
- Optimized for both CPU and GPU inference
Core Capabilities
- Achieves 7.81 on MT-bench, outperforming many larger models
- Supports context length of 8192 tokens
- Excellent performance on various benchmarks including AGIEval, BBH MC, and TruthfulQA
- Specialized coding mode for programming tasks
- Efficient serving capabilities through vLLM
Frequently Asked Questions
Q: What makes this model unique?
OpenChat 3.5 stands out for achieving ChatGPT-comparable performance in a 7B parameter model, making it highly efficient for deployment. The GGUF format adds flexibility in deployment options while maintaining high performance.
Q: What are the recommended use cases?
The model excels in general conversation, coding tasks, and various benchmark evaluations. For optimal performance-to-size ratio, the Q4_K_M quantization is recommended for most uses, while Q5_K_M or Q6_K are suggested for maximum quality.
