OpenChat 3.5 0106 GGUF
| Property | Value |
|---|---|
| Parameter Count | 7B |
| License | Apache 2.0 |
| Context Length | 8192 tokens |
| Paper | arXiv:2309.11235 |
What is openchat-3.5-0106-GGUF?
OpenChat 3.5 0106 GGUF is a state-of-the-art language model that has been optimized for both CPU and GPU inference through GGUF quantization. This model stands out as the best performing open-source 7B model, surpassing ChatGPT (March) and Grok-1 in various benchmarks. The model comes in multiple quantization versions, from 2-bit to 8-bit precision, allowing users to balance between performance and resource requirements.
Implementation Details
The model utilizes advanced quantization techniques with multiple variants available: Q2_K through Q8_0. The recommended Q4_K_M variant offers an excellent balance between model size (4.37GB) and performance quality. The model supports context lengths up to 8192 tokens and can be deployed using various frameworks including llama.cpp, text-generation-webui, and Python libraries.
- Multiple quantization options (2-bit to 8-bit)
- GPU layer offloading support
- OpenAI-compatible API server capability
- Integrated chat template system
Core Capabilities
- Advanced coding and mathematical reasoning abilities
- Outperforms many larger models in benchmarks
- 71.3% score on HumanEval
- 77.4% accuracy on GSM8K
- Specialized modes for coding and mathematical tasks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional performance despite its relatively small size (7B parameters). It achieves state-of-the-art results across multiple benchmarks and offers specialized modes for different tasks, making it versatile for various applications.
Q: What are the recommended use cases?
The model excels in coding tasks, mathematical reasoning, general chat interactions, and problem-solving scenarios. It's particularly well-suited for applications requiring both high performance and resource efficiency, with different quantization options available to match specific hardware constraints.
