OpenChat 3.5-0106
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Context Length | 8192 tokens |
| License | Apache 2.0 |
| Paper | arXiv:2309.11235 |
What is openchat-3.5-0106?
OpenChat 3.5-0106 represents a significant breakthrough in open-source language models, achieving remarkable performance that surpasses both ChatGPT (March) and Grok-1. This 7B parameter model demonstrates exceptional capabilities across various benchmarks, with a particular emphasis on coding and mathematical reasoning tasks. The model features two distinct operational modes: a default mode optimized for general tasks and coding, and a specialized mathematical reasoning mode.
Implementation Details
The model is built on advanced mixed-quality data training techniques and can be deployed using the OpenChat package with vLLM optimization. It supports tensor parallelism and can run on consumer GPUs with 24GB RAM. The model utilizes a sophisticated chat template system and includes experimental evaluator capabilities.
- Achieves 64.5% average score across major benchmarks
- Supports context length of 8192 tokens
- Implements OpenAI-compatible API server
- Features high-throughput deployment capabilities
Core Capabilities
- Superior coding performance with 71.3% on HumanEval
- Enhanced mathematical reasoning with 77.4% on GSM8K
- Strong performance in MT-Bench (7.8)
- Robust truthfulness metrics (61.0% on TruthfulQA)
- Advanced evaluator and feedback capabilities
Frequently Asked Questions
Q: What makes this model unique?
OpenChat 3.5-0106 stands out for achieving state-of-the-art performance in the 7B parameter category, particularly noteworthy for outperforming larger models like Grok-1 while maintaining open-source accessibility. Its dual-mode capability for both general tasks and mathematical reasoning makes it versatile for various applications.
Q: What are the recommended use cases?
The model excels in coding tasks, general conversational interactions, and mathematical problem-solving. It's particularly well-suited for software development, educational applications, and general-purpose AI assistance. The model also includes experimental evaluation capabilities, making it valuable for response quality assessment.
