| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Context Length | 8192 tokens |
| License | Apache-2.0 |
| Research Paper | arXiv:2309.11235 |
| MT-Bench Score | 7.81 |
What is OpenChat 3.5?
OpenChat 3.5 is a groundbreaking open-source language model that achieves performance comparable to ChatGPT despite using only 7B parameters. It's fine-tuned using C-RLFT (a strategy inspired by offline reinforcement learning) and can process mixed-quality data without requiring preference labels. The model demonstrates exceptional capabilities across various benchmarks, notably achieving the #1 position among open-source models on MT-bench with a score of 7.81.
Implementation Details
The model is implemented using a sophisticated architecture that supports high-throughput deployment through vLLM, capable of running on consumer GPUs with 24GB RAM. It includes tensor parallelism capabilities and provides an OpenAI-compatible API server for easy integration.
- Supports both chat and coding modes with specialized templates
- Implements an 8192 token context window
- Features built-in conversation templates for seamless integration
- Provides OpenAI-compatible API endpoints
Core Capabilities
- Achieves 64.3% accuracy on MMLU benchmarks
- Scores 55.5% on HumanEval coding tasks
- Demonstrates 77.3% accuracy on GSM8k mathematical reasoning
- Supports both single-turn and multi-turn conversations
- Excels in coding tasks with specialized coding mode
Frequently Asked Questions
Q: What makes this model unique?
OpenChat 3.5's ability to achieve ChatGPT-level performance with only 7B parameters through innovative C-RLFT training makes it stand out. It outperforms many larger models, including some 70B parameter ones, while maintaining open-source accessibility.
Q: What are the recommended use cases?
The model excels in general conversational tasks, coding assistance, and mathematical reasoning. It's particularly suitable for applications requiring high-performance language understanding within resource constraints, though users should be aware of typical LLM limitations regarding hallucinations and safety considerations.