CompassJudger-1-32B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 32.8B parameters |
| Base Model | Qwen2.5-32B-Instruct |
| License | Apache 2.0 |
| Paper | arXiv:2410.16256 |
| Tensor Type | BF16 |
What is CompassJudger-1-32B-Instruct?
CompassJudger-1-32B-Instruct is an advanced AI judge model designed to provide comprehensive evaluation capabilities across multiple assessment methods. Built on the Qwen2.5-32B architecture, it represents a significant advancement in AI evaluation technology, capable of performing scoring, comparisons, and detailed assessment feedback.
Implementation Details
The model is implemented using the Transformers library and supports various inference acceleration methods including vLLM and LMdeploy. It utilizes BF16 precision for optimal performance and efficiency.
- Comprehensive evaluation capabilities across multiple methods
- Formatted output support for structured assessment feedback
- Integration with popular acceleration frameworks
- Support for chat templates and structured dialogue
Core Capabilities
- General Chat: Capable of engaging in natural conversation while maintaining evaluative abilities
- Reward Modeling: Can function as a reward model for comparing dialogue quality
- Point-wise Judging: Provides detailed dimensional scoring and evaluation
- Pair-wise Comparison: Effectively compares responses from different AI models
- Response Critique: Offers constructive feedback and improvement suggestions
Frequently Asked Questions
Q: What makes this model unique?
CompassJudger-1-32B-Instruct stands out for its all-in-one approach to evaluation, combining multiple assessment methods with the ability to provide structured feedback and function as a general instruction model.
Q: What are the recommended use cases?
The model is ideal for AI response evaluation, model comparison, quality assessment, and general instruction following. It's particularly useful in research settings where objective evaluation of AI outputs is needed.
