Ziya-LLaMA-7B-Reward
| Property | Value |
|---|---|
| Base Architecture | LLaMA 7B |
| License | GPL |
| Developer | IDEA-CCNL |
| Primary Use | Text Quality Assessment |
What is Ziya-LLaMA-7B-Reward?
Ziya-LLaMA-7B-Reward is a specialized reward model built on the LLaMA architecture, designed to evaluate the quality of text generations in both Chinese and English. The model has been trained on an extensive dataset comprising 40,190 self-labeled high-quality preference ranking samples and 3,600 carefully filtered external samples from renowned sources like OpenAssistant, Anthropic HH-RLHF, and GPT-4-LLM.
Implementation Details
The model utilizes the Transformers framework and PyTorch backend, implementing a sequence classification architecture to provide numerical reward scores for input text. It's optimized for efficient inference and can process texts up to 1024 tokens in length.
- Built on LLaMA 7B foundation model
- Supports both Chinese and English text evaluation
- Implements reward scoring through sequence classification
- Utilizes custom tokenization with LlamaTokenizer
Core Capabilities
- Accurate assessment of text quality and adherence to instructions
- Detection of text repetition and abnormal interruptions
- Comparative evaluation of multiple responses to the same prompt
- Bilingual reward scoring for Chinese and English content
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to provide accurate reward feedback for text generation in both Chinese and English, trained on a carefully curated dataset of preference rankings, makes it particularly valuable for evaluating language model outputs.
Q: What are the recommended use cases?
The model is ideal for evaluating the quality of language model outputs, comparing different responses to the same prompt, and detecting common issues like repetition or incomplete responses. It's particularly useful in reinforcement learning pipelines for training language models.
