Qwen2-0.5B-Reward Open-Source Reward Model - Free Evaluation and Optimization of Content Generation Quality

Qwen2 0.5B Reward

Developed by trl-lib

A reward model fine-tuned based on Qwen/Qwen2-0.5B-Instruct, used to evaluate and optimize the quality of generated content

Large Language Model

Transformers

Open Source License:Apache-2.0 #Reward Model Fine-tuning #Instruction Optimization #Multi-GPU Efficient Training

Downloads 916

Release Time : 9/5/2024

Model Overview

This model is a reward model fine-tuned based on Qwen2-0.5B-Instruct, primarily used to assess the quality of generated content and can serve as a reward signal in reinforcement learning. It achieved an accuracy of 0.728 on the evaluation dataset.

Model Features

High Accuracy Evaluation

Achieves 0.728 accuracy on the evaluation dataset, effectively assessing the quality of generated content

Optimized for Reinforcement Learning

Designed for reinforcement learning training, can serve as a reward signal to optimize generative models

Efficient Fine-tuning

Efficiently fine-tuned based on Qwen2-0.5B-Instruct, retaining the powerful capabilities of the base model

Model Capabilities

Text Quality Scoring

Generated Content Evaluation

Reinforcement Learning Reward Signal Generation

Use Cases

Content Generation Optimization

Dialogue System Optimization

Used to evaluate and optimize the quality of responses in dialogue systems

Can improve the relevance and coherence of dialogue systems

Text Generation Quality Control

Evaluates the quality of generated text and provides feedback to the generative model

Helps generate higher-quality content

Reinforcement Learning

RLHF Training

Serves as a reward model for Reinforcement Learning from Human Feedback (RLHF)

Replaces manual annotation, reducing training costs

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6444	0.0516	50	0.6037	0.672
0.5825	0.1032	100	0.5859	0.682
0.5732	0.1548	150	0.5751	0.704
0.5494	0.2064	200	0.5514	0.701
0.5654	0.2580	250	0.5427	0.709
0.5514	0.3096	300	0.5309	0.723
0.537	0.3612	350	0.5259	0.735
0.5236	0.4128	400	0.5368	0.714
0.536	0.4644	450	0.5451	0.726
0.5236	0.5160	500	0.5371	0.727
0.526	0.5676	550	0.5293	0.729
0.5197	0.6192	600	0.5239	0.727
0.525	0.6708	650	0.5227	0.732
0.5123	0.7224	700	0.5206	0.723
0.5171	0.7740	750	0.5237	0.718
0.5156	0.8256	800	0.5245	0.722
0.5115	0.8772	850	0.5234	0.723
0.5007	0.9288	900	0.5207	0.729
0.5018	0.9804	950	0.5182	0.728

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Qwen2 0.5B Reward

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Qwen2-0.5B-Reward

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

🔧 Technical Details

Training procedure

Training hyperparameters

Training results

Framework versions

📄 License