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Qwen 2.5 7B Base RAG RL
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Qwen 2.5 7B Base RAG RL

Developed by XXsongLALA
Qwen-2.5-7B-base-RAG-RL is a large language model with 7B parameters trained from scratch on an unknown dataset, incorporating Retrieval-Augmented Generation (RAG) and Reinforcement Learning (RL) technologies.
Large Language Model
Transformers
#Retrieval-Augmented Generation#Reinforcement Learning Fine-tuning#7B Parameters
Downloads 859
Release Time : 4/25/2025

Model Overview

This model is a large language model that combines Retrieval-Augmented Generation and Reinforcement Learning techniques, suitable for task scenarios requiring external knowledge retrieval and optimized generation results.

Model Features

Retrieval-Augmented Generation (RAG)
Incorporates external knowledge retrieval capabilities to improve the accuracy and relevance of generated content
Reinforcement Learning Optimization
Uses reinforcement learning techniques to optimize generation results
7B Parameter Scale
A medium-scale parameter model that balances performance and computational resource requirements

Model Capabilities

Text Generation
Knowledge Retrieval
Reinforcement Learning Optimization

Use Cases

Knowledge-Intensive Tasks
Question Answering System
Building intelligent question-answering systems by integrating external knowledge bases
Content Generation
Generating high-quality content based on retrieved information
Research Applications
RAG Technology Research
Used for research and experimentation on retrieval-augmented generation technology
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