xunnhi/Qwen2.5-7B-RAG-LoRA

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Jun 10, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The xunnhi/Qwen2.5-7B-RAG-LoRA is a 7.6 billion parameter Qwen2.5 model, fine-tuned by xunnhi, leveraging Unsloth for accelerated training. This model is specifically optimized for Retrieval Augmented Generation (RAG) tasks, building upon the Qwen2.5-7B-Instruct base. It offers a 32K context length, making it suitable for applications requiring processing of extensive documents and generating contextually relevant responses.

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xunnhi/Qwen2.5-7B-RAG-LoRA Overview

This model is a 7.6 billion parameter variant of the Qwen2.5 architecture, fine-tuned by xunnhi. It is built upon the unsloth/Qwen2.5-7B-Instruct-bnb-4bit base model and was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster fine-tuning process. The primary focus of this fine-tuning is on Retrieval Augmented Generation (RAG) capabilities.

Key Capabilities

  • Efficient Fine-tuning: Utilizes Unsloth for significantly faster training, making it resource-efficient for developers.
  • Qwen2.5 Architecture: Benefits from the robust base capabilities of the Qwen2.5 instruction-tuned model.
  • RAG Optimization: Specifically tailored for tasks that involve retrieving information from a knowledge base and generating responses based on that retrieved context.
  • Extended Context Window: Supports a 32,768 token context length, allowing for the processing of large documents or conversational histories.

Good For

  • Applications requiring accurate information retrieval and synthesis.
  • Building chatbots or question-answering systems that need to consult external data sources.
  • Scenarios where efficient fine-tuning and deployment of a RAG-optimized model are crucial.

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