Qwen 2.5 7B Fabric Expert
This model is a specialized version of Qwen-2.5-7B-Instruct, optimized through fine-tuning (LoRA) to provide expert-level answers regarding Hyperledger Fabric.
Objective
The goal of this project is to demonstrate how a Small Language Model (SLM) can achieve high precision in vertical technical domains. This approach reduces computational costs while enabling local execution, ensuring a privacy-first deployment.
Key Features
- Domain: Hyperledger Fabric (Architecture, Chaincode Development, Network Operations).
- Method: Supervised Fine-Tuning (SFT) on a curated dataset of 200 technical instructions.
- Use Case: Ideal for documentation assistants, developer support, or as a technical knowledge base.
Technical Notes
For optimal performance on hardware with limited VRAM (e.g., NVIDIA Tesla T4), loading via bitsandbytes in 4-bit precision is highly recommended.
