HasinduNimesh/qwen3b-finetuned
HasinduNimesh/qwen3b-finetuned is a 3.1 billion parameter Qwen2.5-3B-Instruct model, fine-tuned by HasinduNimesh using Unsloth. This model is specifically optimized for multi-hop reasoning, scientific question answering, and retrieval-augmented generation (RAG) using FAISS and BM25 retrieval. It is available in both 4-bit GGUF and 16-bit versions, making it suitable for efficient inference in specialized applications requiring advanced reasoning capabilities.
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Model Overview
This model, HasinduNimesh/qwen3b-finetuned, is a specialized fine-tuned version of the Qwen2.5-3B-Instruct base model. Developed by HasinduNimesh, it leverages Unsloth for efficient fine-tuning and is designed to excel in complex reasoning tasks.
Key Capabilities
- Multi-hop Reasoning: Optimized using datasets like HotpotQA to handle questions requiring multiple steps of inference.
- Scientific Q&A: Enhanced for answering scientific questions, potentially through its reasoning and retrieval capabilities.
- Retrieval-Augmented Generation (RAG): Integrates with FAISS and BM25 retrieval methods, allowing it to generate responses based on retrieved documents.
- Efficient Deployment: Available in both 4-bit GGUF (for Llama-CPP) and 16-bit versions, offering flexibility for various deployment environments.
Fine-Tuning Details
The model underwent a two-stage fine-tuning process:
- Supervised Fine-Tuning (SFT): Utilized datasets including HotpotQA and synthetic QA generated from document chunks.
- Offline Reward Preference Optimization (ORPO): Applied for further improving reasoning performance.
Good For
- Applications requiring advanced multi-hop reasoning.
- Systems needing to perform scientific question answering.
- Integrating with RAG pipelines for enhanced factual grounding.
- Deployment on resource-constrained devices using its quantized GGUF format.