rohitnagareddy/Qwen3-0.6B-Medical-Finetuned-v1
The rohitnagareddy/Qwen3-0.6B-Medical-Finetuned-v1 model is a 0.8 billion parameter language model based on the Qwen3-0.6B architecture, fine-tuned using LoRA. It is specifically optimized for medical question-answering, providing information on common health topics. This model is designed to act as a helpful medical assistant, emphasizing the importance of professional medical consultation. It features a 32768 token context length and is available in various GGUF quantized versions for flexible deployment.
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Model Overview
The rohitnagareddy/Qwen3-0.6B-Medical-Finetuned-v1 is a specialized language model built upon the Qwen/Qwen3-0.6B base architecture. It has been fine-tuned using Low-Rank Adaptation (LoRA) on a custom medical Q&A dataset, focusing on common health topics. The model's primary function is to serve as a conversational medical assistant, providing accurate, evidence-based information.
Key Capabilities
- Medical Question Answering: Designed to answer questions related to general medical information and common health concerns.
- Conversational Assistance: Optimized for interactive dialogue in a medical context.
- Disclaimer Integration: Emphasizes the necessity of consulting qualified healthcare professionals and is not a substitute for medical advice.
- Quantized Versions: Available in multiple GGUF formats (FP16, Q8_0, Q5_K_M, Q4_K_M) for varying performance and resource requirements, compatible with
llama.cppand Ollama.
Training Details
The model underwent 2 training epochs with a batch size of 2 (and 4 steps of gradient accumulation), using a learning rate of 2e-4 and the Paged AdamW 32-bit optimizer. LoRA parameters included a rank of 16 and an alpha of 32, targeting auto-detected linear layers.
Important Note
This model is explicitly not intended for professional medical advice, diagnosis, or treatment, nor for emergency situations. Users are strongly advised to consult healthcare providers for medical concerns.