--- library_name: transformers tags: - qwen - biomedical - bioinformatics - fine-tuned - medical - llm license: apache-2.0 base_model: - Qwen/Qwen2.5-1.5B --- # Qwen2.5-1.5B Biomedical Fine-Tuned Model This model is a biomedical and bioinformatics fine-tuned version of **Qwen/Qwen2.5-1.5B**, fine-tuned by **Dr. YMG**. --- ## Model Details ### Model Description This model is a domain-adapted and instruction fine-tuned large language model specialized for biomedical and bioinformatics tasks. - Developed by: Dr. YMG - Model type: Causal Language Model (LLM) - Language(s): English - License: Apache 2.0 - Finetuned from model: Qwen/Qwen2.5-1.5B ### Model Sources - Repository: https://huggingface.co/yashm/qwen25-15b-biomed-finetuned - Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B --- ## Uses ### Direct Use - Biomedical concept explanation - Bioinformatics discussions - Research assistance - Literature summarization - Gene expression & biomarker discussion ### Out-of-Scope Use - Clinical diagnosis - Medical treatment decisions - Drug prescription - Patient-specific advice --- ## Example Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline import torch MODEL_ID = "yashm/qwen25-15b-biomed-finetuned" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( MODEL_ID, device_map="auto", dtype=torch.bfloat16, trust_remote_code=True, ) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) prompt = "Explain gene expression in simple terms." out = pipe(prompt, max_new_tokens=200) print(out[0]["generated_text"]) ``` --- ## Training Details - Base model: Qwen/Qwen2.5-1.5B - Method: LoRA (PEFT) - Precision: BF16 - Quantization: 4-bit QLoRA --- ## Limitations - May hallucinate - Not medically validated - Limited to training data --- ## Disclaimer For research and educational use only. Not for clinical decision-making. --- ## Author Fine-tuned by Dr. YMG