Shiva7706

Fine-tuned Model: RxStruct-Gemma-1B | Quantized Version: GGUF Release A fine-tuned variant of Gemma-3-1B-IT optimized for structured medical data extraction from natural, doctor-style prescription dialogues. This model outputs fully structured JSON containing medicines, dosages, diseases, tests, and instructions — without requiring external post-processing. | Property | Value | |-----------|--------| | Base Model | `google/gemma-3-1b-it` | | Fine-tuning Framework | Unsloth | | Method | LoRA (Rank=8, α=16, Dropout=0.05) | | Precision | bfloat16 | | Sequence Length | 1024 tokens | | Stop Token | `"AAA"` | | Parameters Trained | ~13M (1.29%) | | Dataset Source | Synthetic Claude 3.5 Sonnet-generated doctor–patient prescription conversations | | Output Format | Valid JSON object with fixed schema | json { "medicines": [ {"name": "Amlodipine", "dosage": "5mg", "frequency": "once daily", "duration": "unspecified", "route": "oral", "timing": "morning"}, {"name": "Atorvastatin", "dosage": "10mg", "frequency": "at bedtime", "duration": "unspecified", "route": "oral", "timing": "unspecified"} ], "diseases": ["high blood pressure"], "symptoms": ["high blood pressure"], "tests": [ {"name": "lipid profile", "timing": "after 1 month"}, {"name": "kidney function tests", "timing": "after 1 month"} ], "instructions": ["reduce salt intake", "exercise regularly"] } json { "medicines": [ {"name": "string", "dosage": "string", "frequency": "string", "duration": "string", "route": "string", "timing": "string"} ], "diseases": ["string"], "symptoms": ["string"], "tests": [{"name": "string", "timing": "string"}], "instructions": ["string"] } python import json, re def cleanjsonoutput(text): match = re.search(r"\{[\s\S]\}", text) if match: text = match.group(0) try: data = json.loads(text) if "AAA" in text: text = text.replace("AAA", "") return data except json.JSONDecodeError: return text[:text.rfind("}")+1] return None ``` Research on medical NLP and structured data extraction Building medical assistants that convert prescriptions to structured EHR-compatible data Educational and demonstration purposes Real-world clinical applications Diagnostic or treatment decision systems