Mira-1

Key Features

• Structured JSON output — extracts patient demographics, vitals, labs, medications, diagnoses, procedures, allergies
• Source-grounded — every extracted value traces to the input document
• No patient identifiers — extracts age/sex only, strips names/MRN/DOB
• On-prem deployable — 3B parameters, runs on CPU via GGUF quantization
• 98% JSON validity on held-out gold set

Training

Evaluation (50 held-out gold examples)

Usage

python
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from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained("shekharp77/Mira-1")
tokenizer = AutoTokenizer.from_pretrained("shekharp77/Mira-1")

messages = [
    {"role": "system", "content": "You are a clinical information extraction system..."},
    {"role": "user", "content": "Patient: 45/M\nHb 12.5 g/dL (13-17) LOW\nWBC 8.2 x10^9/L (4-11) Normal"},
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=2048, temperature=0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Schema

Outputs conform to this schema (10 required top-level fields):

• document_type: lab_report | medication_list | discharge_summary | pathology_report | intake_form | progress_note | other
• patient: {age, sex}
• encounter: {date, department}
• vitals[], labs[], medications[], diagnoses[], procedures[], allergies[]
• extraction_notes

Limitations

• English only (v0)
• Trained on synthetic data (Synthea + curated seeds), not real clinical records
• Every output is a draft for human review — not for autonomous clinical decisions
• No ICD-10/SNOMED coding unless explicitly in the source document

License

Apache-2.0 (same as base model)