medical-ner-qwen25-3b

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🔬 Medical Named Entity Recognition

Qwen2.5-3B fine-tuned for medical named entity recognition

Part of the Medical AI Fine-tuned Model Suite — 16 specialist models, one per task

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TL;DR

Extracts structured medical entities — conditions, drugs, dosages, procedures — from free-text clinical notes as JSON.

INPUT:  Patient diagnosed with type 2 diabetes, started on Metformin 500mg twice daily, scheduled for retinal screening.
OUTPUT: {"conditions": ["type 2 diabetes"], "drugs": ["Metformin"], "dosages": ["500mg twice daily"], "procedures": ["retinal screening"]}

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Architecture

                  +-------------------------+
  user prompt --> |  Qwen2.5-3B-Instruct  | --> base weights (frozen, 4-bit NF4)
                  |  + LoRA adapter (r=16)  | --> medical-ner-qwen25-3b
                  +-------------------------+
                              |
                              v
                     structured output
                  (code / JSON / classification)

This repo contains only the LoRA adapter (~60MB), not the full merged weights. Load it on top of the base model as shown below — this keeps the download small and lets you swap adapters on one base model in memory.

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Intended use

Build structured data extraction pipelines for EHR systems and clinical research data abstraction.

Direct use

Paste clinical text, get structured entities as JSON.

Downstream use

Feed into a research data abstraction tool, a clinical data warehouse ETL job, or a cohort-finding query engine.

Out of scope

Entity linking to external ontology codes — this model extracts entity text spans, it does not map them to SNOMED/RxNorm codes.

This model is not a substitute for a certified medical professional's judgment. Output should be reviewed by a qualified person before being used in a clinical or billing decision.

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Quickstart

Option A — Transformers + PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = "unsloth/Qwen2.5-3B-Instruct"
adapter    = "AmareshHebbar/medical-ner-qwen25-3b"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)

messages = [
    {"role": "system", "content": "You are a clinical NLP system. Extract all medical entities from the text and return them as JSON: {conditions, drugs, dosages, procedures}."},
    {"role": "user", "content": "Patient diagnosed with type 2 diabetes, started on Metformin 500mg twice daily, scheduled for retinal screening."},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=128, temperature=0.1, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

Expected output:

{"conditions": ["type 2 diabetes"], "drugs": ["Metformin"], "dosages": ["500mg twice daily"], "procedures": ["retinal screening"]}

Option B — Unsloth (2x faster load + inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="AmareshHebbar/medical-ner-qwen25-3b",
    max_seq_length=512,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

messages = [
    {"role": "system", "content": "You are a clinical NLP system. Extract all medical entities from the text and return them as JSON: {conditions, drugs, dosages, procedures}."},
    {"role": "user", "content": "Patient with hypertension prescribed Lisinopril 10mg daily."},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.1, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Option C — vLLM (production serving, OpenAI-compatible)

vllm serve unsloth/Qwen2.5-3B-Instruct \
    --enable-lora \
    --lora-modules medical-ner-qwen25-3b=AmareshHebbar/medical-ner-qwen25-3b \
    --host 0.0.0.0 --port 8000 --dtype bfloat16

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
response = client.chat.completions.create(
    model="medical-ner-qwen25-3b",
    messages=[
        {"role": "system", "content": "You are a clinical NLP system. Extract all medical entities from the text and return them as JSON: {conditions, drugs, dosages, procedures}."},
        {"role": "user", "content": "Post-MI patient on Aspirin 81mg, scheduled for cardiac catheterization."},
    ],
    temperature=0.1,
)
print(response.choices[0].message.content)

Option D — GGUF / llama.cpp (CPU / edge inference)

This repo ships LoRA adapter weights, not a pre-merged GGUF. To run on llama.cpp, merge first:

pip install unsloth
python -c "
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained('AmareshHebbar/medical-ner-qwen25-3b', load_in_4bit=False)
model.save_pretrained_gguf('medical-ner-qwen25-3b-gguf', tokenizer, quantization_method='q4_k_m')
"

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Training details

Data

Trained on 16,671 examples extracted from real clinical notes filtered for medication, diagnosis, and procedure mentions (source). No synthetic or LLM-generated training data — every example pairs real-world input with its authoritative output.

Full extraction pipeline documented on the dataset card.

Hyperparameters

Training compute

Fine-tuned with Unsloth for 2x faster training and reduced VRAM, using TRL's SFTTrainer. Full project: wandb.ai/amareshhebbar-/axiomapper.

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Bias, risks & limitations

Data recency. Training data reflects a specific snapshot in time (CMS FY2026 / dataset publish date). Codes, rates, and rules referenced may become outdated as source authorities issue updates — always cross-check against the live authoritative source before high-stakes use.

Failure mode. Like any LLM, this model can produce a plausible-sounding but incorrect output, especially on rare, ambiguous, or highly compound real-world cases that fall outside the training distribution. It does not know when it's wrong.

Language. English-language input only (Hindi-medical model excepted, where Hindi system prompts are used but underlying clinical reasoning data is largely English-sourced).

Not a regulated medical device. This model has not been validated, cleared, or approved by any regulatory body (FDA, CDSCO, or equivalent) as a medical device or clinical decision support tool. It is a research/engineering artifact.

Misapplication risk. Do not use this model as the sole basis for a clinical, billing, or compliance decision affecting a real patient or claim. Do not deploy in an emergency triage context without a human-in-the-loop and clear escalation paths.

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FAQ

Q: Can I merge the adapter into the base model for faster inference? Yes — use model.merge_and_unload() after loading with PEFT, or use Unsloth's save_pretrained_merged() method.

Q: Why QLoRA instead of full fine-tuning? The base model already has strong language and medical knowledge from pretraining. QLoRA adapts only ~0.5-1% of parameters, which is enough to specialize the output format and domain without the cost or overfitting risk of full fine-tuning.

Q: Can I fine-tune this further on my own data? Yes, this adapter can be used as a starting checkpoint for continued fine-tuning. Note this may require merging first depending on your training framework.

Q: Why is the output format so strict? Each task was trained on a fixed system prompt and consistent output structure. Following the documented system prompt closely (see Quickstart above) gives the most reliable results — deviating from it may produce inconsistent formatting.

Q: Does this model store or transmit my input data? No. Like any open-weight model, all inference happens locally on your own infrastructure (or wherever you deploy it) — nothing is sent back to the model author.

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Troubleshooting

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Related models in this suite

Full suite overview: AmareshHebbar/medical-ai-model-suite

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Changelog

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Citation

@misc{medicalai2026,
  author    = {Hebbar, Amaresh},
  title     = {Medical AI Fine-tuning Suite},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/AmareshHebbar}
}

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