README.md

---
license: apache-2.0
tags:
  - medical
  - healthcare
  - collection
  - qlora
  - unsloth
  - qwen2.5
  - clinical-nlp
language:
  - en
---

# Medical AI Fine-tuned Model Suite

A collection of 16 [Qwen2.5](https://huggingface.co/Qwen) models fine-tuned with QLoRA, one per medical/healthcare task — ICD-10 coding, billing, clinical documentation, India's PM-JAY scheme, and more. Built by [Amaresh Hebbar](https://huggingface.co/AmareshHebbar).

**Collection page:** [link to your HF collection here]

Every model uses the same approach: a real public data source (CMS, NHA India, peer-reviewed biomedical corpora — no synthetic or LLM-generated training data), QLoRA fine-tuning on Qwen2.5, and a narrow, well-defined task with a strict system prompt. The goal is a set of small, deployable specialists rather than one large general-purpose medical model.

## Why specialist models instead of one big model

A single general medical LLM has to be evaluated, monitored, and trusted across every task it might be asked to do. These models are scoped to one job each — ICD-10 coding, PM-JAY classification, radiology report coding — so each one is small enough to self-host cheaply, easy to evaluate against a clear ground truth, and safe to swap out independently if a better version ships later.

## The 16 models

| Model | Task | Base | Rows |
|---|---|---|---|
| [icd10-coder-qwen25-7b](https://huggingface.co/AmareshHebbar/icd10-coder-qwen25-7b) | ICD-10-CM medical coding | Qwen2.5-7B | 74,719 |
| [snomed-mapper-qwen25-7b](https://huggingface.co/AmareshHebbar/snomed-mapper-qwen25-7b) | Clinical terminology mapping | Qwen2.5-7B | 74,719 |
| [clinical-summarizer-qwen25-7b](https://huggingface.co/AmareshHebbar/clinical-summarizer-qwen25-7b) | SOAP note summarization | Qwen2.5-7B | 30,000 |
| [symptom-diagnoser-qwen25-7b](https://huggingface.co/AmareshHebbar/symptom-diagnoser-qwen25-7b) | Symptom → differential diagnosis | Qwen2.5-7B | 119,467 |
| [discharge-qa-qwen25-3b](https://huggingface.co/AmareshHebbar/discharge-qa-qwen25-3b) | Discharge summary Q&A | Qwen2.5-3B | 30,000 |
| [radiology-coder-qwen25-3b](https://huggingface.co/AmareshHebbar/radiology-coder-qwen25-3b) | Radiology report coding | Qwen2.5-3B | 25,090 |
| [medical-ner-qwen25-3b](https://huggingface.co/AmareshHebbar/medical-ner-qwen25-3b) | Clinical named entity recognition | Qwen2.5-3B | 16,671 |
| [hindi-medical-qwen25-3b](https://huggingface.co/AmareshHebbar/hindi-medical-qwen25-3b) | Hindi medical reasoning | Qwen2.5-3B | 19,704 |
| [cpt-coder-qwen25-3b](https://huggingface.co/AmareshHebbar/cpt-coder-qwen25-3b) | CPT/HCPCS procedure coding | Qwen2.5-3B | 17,029 |
| [medical-billing-qwen25-3b](https://huggingface.co/AmareshHebbar/medical-billing-qwen25-3b) | Medical billing assistant | Qwen2.5-3B | 17,029 |
| [pmjay-classifier-qwen25-3b](https://huggingface.co/AmareshHebbar/pmjay-classifier-qwen25-3b) | India PM-JAY package classification | Qwen2.5-3B | 11,140 |
| [pharmacy-ner-qwen25-1b](https://huggingface.co/AmareshHebbar/pharmacy-ner-qwen25-1b) | Drug entity extraction | Qwen2.5-1.5B | 3,500 |
| [ayurveda-icd-qwen25-1b](https://huggingface.co/AmareshHebbar/ayurveda-icd-qwen25-1b) | Ayurveda to ICD-10 bridge | Qwen2.5-1.5B | 3,002 |
| [insurance-classifier-qwen25-1b](https://huggingface.co/AmareshHebbar/insurance-classifier-qwen25-1b) | Stark Law DHS compliance | Qwen2.5-1.5B | 1,601 |
| [icd10-to-drg-qwen25-1b](https://huggingface.co/AmareshHebbar/icd10-to-drg-qwen25-1b) | ICD-10 → MS-DRG reimbursement | Qwen2.5-1.5B | 5,385 |
| [loinc-coder-qwen25-1b](https://huggingface.co/AmareshHebbar/loinc-coder-qwen25-1b) | Lab test CPT coding | Qwen2.5-1.5B | 2,179 |

## Training method

All 16 models share the same recipe:

| | |
|---|---|
| Fine-tuning method | QLoRA, 4-bit NF4 quantization, rank 16, alpha 32 |
| Training framework | [Unsloth](https://github.com/unslothai/unsloth) + TRL `SFTTrainer` |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Hardware | Single NVIDIA A40 (48GB) |
| Optimizer | paged_adamw_8bit, cosine LR schedule |
| Experiment tracking | [Weights & Biases](https://wandb.ai/amareshhebbar-/axiomapper) |

Model size is matched to dataset size — large datasets (>50k rows) get Qwen2.5-7B, mid-size (10k–50k) get Qwen2.5-3B, and smaller specialist datasets (<10k) get Qwen2.5-1.5B. This keeps inference cost proportional to task complexity instead of running every task through the same large model.

## Data sources

Every dataset behind these models is built from real authoritative public data — CMS (ICD-10-CM, MS-DRG, Physician Fee Schedule, HCPCS), NHA India (PM-JAY HBP 2022, PM RAHAT), and peer-reviewed biomedical corpora (chat_doctor, augmented-clinical-notes, drugprot). No synthetic or LLM-generated training data. Full extraction pipelines and column-level provenance are documented on each [dataset card](https://huggingface.co/AmareshHebbar).

## How to use any model in this suite

Each model is a LoRA adapter on top of its base Qwen2.5 model. Load with PEFT:

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

base_model = "unsloth/Qwen2.5-7B-Instruct"  # match the base size for the model you're using
adapter    = "AmareshHebbar/icd10-coder-qwen25-7b"

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)
```

See each model's individual card for its exact system prompt, example input/output, and recommended serving setup (Transformers, Unsloth, or vLLM).

## Limitations

These are narrow specialist models, not general medical assistants. Each model only performs the single task it was trained on — using it outside that task will produce unreliable output. None of these models are a substitute for a licensed medical or billing professional; all output should be reviewed by a qualified person before being used in a clinical, billing, or compliance decision.

## Citation

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

## Contact

- GitHub: [amareshhebbar](https://github.com/amareshhebbar)
- LinkedIn: [gvamaresh](https://www.linkedin.com/in/gvamaresh)
- HuggingFace: [AmareshHebbar](https://huggingface.co/AmareshHebbar)