AmareshHebbar/hindi-medical-sft

Hindi Medical Reasoning (Medical-o1-SFT)

Part of the [AxisMapper Medical AI Suite](https://huggingface.co/collections/AmareshHebbar/axiomapper-medical-ai-suite) — 16 domain-specific SFT datasets for fine-tuning medical LLMs.

Built by [AmareshHebbar](https://huggingface.co/AmareshHebbar) | Studio Ilios / Humanova Minds

What this dataset does

Medical questions → detailed chain-of-thought reasoning and clinical answers

Why download this

Fine-tune models for Hindi-language medical Q&A, build ABDM-compatible clinical assistants, or create multilingual medical reasoning systems for India's healthcare market.

Dataset stats

Data format

Every row is a messages list in chat format — compatible with Unsloth, TRL SFTTrainer, LLaMA-Factory, and any OpenAI-style fine-tuning pipeline:

{
  "messages": [
    {"role": "system",    "content": "You are a ..."},
    {"role": "user",      "content": "A 45-year-old presents with fatigue, weight gain, cold intolerance and bradycardia for 6 months."},
    {"role": "assistant", "content": "Chain-of-thought reasoning through hypothyroidism differential → TSH, free T4 workup → diagnosis and management plan."}
  ]
}

Data source

FreedomIntelligence/medical-o1-reasoning-SFT (en config) — 19k medical CoT reasoning pairs → https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT

All data is extracted from authoritative public sources. No LLM-generated or synthetic content.

Who should use this

Indian health AI developers, multilingual NLP researchers, ABDM ecosystem builders, Indic language medical AI teams.

Quick start

from datasets import load_dataset

ds = load_dataset("AmareshHebbar/hindi-medical-sft")
print(ds["train"][0])

Fine-tuning example (Unsloth)

from unsloth import FastLanguageModel
from trl import SFTTrainer
from datasets import load_dataset

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen2.5-3B-Instruct",
    max_seq_length=2048,
    load_in_4bit=True,
)

dataset = load_dataset("AmareshHebbar/hindi-medical-sft", split="train")

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="messages",
    max_seq_length=2048,
)
trainer.train()

Related datasets in this collection

Citation

@misc{axiomapper2026,
  author    = {Hebbar, Amaresh},
  title     = {AxisMapper: Medical AI Fine-tuning Dataset Suite},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/collections/AmareshHebbar/axiomapper-medical-ai-suite}
}

AxisMapper is an open-source project. Star the repo, open issues, and contribute at [GitHub](https://github.com/amareshhebbar/AxisMapper).