248 lines
8.6 KiB
Markdown
248 lines
8.6 KiB
Markdown
---
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library_name: transformers
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license: llama3.2
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datasets:
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- OpenMed/Medical-Reasoning-SFT-Trinity-Mini
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language:
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- en
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base_model:
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- meta-llama/Llama-3.2-1B-Instruct
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tags:
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- transformers
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- medical
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- reasoning
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- chain-of-thought
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- clinical
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- healthcare
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- llama
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- lora
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- unsloth
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- fine-tuned
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---
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[](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
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[](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Trinity-Mini)
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[](https://github.com/unslothai/unsloth)
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[](https://www.nvidia.com)
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---
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</div>
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<br/>
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## What is LlamaTron RS1 MedThinker?
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Most medical language models are trained to retrieve answers. LlamaTron RS1 MedThinker was trained to **reason** through them.
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Built on Meta's Llama 3.2 1B Instruct and fine-tuned on 810,374 medical chain-of-thought samples, MedThinker doesn't just return a diagnosis — it works through the differential, explains the pathophysiology behind its conclusion, gives dose-specific treatment steps, and tells you exactly when to escalate care.
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The reasoning pattern mirrors that of a junior physician presenting a case: structured, explainable, and clinically grounded.
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<br/>
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## Demo
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**Model Output — Step-by-Step Clinical Reasoning**
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<br/>
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## Output Format
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Every response follows a strict four-part clinical structure:
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```
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DIAGNOSIS Primary diagnosis with key differentials
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REASONING Clinical logic, pathophysiology, and decision trail
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IMMEDIATE TREATMENT Prioritised, dose-specific intervention steps
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RED FLAGS Escalation criteria and emergency triggers
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```
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**Example — Input:**
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```
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3yo boy, barking cough, stridor, worse at night. Diagnosis and treatment?
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```
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**Example — Output:**
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```
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DIAGNOSIS: Viral croup (laryngotracheobronchitis).
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Differentials: epiglottitis, foreign body aspiration, bacterial tracheitis.
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REASONING: The classic triad of barking cough, inspiratory stridor, and nocturnal
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worsening in a toddler is pathognomonic for croup. Caused by parainfluenza virus
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producing subglottic edema. Nighttime worsening occurs due to supine positioning
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increasing mucosal congestion. Age 6 months to 3 years is the peak incidence window.
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Epiglottitis is deprioritised given the absence of drooling, tripod posturing,
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or toxic appearance.
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IMMEDIATE TREATMENT:
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- Dexamethasone 0.6mg/kg PO/IM single dose (reduces subglottic edema within 2-6h)
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- Nebulised epinephrine 5ml of 1:1000 if moderate-severe stridor present at rest
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- Humidified cool air — supportive, reduces mucosal congestion
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- Keep child calm — agitation significantly worsens stridor
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- Oxygen via mask if SpO2 drops below 92%
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RED FLAGS: Call emergency services immediately if stridor is present at rest and
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not improving, child is drooling or cannot swallow, cyanosis appears, or the child
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becomes exhausted. These indicate impending airway obstruction requiring intubation.
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```
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<br/>
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## Training Details
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| Parameter | Value |
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|---|---|
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| Base Model | meta-llama/Llama-3.2-1B-Instruct |
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| Fine-Tune Method | LoRA SFT via Unsloth |
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| LoRA Rank | 32 |
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| LoRA Alpha | 64 |
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| Target Modules | q, k, v, o, gate, up, down projections |
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| Sequence Length | 2048 |
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| Batch Size | 16 per device |
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| Gradient Accumulation | 2 steps — effective batch size 32 |
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| Learning Rate | 2e-4 |
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| Optimizer | AdamW 8-bit |
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| Precision | BF16 |
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| Packing | Enabled |
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| Hardware | NVIDIA RTX A6000 48GB |
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| Framework | Unsloth + TRL |
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<br/>
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## Dataset
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Trained on [OpenMed/Medical-Reasoning-SFT-Trinity-Mini](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Trinity-Mini), generated using `arcee-ai/Trinity-Mini`.
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| Metric | Value |
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| Total Samples | 810,374 |
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| Total Tokens | 1.52 Billion |
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| Reasoning Tokens | 977 Million |
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| Content Tokens | 542 Million |
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| Language | English |
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Each sample contains two components: the **content** (the answer) and the **reasoning_content** (the chain-of-thought trace that produced it). Training on both means the model internalised not just medical knowledge, but the structured thinking process behind clinical decision-making.
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Dataset credit: Maziyar P.
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<br/>
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## Quickstart
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### Installation
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```bash
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pip install torch transformers accelerate
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```
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### Inference
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```python
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import torch
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from transformers import AutoTokenizer, LlamaForCausalLM
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MODEL_PATH = "Rumiii/LlamaTron-RS1-MedThinker"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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model = LlamaForCausalLM.from_pretrained(
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MODEL_PATH,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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model.eval()
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FEW_SHOT = """CASE: 2yo girl, high fever, tugging right ear, irritable, not sleeping.
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DIAGNOSIS: Acute otitis media (AOM). Differentials: otitis externa, teething.
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REASONING: Unilateral ear tugging with fever and irritability in a toddler is the
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classic AOM presentation. Peak incidence at 6mo-2yr due to horizontal Eustachian
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tube anatomy impairing drainage.
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IMMEDIATE TREATMENT:
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- Amoxicillin 90mg/kg/day divided BID x 10 days
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- Ibuprofen/paracetamol for pain and fever
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- Re-evaluate in 48-72h if no improvement
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RED FLAGS: Refer immediately if mastoid swelling, facial palsy, or no improvement
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after 72h of antibiotics."""
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def ask(question: str) -> str:
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prompt = (
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f"<|begin_of_text|>"
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f"<|start_header_id|>system<|end_header_id|>\n"
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f"You are LlamaTron RS1 MedThinker, a clinical medical assistant. "
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f"Always use the structured format shown.<|eot_id|>"
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f"<|start_header_id|>user<|end_header_id|>\n"
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f"Answer this case using structured format:\n\n{FEW_SHOT}<|eot_id|>"
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f"<|start_header_id|>assistant<|end_header_id|>\n{FEW_SHOT}<|eot_id|>"
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f"<|start_header_id|>user<|end_header_id|>\nCASE: {question}<|eot_id|>"
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f"<|start_header_id|>assistant<|end_header_id|>\n"
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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input_len = inputs["input_ids"].shape[1]
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with torch.no_grad():
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out = model.generate(
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**inputs,
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max_new_tokens=400,
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temperature=0.35,
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top_p=0.85,
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do_sample=True,
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repetition_penalty=1.2,
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pad_token_id=tokenizer.eos_token_id,
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)
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raw = tokenizer.decode(out[0][input_len:], skip_special_tokens=False)
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for stop in ["<|eot_id|>", "<|end_of_text|>", "<|start_header_id|>"]:
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if stop in raw:
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raw = raw[:raw.index(stop)]
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return raw.strip()
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# Run
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print(ask("68yo woman, chest pain radiating to left arm, diaphoresis, nausea. BP 90/60, HR 110."))
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```
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<br/>
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## Important Notes on Inference
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This model benefits significantly from **few-shot prompting**. Because the fine-tuning dataset emphasised reasoning content over instruction-following format, providing a single worked example in the prompt before your real question enforces the structured output reliably. The quickstart code above includes this pattern — do not remove the `FEW_SHOT` block.
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Recommended inference parameters:
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| Parameter | Value | Reason |
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| temperature | 0.35 | Confident without hallucinating |
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| top_p | 0.85 | Cuts low-probability tokens |
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| repetition_penalty | 1.2 | Prevents reasoning loops |
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| max_new_tokens | 400-512 | Sufficient for full structured response |
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<br/>
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## Disclaimer
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LlamaTron RS1 MedThinker is intended strictly for **research and educational purposes**. It is not a substitute for professional medical advice, clinical diagnosis, or treatment decisions. All outputs must be reviewed by a qualified medical professional before any clinical application. The authors accept no liability for decisions made on the basis of this model's outputs.
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<br/>
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## Citation
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```bibtex
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@model{llamatron_rs1_medthinker_2026,
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title = {LlamaTron RS1 MedThinker},
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author = {Rumiii},
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year = {2026},
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base_model = {meta-llama/Llama-3.2-1B-Instruct},
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dataset = {OpenMed/Medical-Reasoning-SFT-Trinity-Mini},
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method = {LoRA SFT via Unsloth},
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url = {https://huggingface.co/Rumiii/LlamaTron-RS1-MedThinker}
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}
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``` |