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Model: Rumiii/LlamaTron-RS1-MedThinker Source: Original Platform
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248
README.md
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README.md
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---
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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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|---|---|
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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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|---|---|---|
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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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```
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
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{{- '{"name": "' + tool_call.name + '", ' }}
|
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{{- '"parameters": ' }}
|
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{{- tool_call.arguments | tojson }}
|
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{{- "}" }}
|
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{{- "<|eot_id|>" }}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
|
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{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
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||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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37
config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 128000,
|
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"torch_dtype": "bfloat16",
|
||||
"eos_token_id": 128009,
|
||||
"head_dim": 64,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
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"initializer_range": 0.02,
|
||||
"intermediate_size": 8192,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 16,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 128004,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_parameters": {
|
||||
"factor": 32.0,
|
||||
"high_freq_factor": 4.0,
|
||||
"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_theta": 500000.0,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"tie_word_embeddings": true,
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.3.11",
|
||||
"use_cache": false,
|
||||
"vocab_size": 128256
|
||||
}
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3
model.safetensors
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3
model.safetensors
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|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4bca3b1c5836b1844858ceb121a93bd814632d1b6ccb541a49682f0dffde1fe3
|
||||
size 2471645608
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
19
tokenizer_config.json
Normal file
19
tokenizer_config.json
Normal file
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"backend": "tokenizers",
|
||||
"bos_token": "<|begin_of_text|>",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|eot_id|>",
|
||||
"from_slow": true,
|
||||
"is_local": false,
|
||||
"legacy": false,
|
||||
"model_input_names": [
|
||||
"input_ids",
|
||||
"attention_mask"
|
||||
],
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|finetune_right_pad_id|>",
|
||||
"padding_side": "left",
|
||||
"tokenizer_class": "TokenizersBackend",
|
||||
"unk_token": null,
|
||||
"chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
Reference in New Issue
Block a user