CraneAILabs/v2.1-instruct-medgemma-bf16

v2.1 Instruct MedGemma (SFT-only)

A clinical decision-support model fine-tuned from MedGemma 1.5 4B-IT on the Uganda Clinical Guidelines 2023. Conservative SFT-only checkpoint with perfect refusal compliance.

For the primary production model (SFT + DPO, best overall performance), see Crane MedGemma 1.5 IT.

This model is a clinical thinking aid. It does not provide diagnoses, prescriptions, or automated clinical decisions.

Intended Use

Primary use case: Assist frontline health workers (clinical officers, nurses) at Health Centre II–IV facilities in Uganda with clinical triage reasoning — differential diagnosis, danger sign identification, investigation ordering, referral criteria, and special population considerations.

Output formats: The model can emit structured triage assessments in three formats (XML, positional array, prose) based on the system prompt. The production app uses compact XML packets for low-latency on-device inference.

Deployment target: On-device inference on Android via quantized GGUF. This bf16 checkpoint is the full-precision reference and was used as the base for the DPO-aligned Crane MedGemma 1.5 IT.

In-Scope

• Differential diagnosis reasoning for conditions in the Uganda Clinical Guidelines
• Danger sign identification and referral triage
• Investigation recommendations
• Special population considerations (pediatric, pregnancy, HIV)
• Refusal of out-of-scope queries (treatment, dosing)

Out-of-Scope

• Treatment and dosing recommendations — the model is trained to refuse these queries and redirect to the facility's Uganda Clinical Guidelines
• Diagnostic conclusions or prescriptions
• Multi-turn conversations
• Languages other than English
• Conditions not covered by the Uganda Clinical Guidelines 2023

Model Details

Base model	google/medgemma-1.5-4b-it (Gemma 3 4B architecture)
Parameters	4.3B
Precision	BF16
Training method	QLoRA SFT (Supervised Fine-Tuning)
Training data	11,991 decision-support Q&A pairs derived from Uganda Clinical Guidelines 2023, followed by instruction-following SFT on format-switching data
Scope	Decision-support only — treatment and dosing categories removed by design

Training Approach

The model was fine-tuned in two stages:

1. Decision-support SFT: QLoRA fine-tuning on 11,991 Q&A pairs covering 7 clinical categories (differential diagnosis, diagnosis, referral, investigation, danger signs, special populations, refusal). Treatment and dosing categories were excluded after evaluation showed a capacity ceiling on factual drug recall at this parameter scale.

2. Instruction-following SFT: Continued fine-tuning to teach format-switching — the model emits XML, array, or prose triage packets depending on the system prompt.

What the Model Refuses

The model is trained to refuse treatment and dosing questions with: "Treatment and dosing recommendations are outside the scope of this tool. Please refer to your facility's Uganda Clinical Guidelines."

This is a deliberate safety boundary. Drug name confusion at the 4B parameter scale made treatment responses unreliable, so the scope was narrowed to decision-support where the model performs well.

Evaluation

Evaluated across three benchmarks using Gemini as an automated evaluator in constrained comparison mode.

Ship Gate Analysis

Gate	Target	Result
Prose content quality (210 samples)	>= 3.43/5	3.39
XML parse rate	>= 95%	98.2%
Array parse rate	>= 95%	92.2%
Prose parse rate	>= 95%	100%
XML content quality (held-out)	>= 3.43/5	3.03
Refusal compliance (prose)	>= 4.5/5	5.00

5 of 12 ship gates passed. Content quality on held-out conditions caps at ~3.0/5 — a parameter capacity ceiling, not a training procedure failure. Retrieval-augmented generation (RAG) is expected to close this gap.

Strengths

• Perfect refusal compliance: 5.00/5 on prose refusal — never leaks treatment or dosing content
• Strong differential diagnosis: 4.38/5 on diagnosis questions
• High XML parse rate: 98.2% valid structured output
• Reasoning over recall: "Why" questions score 5.0/5; the model reasons well about clinical logic

Known Limitations

• 4B parameter capacity ceiling: Held-out content quality caps at ~3.0/5. The model has limited factual recall for conditions it hasn't seen extensively in training.
• Treatment and dosing excluded: By design. The model refuses these queries.
• Special populations weakness: 1.11/5 — the source guidelines have limited population-specific detail for many conditions.
• Array parse rate: 92.2% (below 95% gate). XML and prose formats are more reliable.
• Single-turn only: No conversational follow-up capability.

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "CraneAILabs/v2.1-instruct-medgemma-bf16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

messages = [
    {"role": "system", "content": "You are a clinical decision-support tool based on the Uganda Clinical Guidelines. Provide structured triage assessments. Do not provide treatment or dosing recommendations."},
    {"role": "user", "content": "A 4-year-old presents with fever 39.5C for 3 days, neck stiffness, and photophobia. What should I consider?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.3)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Ethical Considerations

• Not a replacement for clinical judgment. All outputs are advisory. The clinician makes all decisions.
• No patient data was used in training. All training data is derived from published government clinical guidelines.
• Offline-first deployment — no patient data leaves the device.
• Scope boundaries are safety boundaries. The refusal of treatment/dosing queries is a deliberate design choice to prevent harm from drug name confusion at this model scale.

Citation

If you use this model, please cite:

@misc{crane-medgemma-v21-2026,
  title={v2.1 Instruct MedGemma: SFT-Only Clinical Decision-Support for Uganda Clinical Guidelines},
  author={Crane AI Labs},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/CraneAILabs/v2.1-instruct-medgemma-bf16}
}

License

This model is subject to the MedGemma Terms of Use. Additional fine-tuning artifacts are proprietary to Crane AI Labs.