Instructions to use icdevelop/pai1-medgemma-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use icdevelop/pai1-medgemma-27b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="icdevelop/pai1-medgemma-27b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("icdevelop/pai1-medgemma-27b")
model = AutoModelForMultimodalLM.from_pretrained("icdevelop/pai1-medgemma-27b", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use icdevelop/pai1-medgemma-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "icdevelop/pai1-medgemma-27b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "icdevelop/pai1-medgemma-27b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/icdevelop/pai1-medgemma-27b

SGLang

How to use icdevelop/pai1-medgemma-27b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "icdevelop/pai1-medgemma-27b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "icdevelop/pai1-medgemma-27b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "icdevelop/pai1-medgemma-27b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "icdevelop/pai1-medgemma-27b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use icdevelop/pai1-medgemma-27b with Docker Model Runner:
```
docker model run hf.co/icdevelop/pai1-medgemma-27b
```

pai1-medgemma-27b / README.md

emmafar

Comprehensive v26-DPO card: purpose, training, benchmark methodology, usage guidance

a69019d verified 2 days ago

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metadata

license: other
license_name: health-ai-developer-foundations
license_link: https://developers.google.com/health-ai-developer-foundations/terms
base_model: google/medgemma-27b-it
pipeline_tag: image-text-to-text
library_name: transformers
language:
  - th
  - en
tags:
  - medical
  - pharmacy
  - thai
  - medgemma
  - gemma3
  - lora
  - dpo
  - rag
extra_gated_prompt: >-
  **ต้องอ่านและยอมรับก่อนดาวน์โหลด / You must read and accept before
  downloading.**


  โมเดลและข้อมูลนี้ **ไม่ใช่เครื่องมือแพทย์ ไม่ใช่แพทย์หรือเภสัชกร
  และไม่ใช่คำแนะนำทางการแพทย์** ข้อมูลบางส่วนสร้างโดย AI และ
  **ยังไม่ได้รับการตรวจสอบโดยเภสัชกร (อยู่ระหว่างการตรวจสอบ)**
  จึงอาจมีความคลาดเคลื่อน **ผู้ใช้ต้องให้ผู้เชี่ยวชาญ (เภสัชกร/แพทย์)
  ตรวจสอบข้อมูลก่อนนำโมเดลนี้หรือข้อมูลนี้ไปใช้**
  ผลลัพธ์ทุกอย่างต้องผ่านการตรวจสอบโดยเภสัชกรที่มีใบอนุญาต
  ห้ามใช้กับผู้ป่วยจริงโดยไม่มีเภสัชกร/แพทย์กำกับดูแล


  This model/data is **not a medical device, not a doctor or pharmacist, and not
  medical advice.** By accepting you acknowledge: (1) it is not for diagnosis or
  treatment and every output must be reviewed by a licensed pharmacist; (2) part
  of the drug knowledge base is AI-generated and **not yet pharmacist-reviewed
  (review in progress)** and may contain inaccuracies; (3) **you must have the
  information verified by a qualified professional before use**; (4) you accept
  the risk and responsibility of your own use and the developer accepts no
  liability for resulting harm; (5) you are bound by the HAI-DEF Terms
  (https://developers.google.com/health-ai-developer-foundations/terms); (6)
  clinical use on real patients without a supervising pharmacist or physician is
  prohibited.
extra_gated_fields:
  I acknowledge this is NOT a doctor/pharmacist and NOT medical advice / ยอมรับว่าไม่ใช่แพทย์หรือเภสัชกรและไม่ใช่คำแนะนำทางการแพทย์: checkbox
  I acknowledge the knowledge base is not yet pharmacist-reviewed (in review) / ยอมรับว่าข้อมูลยังไม่ผ่านการตรวจสอบโดยเภสัชกร (อยู่ระหว่างตรวจสอบ): checkbox
  I will have the information verified by a qualified professional before use / จะให้ผู้เชี่ยวชาญตรวจสอบข้อมูลก่อนนำไปใช้: checkbox
  I accept the risk and the HAI-DEF Terms: checkbox
  Organization / User: text

pai1-medgemma-27b — Thai Pharmacy Assistant (v26-DPO, premium tier)

⚠️ DRAFT — pharmacist & legal review pending. The drug knowledge base behind this model is partly AI-generated and not yet fully reviewed by a licensed pharmacist (534 entries flagged). Released under an acknowledge-before-download gate. Every output must be verified by a licensed pharmacist before real-world use.

What this model is

pai1-medgemma-27b is a Thai community-pharmacy assistant fine-tuned from google/medgemma-27b-it by I C Develop Co., Ltd.

Its job: help a pharmacy counter handle over-the-counter (OTC) requests in Thai — take a short history, rule out drugs that are unsafe for that customer, recommend a suitable OTC option with dose and cautions, and refer to a doctor when red-flag symptoms appear.

Design principle — reason like a clinician internally, present as a pharmacy assistant externally. The model is trained to do the clinical reasoning a good pharmacist does (contraindication checks, interaction awareness, red-flag triage) while never claiming to be a physician and never issuing a diagnosis — presenting as a doctor is legally prohibited in Thailand.

Tier: this is the premium tier (higher accuracy). The smaller, faster, cheaper tier is icdevelop/pai1-medgemma-4b. A smaller FP8 build (~28 GB, near-identical quality) is at icdevelop/pai1-medgemma-27b-fp8.

Multimodal: inherits MedGemma's image input — intended for medicine boxes, labels and prescriptions, not X-rays or lab films.

Benchmarks

Version lineage (higher is better; every score below was produced by running all cases):

Task	v20	v24	v26-DPO (this release)
lookup (no RAG)	46.5 %	43.0 %	50.5 %
lookup + RAG	64.5 %	65.3 %	64.5 %
deliberation	29.2 %	37.7 %	38.3 %

Reading the numbers: v26-DPO is the strongest on drug-fact recall (lookup 50.5 %, +7.5 over v24) while holding RAG performance and slightly improving clinical deliberation. Deliberation has risen across the whole line (29.2 → 38.3) thanks to the deliberation training described below.

How the benchmark was run (methodology)

All benchmarks are deterministic — the model is served on vLLM with --seed 0 --enforce-eager, and answers are graded by an LLM judge (0–10). A case counts as pass only when the judge passes it and no safety violation (must_not) is triggered. All benchmark cases are held out and decontaminated from the training corpus, so scores are not inflated by memorisation.

Benchmark	Cases	What it measures
`thai_pharma_bench_v2` (lookup)	200	Brand / route / interaction facts with no retrieval. Ground truth from the Thai TMT registry + KB. Pure drug-fact recall.
`thai_pharma_bench_v2_rag` (lookup + RAG)	200	Same questions, but real KB facts are retrieved and injected into the prompt — the intended production setting.
`deliberation_bench_v1`	120	The obvious/top retrieved drug is contraindicated for the customer's disclosed profile. The judge checks whether the model rules it out, explains why, and recommends a safe alternative — reasoning, not recitation.

Before release, the merged model also passes a 6-prompt generation check (headache+fever, greeting, diarrhoea, allergic rhinitis, drug comparison, abdominal pain) verifying coherent Thai output with no repetition or degeneration.

How it was trained

Base model: google/medgemma-27b-it (Google MedGemma, Gemma-3 multimodal).
Stage 1 — LoRA SFT (rank 32, 2 epochs, bf16, LR 3e-6) on thai_pharma_v24 (~33 k dialogues). The vision tower and multi-modal projector are frozen — this is a text-domain adaptation, so image understanding stays exactly as MedGemma trained it.
Stage 2 — combined DPO (β 0.1, ld_alpha 0.5 length-desensitisation, LR 1e-6, 100 steps) on 6,648 preference pairs = 2,872 OTC-accuracy pairs + 3,776 deliberation pairs.
Training corpus (thai_pharma_v24): curated Thai pharmacy dialogues — brand / route / interaction lookup, supplement & vitamin guidance, professional-register Q&A, TMT product grounding, drug-interaction cases, plus the deliberation set below. Exact-dedup + MinHash-LSH near-dedup, then decontaminated against every evaluation set.
Deliberation data (deliberate_v2) — the key ingredient: multi-turn dialogues in which at least one retrieved candidate is contraindicated for the customer's disclosed profile (pregnancy, warfarin, gastric ulcer, driving, …). The target answer must take history → rule out the unsafe option with a reason → recommend a safe alternative → give dose/cautions from the retrieved facts → refer on red flags, and ask for more history when information is insufficient instead of guessing. A verifier rejects any turn that hard-diagnoses, impersonates a physician, or merely recites the record.
Deliberation preference pairs (DPO): chosen = the clinician-style deliberation, rejected = a fluent but unsafe answer that recommends the contraindicated drug. The pairs are length-balanced (chosen/rejected token ratio ≈ 1.0) so the preference signal is behaviour, not verbosity.

How to use

Recommended: serve with vLLM and ground every answer with RAG

vllm serve icdevelop/pai1-medgemma-27b \
  --dtype bfloat16 --max-model-len 8192 \
  --served-model-name pai --seed 0 --enforce-eager

# the knowledge base used for grounding
huggingface-cli download icdevelop/thai-pharma-kb --repo-type dataset

Getting the best results

Always ground with RAG. Retrieve the real drug facts (from the TMT registry / KB) for the drugs in question and inject them into the prompt. Lookup+RAG (64.5 %) is far above no-RAG behaviour, and grounded answers are what the model was trained to produce.
Write the reference block and the question in Thai. The model is purpose-built for Thai community pharmacy; Thai prompts get noticeably better answers.
Let it take history. If the customer's message lacks key details, the model is trained to ask follow-up questions — allow a multi-turn conversation rather than forcing a single-shot answer.
Give it more than one candidate drug. The deliberation training shows its value when the model must choose between options and exclude the unsafe one.
Keep a pharmacist in the loop. Treat every answer as a draft recommendation for a licensed pharmacist to confirm.

Not recommended

Using it without retrieval for factual drug questions (recall is much weaker).
Asking for a diagnosis, or presenting it to end users as a doctor — it is trained to refuse and refer, and doing so is legally prohibited in Thailand.
Radiology or laboratory image interpretation — out of scope.

Data provenance & review status

Source	Count	Status
TMT drug registry (Thai MoPH)	~31,000 products	Official government data
Ingredient knowledge base	1,918 ingredients	AI-generated — not yet pharmacist-reviewed (534 flagged for review)

Knowledge cutoff (data freshness)

The knowledge available to this model is bounded by the RAG knowledge base, not by the weights.

Knowledge base: icdevelop/thai-pharma-kb · cutoff: July 2026
Drugs, brands, supplements or regulatory changes registered after that date are not reflected. To advance the cutoff, re-pull the TMT registry, rebuild the KB, and update the dataset.

Model format

BF16 — full-precision merged weights, ~52 GB on disk.

Limitations & safety

Not a medical device. Not for diagnosis or treatment. Every output must be reviewed by a licensed pharmacist before it reaches a patient.
The knowledge base is not yet pharmacist-reviewed and may contain inaccuracies (534 entries flagged). This is why the repository is gated behind an explicit acknowledgement.
Thai community-pharmacy domain only. Image input is limited to medicine boxes, labels and prescriptions.
The model can still make mistakes on rare drugs, complex poly-pharmacy and unusual interactions — RAG grounding reduces but does not eliminate this.

License

A Model Derivative of google/medgemma-27b-it under the Health AI Developer Foundations (HAI-DEF) Terms. Redistribution requires attaching the Terms, the §3.2 use restrictions, the NOTICE, and a modification notice.

Not a medical device; not for clinical use without a licensed pharmacist. Contact: I C Develop Co., Ltd.