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
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

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.

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.