Instructions to use ig1/medgemma-27b-text-it-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ig1/medgemma-27b-text-it-FP8-Dynamic with Transformers:

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

pipe = pipeline("text-generation", model="ig1/medgemma-27b-text-it-FP8-Dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ig1/medgemma-27b-text-it-FP8-Dynamic")
model = AutoModelForCausalLM.from_pretrained("ig1/medgemma-27b-text-it-FP8-Dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ig1/medgemma-27b-text-it-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ig1/medgemma-27b-text-it-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ig1/medgemma-27b-text-it-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ig1/medgemma-27b-text-it-FP8-Dynamic

SGLang

How to use ig1/medgemma-27b-text-it-FP8-Dynamic 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 "ig1/medgemma-27b-text-it-FP8-Dynamic" \
    --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": "ig1/medgemma-27b-text-it-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ig1/medgemma-27b-text-it-FP8-Dynamic" \
        --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": "ig1/medgemma-27b-text-it-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ig1/medgemma-27b-text-it-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/ig1/medgemma-27b-text-it-FP8-Dynamic
```

MedGemma-27B-Text-IT-FP8-Dynamic

Overview

MedGemma-27B-Text-IT-FP8-Dynamic is an FP8 Dynamic–quantized derivative of Google’s MedGemma-27B-Text-IT model, optimized for high-throughput inference while preserving strong performance on medical and biomedical instruction-tuned text-only tasks.

This version is intended for vLLM deployment on modern NVIDIA GPUs and follows a conservative FP8 Dynamic quantization strategy designed for maximum stability.

Base Model

Base model: google/medgemma-27b-text-it
Architecture: Decoder-only Transformer (instruction-tuned)
Domain: Medical / Biomedical NLP
Modality: Text-only

Quantization Details

Method: FP8 Dynamic
Tooling: llmcompressor
Quantized layers: Linear layers
Excluded components:
- lm_head

Rationale

FP8 Dynamic reduces VRAM usage and improves inference throughput.
Excluding lm_head preserves output stability.
The resulting model is fully compatible with vLLM.

Weights are already quantized — do not apply runtime quantization.

Intended Use

Medical and biomedical instruction-following
Clinical text summarization
Medical RAG pipelines
Decision-support and research assistance

Deployment (vLLM)

Model tree for ig1/medgemma-27b-text-it-FP8-Dynamic

Base model

google/gemma-3-27b-pt

Finetuned

google/medgemma-27b-text-it

Quantized

(28)

this model

ig1
/

medgemma-27b-text-it-FP8-Dynamic