Instructions to use isbondarev/gemma-3-270m-adv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use isbondarev/gemma-3-270m-adv with Transformers:

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

pipe = pipeline("text-generation", model="isbondarev/gemma-3-270m-adv")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("isbondarev/gemma-3-270m-adv")
model = AutoModelForCausalLM.from_pretrained("isbondarev/gemma-3-270m-adv", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use isbondarev/gemma-3-270m-adv with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "isbondarev/gemma-3-270m-adv"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isbondarev/gemma-3-270m-adv",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/isbondarev/gemma-3-270m-adv

SGLang

How to use isbondarev/gemma-3-270m-adv 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 "isbondarev/gemma-3-270m-adv" \
    --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": "isbondarev/gemma-3-270m-adv",
		"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 "isbondarev/gemma-3-270m-adv" \
        --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": "isbondarev/gemma-3-270m-adv",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use isbondarev/gemma-3-270m-adv with Docker Model Runner:
```
docker model run hf.co/isbondarev/gemma-3-270m-adv
```

isbondarev commited on Dec 24, 2025

Commit

41cbb20

verified ·

1 Parent(s): ab7086e

Upload tokenizer

Browse files

Files changed (7) hide show

.gitattributes +1 -0
added_tokens.json +3 -0
chat_template.jinja +7 -0
special_tokens_map.json +49 -0
tokenizer.json +3 -0
tokenizer.model +3 -0
tokenizer_config.json +0 -0

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+' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '<start_of_turn>user
+' + content + '<end_of_turn>
+<start_of_turn>model
+' }}{% elif message['role'] == 'assistant' %}{{ content + '<end_of_turn>
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