Instructions to use neuralworm/gemma-3-270m-it-p2.8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neuralworm/gemma-3-270m-it-p2.8 with Transformers:

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

pipe = pipeline("text-generation", model="neuralworm/gemma-3-270m-it-p2.8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("neuralworm/gemma-3-270m-it-p2.8", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use neuralworm/gemma-3-270m-it-p2.8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neuralworm/gemma-3-270m-it-p2.8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neuralworm/gemma-3-270m-it-p2.8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neuralworm/gemma-3-270m-it-p2.8

SGLang

How to use neuralworm/gemma-3-270m-it-p2.8 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 "neuralworm/gemma-3-270m-it-p2.8" \
    --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": "neuralworm/gemma-3-270m-it-p2.8",
		"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 "neuralworm/gemma-3-270m-it-p2.8" \
        --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": "neuralworm/gemma-3-270m-it-p2.8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use neuralworm/gemma-3-270m-it-p2.8 with Docker Model Runner:
```
docker model run hf.co/neuralworm/gemma-3-270m-it-p2.8
```

gemma-3-270m-it-p2.8 / configuration_gemma_px.py

neuralworm

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a4d8bec verified about 2 months ago

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	from transformers import PretrainedConfig
	from transformers.models.gemma3.configuration_gemma3 import Gemma3TextConfig

	class Gemma3PXConfig(Gemma3TextConfig):
	model_type = "gemma_3_px"

	def __init__(self, **kwargs):
	super().__init__(**kwargs)
	# Default config for 270m
	self.px_routing_mode = kwargs.get("px_routing_mode", "adaptive")
	self.px_gamma = kwargs.get("px_gamma", 0.10)