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google
/
functiongemma-270m-it

Text Generation
Transformers
Safetensors
gemma3
gemma
google
functiongemma
Model card Files Files and versions
xet
Community
22

Instructions to use google/functiongemma-270m-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use google/functiongemma-270m-it with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="google/functiongemma-270m-it")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("google/functiongemma-270m-it", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use google/functiongemma-270m-it with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "google/functiongemma-270m-it"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "google/functiongemma-270m-it",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/google/functiongemma-270m-it
  • SGLang

    How to use google/functiongemma-270m-it 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 "google/functiongemma-270m-it" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "google/functiongemma-270m-it",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    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 "google/functiongemma-270m-it" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "google/functiongemma-270m-it",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use google/functiongemma-270m-it with Docker Model Runner:

    docker model run hf.co/google/functiongemma-270m-it
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

functionGemma

#22 opened 2 months ago by
dabbledabble-IND-da-air

Install & run this model easily using llmpm

#21 opened 3 months ago by
sarthak-saxena

Best way to finetune for structured final output instead of plain text final output ?

3
#20 opened 3 months ago by
vncntrsnlt

preprocessor_config.json is missing?

3
#19 opened 4 months ago by
matthewgaddis-streamline

Any plans to release 1b-it model?

1
#16 opened 5 months ago by
uasan

New tool to benchmark GGUF files

2
#15 opened 5 months ago by
Nerdsking

Tool Calling example on Raspberry PI

👍 1
2
#14 opened 5 months ago by
samairtimer

Fine-tuning didn't work after ONNX export

4
#13 opened 5 months ago by
harlley

Request: DOI

1
#11 opened 5 months ago by
Vighnesh7711

Request: DOI

1
#10 opened 5 months ago by
maalikishaak

Lora training code

2
#9 opened 5 months ago by
HarryML

functiongemma-270m-it-demo

🔥👍 2
1
#8 opened 5 months ago by
aifeifei798

Installation Video and Testing - Step by Step

3
#6 opened 5 months ago by
fahdmirzac

Fixing MLX stop tokens

3
#3 opened 5 months ago by
prince-canuma
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