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

Libraries

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

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

pipe = pipeline("text-generation", model="unsloth/functiongemma-270m-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsloth/functiongemma-270m-it")
model = AutoModelForCausalLM.from_pretrained("unsloth/functiongemma-270m-it")
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 unsloth/functiongemma-270m-it with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/unsloth/functiongemma-270m-it

SGLang

How to use unsloth/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 "unsloth/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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/functiongemma-270m-it",
		"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 "unsloth/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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/functiongemma-270m-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use unsloth/functiongemma-270m-it with Docker Model Runner:
```
docker model run hf.co/unsloth/functiongemma-270m-it
```

danielhanchen commited on Jan 15

Commit

79eaaa8

verified ·

1 Parent(s): b5f4178

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -37,7 +37,7 @@ base_model:
 -   [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
 -   [FunctionGemma on Kaggle](https://www.kaggle.com/models/google/functiongemma/)
--   Function[Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/functiongemma)
 **Terms of Use**: [Terms](https://ai.google.dev/gemma/terms)\
 **Authors**: Google DeepMind
@@ -265,12 +265,12 @@ Model evaluation metrics and results.
       <td>61.6</td>
     </tr>
     <tr>
-      <td>BFCL Parallel</td>
       <td>0-shot</td>
       <td>63.5</td>
     </tr>
     <tr>
-      <td>BFCL Multiple</td>
       <td>0-shot</td>
       <td>39</td>
     </tr>
@@ -285,12 +285,12 @@ Model evaluation metrics and results.
       <td>36.2</td>
     </tr>
     <tr>
-      <td>BFCL Live Parallel</td>
       <td>0-shot</td>
       <td>25.7</td>
     </tr>
     <tr>
-      <td>BFCL Live Multiple</td>
       <td>0-shot</td>
       <td>22.9</td>
     </tr>
@@ -307,7 +307,7 @@ Model evaluation metrics and results.
     <tr>
       <td>BFCL Irrelevance</td>
       <td>0-shot</td>
-      <td>70.6</td>
     </tr>
   </tbody>
 </table>
@@ -572,4 +572,4 @@ Risks identified and mitigations:
 ### Benefits
-At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models.

 -   [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
 -   [FunctionGemma on Kaggle](https://www.kaggle.com/models/google/functiongemma/)
+-   [FunctionGemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/functiongemma)
 **Terms of Use**: [Terms](https://ai.google.dev/gemma/terms)\
 **Authors**: Google DeepMind
       <td>61.6</td>
     </tr>
     <tr>
+      <td>BFCL Multiple</td>
       <td>0-shot</td>
       <td>63.5</td>
     </tr>
     <tr>
+      <td>BFCL Parallel</td>
       <td>0-shot</td>
       <td>39</td>
     </tr>
       <td>36.2</td>
     </tr>
     <tr>
+      <td>BFCL Live Multiple</td>
       <td>0-shot</td>
       <td>25.7</td>
     </tr>
     <tr>
+      <td>BFCL Live Parallel</td>
       <td>0-shot</td>
       <td>22.9</td>
     </tr>
     <tr>
       <td>BFCL Irrelevance</td>
       <td>0-shot</td>
+      <td>73.7</td>
     </tr>
   </tbody>
 </table>
 ### Benefits
+At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models.