Instructions to use bumblebee-testing/tiny-random-Gemma3ForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bumblebee-testing/tiny-random-Gemma3ForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="bumblebee-testing/tiny-random-Gemma3ForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bumblebee-testing/tiny-random-Gemma3ForCausalLM")
model = AutoModelForCausalLM.from_pretrained("bumblebee-testing/tiny-random-Gemma3ForCausalLM", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bumblebee-testing/tiny-random-Gemma3ForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bumblebee-testing/tiny-random-Gemma3ForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bumblebee-testing/tiny-random-Gemma3ForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bumblebee-testing/tiny-random-Gemma3ForCausalLM

SGLang

How to use bumblebee-testing/tiny-random-Gemma3ForCausalLM 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 "bumblebee-testing/tiny-random-Gemma3ForCausalLM" \
    --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": "bumblebee-testing/tiny-random-Gemma3ForCausalLM",
		"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 "bumblebee-testing/tiny-random-Gemma3ForCausalLM" \
        --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": "bumblebee-testing/tiny-random-Gemma3ForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bumblebee-testing/tiny-random-Gemma3ForCausalLM with Docker Model Runner:
```
docker model run hf.co/bumblebee-testing/tiny-random-Gemma3ForCausalLM
```

tiny-random-Gemma3ForCausalLM / model.safetensors

jonatanklosko

Upload Gemma3ForCausalLM

e075e7c verified 7 months ago

Download

History Blame

954 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: e1e3e878f1ec4190b3eee2754687cdb08a3cd3c46a4b3716d7c9fb77478f5959
Size of remote file:: 954 kB
SHA256:: c6d80df4c7423e5a75e37a82fa00d142f0bab2335e558ef40833b6b908b4bd9f

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.