Instructions to use JWei05/gemma4-e2b-base-to-e4b-topk128-distill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JWei05/gemma4-e2b-base-to-e4b-topk128-distill with Transformers:

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

pipe = pipeline("text-generation", model="JWei05/gemma4-e2b-base-to-e4b-topk128-distill")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("JWei05/gemma4-e2b-base-to-e4b-topk128-distill")
model = AutoModelForMultimodalLM.from_pretrained("JWei05/gemma4-e2b-base-to-e4b-topk128-distill", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JWei05/gemma4-e2b-base-to-e4b-topk128-distill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JWei05/gemma4-e2b-base-to-e4b-topk128-distill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JWei05/gemma4-e2b-base-to-e4b-topk128-distill",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JWei05/gemma4-e2b-base-to-e4b-topk128-distill

SGLang

How to use JWei05/gemma4-e2b-base-to-e4b-topk128-distill 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 "JWei05/gemma4-e2b-base-to-e4b-topk128-distill" \
    --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": "JWei05/gemma4-e2b-base-to-e4b-topk128-distill",
		"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 "JWei05/gemma4-e2b-base-to-e4b-topk128-distill" \
        --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": "JWei05/gemma4-e2b-base-to-e4b-topk128-distill",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JWei05/gemma4-e2b-base-to-e4b-topk128-distill with Docker Model Runner:
```
docker model run hf.co/JWei05/gemma4-e2b-base-to-e4b-topk128-distill
```

New discussion

Resources

View closed (0)

Welcome to the community

The community tab is the place to discuss and collaborate with the HF community!