- Libraries
- Transformers
How to use ercwang/gemma-demo with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="ercwang/gemma-demo") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ercwang/gemma-demo", device_map="auto") - Keras
How to use ercwang/gemma-demo with Keras:
# !pip install -U keras tensorflow huggingface_hub
# Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here;
# "jax" and "torch" also work for computation once TensorFlow is installed.
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import keras
model = keras.saving.load_model("hf://ercwang/gemma-demo")
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ercwang/gemma-demo with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ercwang/gemma-demo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ercwang/gemma-demo",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/ercwang/gemma-demo
- SGLang
How to use ercwang/gemma-demo 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 "ercwang/gemma-demo" \
--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": "ercwang/gemma-demo",
"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 "ercwang/gemma-demo" \
--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": "ercwang/gemma-demo",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' - Docker Model Runner
How to use ercwang/gemma-demo with Docker Model Runner:
docker model run hf.co/ercwang/gemma-demo
