- Libraries
- Transformers
How to use macmacmacmac/gemma-4-31B-it-litert-lm with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="macmacmacmac/gemma-4-31B-it-litert-lm") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("macmacmacmac/gemma-4-31B-it-litert-lm", device_map="auto") - LiteRT-LM
How to use macmacmacmac/gemma-4-31B-it-litert-lm with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM)
# and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter).
# For platform-specific integration guides, please refer to the official developer website:
# https://ai.google.dev/edge/litert-lm
# To try LiteRT-LM, the easiest way is to use our CLI tool.
# 1. Install the LiteRT-LM CLI tool:
pip install -U litert-lm
# 2. Download and run this model locally:
# See: https://ai.google.dev/edge/litert-lm/cli
# A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run
# (or pass its name right after the repo id).
litert-lm run \
--from-huggingface-repo=macmacmacmac/gemma-4-31B-it-litert-lm \
--prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use macmacmacmac/gemma-4-31B-it-litert-lm with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macmacmacmac/gemma-4-31B-it-litert-lm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "macmacmacmac/gemma-4-31B-it-litert-lm",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/macmacmacmac/gemma-4-31B-it-litert-lm
- SGLang
How to use macmacmacmac/gemma-4-31B-it-litert-lm 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 "macmacmacmac/gemma-4-31B-it-litert-lm" \
--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": "macmacmacmac/gemma-4-31B-it-litert-lm",
"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 "macmacmacmac/gemma-4-31B-it-litert-lm" \
--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": "macmacmacmac/gemma-4-31B-it-litert-lm",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' - Docker Model Runner
How to use macmacmacmac/gemma-4-31B-it-litert-lm with Docker Model Runner:
docker model run hf.co/macmacmacmac/gemma-4-31B-it-litert-lm
