Instructions to use melonTraining/gemma-4-31b-kairos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use melonTraining/gemma-4-31b-kairos with PEFT:
```
Task type is invalid.
```

How to use melonTraining/gemma-4-31b-kairos with Transformers:

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

pipe = pipeline("text-generation", model="melonTraining/gemma-4-31b-kairos")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("melonTraining/gemma-4-31b-kairos")
model = AutoModelForMultimodalLM.from_pretrained("melonTraining/gemma-4-31b-kairos")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use melonTraining/gemma-4-31b-kairos with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "melonTraining/gemma-4-31b-kairos"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "melonTraining/gemma-4-31b-kairos",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/melonTraining/gemma-4-31b-kairos

SGLang

How to use melonTraining/gemma-4-31b-kairos 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 "melonTraining/gemma-4-31b-kairos" \
    --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": "melonTraining/gemma-4-31b-kairos",
		"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 "melonTraining/gemma-4-31b-kairos" \
        --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": "melonTraining/gemma-4-31b-kairos",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use melonTraining/gemma-4-31b-kairos with Docker Model Runner:
```
docker model run hf.co/melonTraining/gemma-4-31b-kairos
```

gemma-4-31b-kairos

86.8 GB

Ctrl+K

1 contributor

History: 2 commits

galeswel

Add files using upload-large-folder tool

7deec60 verified 2 days ago

melonTraining
/

gemma-4-31b-kairos

Detected Pickle imports (7)

Detected Pickle imports (7)

Detected Pickle imports (7)

Detected Pickle imports (7)

Detected Pickle imports (7)

Detected Pickle imports (7)

Detected Pickle imports (7)

Detected Pickle imports (7)

Detected Pickle imports (10)