Instructions to use YuCollection/gemma-4-31B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YuCollection/gemma-4-31B-bf16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="YuCollection/gemma-4-31B-bf16")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("YuCollection/gemma-4-31B-bf16")
model = AutoModelForImageTextToText.from_pretrained("YuCollection/gemma-4-31B-bf16")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use YuCollection/gemma-4-31B-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YuCollection/gemma-4-31B-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YuCollection/gemma-4-31B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/YuCollection/gemma-4-31B-bf16

SGLang

How to use YuCollection/gemma-4-31B-bf16 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 "YuCollection/gemma-4-31B-bf16" \
    --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": "YuCollection/gemma-4-31B-bf16",
		"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 "YuCollection/gemma-4-31B-bf16" \
        --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": "YuCollection/gemma-4-31B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use YuCollection/gemma-4-31B-bf16 with Docker Model Runner:
```
docker model run hf.co/YuCollection/gemma-4-31B-bf16
```

gemma-4-31B-bf16

File size: 881 Bytes

a9b2a84

{
  "audio_token": "<|audio|>",
  "backend": "tokenizers",
  "boa_token": "<|audio>",
  "boi_token": "<|image>",
  "bos_token": "<bos>",
  "eoa_token": "<audio|>",
  "eoc_token": "<channel|>",
  "eoi_token": "<image|>",
  "eos_token": "<eos>",
  "eot_token": "<turn|>",
  "escape_token": "<|\"|>",
  "etc_token": "<tool_call|>",
  "etd_token": "<tool|>",
  "etr_token": "<tool_response|>",
  "extra_special_tokens": [
    "<|video|>"
  ],
  "image_token": "<|image|>",
  "mask_token": "<mask>",
  "model_max_length": 1000000000000000019884624838656,
  "pad_token": "<pad>",
  "padding_side": "left",
  "processor_class": "Gemma4Processor",
  "soc_token": "<|channel>",
  "sot_token": "<|turn>",
  "stc_token": "<|tool_call>",
  "std_token": "<|tool>",
  "str_token": "<|tool_response>",
  "think_token": "<|think|>",
  "tokenizer_class": "GemmaTokenizer",
  "unk_token": "<unk>"
}