Instructions to use LLMWildling/gemma-4-100b-a10b-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLMWildling/gemma-4-100b-a10b-coder with Transformers:

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

pipe = pipeline("text-generation", model="LLMWildling/gemma-4-100b-a10b-coder")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("LLMWildling/gemma-4-100b-a10b-coder")
model = AutoModelForMultimodalLM.from_pretrained("LLMWildling/gemma-4-100b-a10b-coder")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLMWildling/gemma-4-100b-a10b-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLMWildling/gemma-4-100b-a10b-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLMWildling/gemma-4-100b-a10b-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLMWildling/gemma-4-100b-a10b-coder

SGLang

How to use LLMWildling/gemma-4-100b-a10b-coder 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 "LLMWildling/gemma-4-100b-a10b-coder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLMWildling/gemma-4-100b-a10b-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "LLMWildling/gemma-4-100b-a10b-coder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLMWildling/gemma-4-100b-a10b-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLMWildling/gemma-4-100b-a10b-coder with Docker Model Runner:
```
docker model run hf.co/LLMWildling/gemma-4-100b-a10b-coder
```

gemma-4-100b-a10b-coder / README.md

LLMWildling

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	---
	license: gemma
	base_model: gemma-4
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- gemma
	- gemma-4
	- coder
	- mixture-of-experts
	- mxfp4
	- thinking
	- tool-use
	- long-context
	---

	# gemma-4-100b-a10b-coder

	gemma-4-100b-a10b-coder is a Gemma 4 based coder model for software engineering, code
	editing, Q/A, tool use, and long-context assistant workflows.

	## model

	- Family: `gemma-4`
	- Variant: `coder`
	- Model type: sparse Mixture-of-Experts language model
	- Total logical text parameters: approximately `100.3B`
	- Active logical text parameters per token: approximately `10.0B`
	- Active experts per token: `38`
	- Weight format: MXFP4 expert weights with BF16 shared weights
	- Context: up to `200000` tokens in the listed vLLM configuration

	## serving

	```bash
	CUDA_VISIBLE_DEVICES=0,1 vllm serve /path/to/gemma-4-100b-a10b-coder \
	--served-model-name vllm/doobee \
	--host 0.0.0.0 \
	--port 23333 \
	--dtype bfloat16 \
	--tensor-parallel-size 2 \
	--enable-expert-parallel \
	--max-model-len 200000 \
	--gpu-memory-utilization 0.96 \
	--trust-remote-code \
	--reasoning-parser gemma4 \
	--tool-call-parser gemma4 \
	--enable-auto-tool-choice \
	--default-chat-template-kwargs '{"enable_thinking": true}' \
	--generation-config vllm \
	--language-model-only \
	--skip-mm-profiling \
	--max-num-seqs 1 \
	--max-num-batched-tokens 8192
	```

	For clients that should not receive reasoning text, send
	`"include_reasoning": false` in chat-completion requests.

	## files

	- `config.json`
	- `generation_config.json`
	- `tokenizer.json`
	- `tokenizer_config.json`
	- `chat_template.jinja`
	- `model.safetensors.index.json`
	- MXFP4/BF16 safetensor shards

	## license

	This model is released under the Gemma license.