Instructions to use LLMWildling/gemma-4-160b-a18b-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLMWildling/gemma-4-160b-a18b-coder with Transformers:

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

pipe = pipeline("text-generation", model="LLMWildling/gemma-4-160b-a18b-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-4-160b-a18b-coder")
model = AutoModelForMultimodalLM.from_pretrained("LLMWildling/gemma-4-160b-a18b-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-4-160b-a18b-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLMWildling/gemma-4-160b-a18b-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-4-160b-a18b-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLMWildling/gemma-4-160b-a18b-coder

SGLang

How to use LLMWildling/gemma-4-160b-a18b-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-4-160b-a18b-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-4-160b-a18b-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-4-160b-a18b-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-4-160b-a18b-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLMWildling/gemma-4-160b-a18b-coder with Docker Model Runner:
```
docker model run hf.co/LLMWildling/gemma-4-160b-a18b-coder
```

gemma-4-160b-a18b-coder

gemma-4-160b-a18b-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 160.2B
Active logical text parameters per token: approximately 18.0B
Active experts per token: 83
Weight format: MXFP4 expert weights with BF16 shared weights
Context: up to 200000 tokens in the listed vLLM configuration

serving

CUDA_VISIBLE_DEVICES=0,1 vllm serve /path/to/gemma-4-160b-a18b-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.

Downloads last month: 53

Safetensors

Model size

94B params

Tensor type

BF16