Instructions to use LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4 with Transformers:

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

pipe = pipeline("text-generation", model="LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4")
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-opencoder-48b-a10b-nvfp4")
model = AutoModelForMultimodalLM.from_pretrained("LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4", device_map="auto")
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-opencoder-48b-a10b-nvfp4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4"
# 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-opencoder-48b-a10b-nvfp4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4

SGLang

How to use LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4 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-opencoder-48b-a10b-nvfp4" \
    --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-opencoder-48b-a10b-nvfp4",
		"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-opencoder-48b-a10b-nvfp4" \
        --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-opencoder-48b-a10b-nvfp4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4 with Docker Model Runner:
```
docker model run hf.co/LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4
```

Gemma 4 OpenCoder 48B-A10B

Gemma 4 OpenCoder 48B-A10B is a post-trained Gemma 4 coding model focused on OpenCode-style software engineering workflows, long-context reasoning, and tool use.

The model has approximately 48.1B total text parameters and approximately 9.5B active text parameters per token. It supports a 262K token context window through the underlying Gemma 4 configuration.

This NVFP4 release is intended for efficient serving in runtimes with Gemma 4 NVFP4, reasoning, and tool-calling support.

Intended use

OpenCode coding workflows
Agentic software engineering tasks
Long-context codebase navigation
Reasoning-enabled tool use
Multi-file implementation and editing sessions

Run with reasoning and tool calling enabled.

Format

Format: NVFP4 / compressed-tensors
Architecture: Gemma 4
Total text parameters: ~48.1B
Active text parameters: ~9.5B
Context length: 262,144 tokens
Language focus: English

Example outputs

In one continuous OpenCode chat, this checkpoint one-shot generated:

a Flappy Bird-style game
a Breakout game
a skiing game
a monster truck jumping over cars

Serving

Use a Gemma 4 compatible runtime with reasoning and tool calling enabled. For NVFP4 serving, use a runtime build that supports Gemma 4 compressed-tensors NVFP4 MoE kernels.

Example:

vllm serve <repo-or-local-path> \
  --trust-remote-code \
  --reasoning-parser gemma4 \
  --tool-call-parser gemma4 \
  --enable-auto-tool-choice

For best results in OpenCode, keep the model's native chat template and enable thinking/reasoning mode.

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Safetensors

Model size

26B params

Tensor type

BF16

F8_E4M3

Model tree for LLMWildling/gemma-4-opencoder-48b-a10b-nvfp4

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Quantized

(331)

this model