Instructions to use LLMWildling/gemma-4-120b-a12b-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLMWildling/gemma-4-120b-a12b-coder with Transformers:

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

pipe = pipeline("text-generation", model="LLMWildling/gemma-4-120b-a12b-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-4-120b-a12b-coder")
model = AutoModelForMultimodalLM.from_pretrained("LLMWildling/gemma-4-120b-a12b-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-4-120b-a12b-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLMWildling/gemma-4-120b-a12b-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-4-120b-a12b-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLMWildling/gemma-4-120b-a12b-coder

SGLang

How to use LLMWildling/gemma-4-120b-a12b-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-4-120b-a12b-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-4-120b-a12b-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-4-120b-a12b-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-4-120b-a12b-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLMWildling/gemma-4-120b-a12b-coder with Docker Model Runner:
```
docker model run hf.co/LLMWildling/gemma-4-120b-a12b-coder
```

Questions from noob

by lordfervi - opened 4 days ago

Discussion

lordfervi

4 days ago

I stumbled upon your profile by accident.

I'm wondering how you add so many parameters to these models—is it a lot of fine-tuning?

My DGX Spark is supposed to arrive soon, and I'm really looking forward to seeing if the model is significantly better.

LLMWildling

Owner 2 days ago

@lordfervi ive been going at this for 2 years now, its pretty complicated and I still work on perfecting it. do you want me to make you one? feedback is super welcome. enjoy.

lordfervi

2 days ago

@LLMWildling
Let's just say this, we'll stay in touch :)

People usually do various types of model optimization. Your models look like they do some fine-tuning.

I'm afraid we'll reach a point where you'll have enterprise models in the cloud (OpenAI, Claude), some large open-source models (like DeepSeek), but not for consumers (too expensive), you'll have small models (like Gemma), but very few mid-tier models (like Mistral Small 4, GPT-OSS, etc.).

If it's possible to "easily" improve AI models, I think it's revolutionary.

I accidentally found your profile and see that you've made a lot of models much larger. I'm wondering if it works correctly and so on.

Unfortunately, I don't have the DGX Spark for now (I hope I'll have it next week). I'll let you know when I can test it.

Maybe in the future you'll manage to slightly (even not significantly) tune Mistral Small 4 ;)

LLMWildling

Owner 2 days ago

I just checked my pipeline and but pretty sure it's pre/post training, the full run. I use my own optimizer to speed things up.

"If it's possible to "easily" improve AI models, I think it's revolutionary." - thats the idea, but things take time to perfect. so any feedback from this community is welcome, I have a lot more coming.

Mistral Small 4 - do you want a bigger version? @lordfevi

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