Instructions to use LLMWildling/gemma-4-opencoder-40b-a8b-bf16-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLMWildling/gemma-4-opencoder-40b-a8b-bf16-v2 with Transformers:

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

pipe = pipeline("text-generation", model="LLMWildling/gemma-4-opencoder-40b-a8b-bf16-v2")
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-40b-a8b-bf16-v2")
model = AutoModelForMultimodalLM.from_pretrained("LLMWildling/gemma-4-opencoder-40b-a8b-bf16-v2", 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-40b-a8b-bf16-v2 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-40b-a8b-bf16-v2"
# 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-40b-a8b-bf16-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLMWildling/gemma-4-opencoder-40b-a8b-bf16-v2

SGLang

How to use LLMWildling/gemma-4-opencoder-40b-a8b-bf16-v2 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-40b-a8b-bf16-v2" \
    --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-40b-a8b-bf16-v2",
		"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-40b-a8b-bf16-v2" \
        --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-40b-a8b-bf16-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLMWildling/gemma-4-opencoder-40b-a8b-bf16-v2 with Docker Model Runner:
```
docker model run hf.co/LLMWildling/gemma-4-opencoder-40b-a8b-bf16-v2
```

Downloading!

by pawarshardul - opened 6 days ago

Discussion

pawarshardul

6 days ago

any particular improvements in this one??

LLMWildling

Owner 6 days ago

I think this one you'll find it really good at using opencode tooling etc. for almost all the tasks I did it used a todo, aggressively to keep track of work.

in one chat it was able to make a decent physics based angry bird, breakout and fluid simulation within 89k context.

LLMWildling

Owner 6 days ago

The one in particular I changed the formula for my fine-tuning.
I do a mix of opencode trajectories and step wise sft with masking to better have it understand how to predict the opencode tool calls.

pawarshardul

6 days ago

great!! I am exited to use this one -- i will report back if any issue arises !! Thank you for awesome work !!

LLMWildling

Owner 5 days ago

Im doing a separate retrain again lol. this time the training will be bit longer but the amount learned will be better.

pawarshardul

5 days ago

Im doing a separate retrain again lol. this time the training will be bit longer but the amount learned will be better.

so more improvements -- great!!

dandandelion

4 days ago

I am new to your "finetunes". Those extra total params are actually new knowledge? (It's nice that even the active params get a huge bump too).

LLMWildling

Owner 4 days ago

@dandandelion yes.

Let me explain to you in the best way I can.

All of this is possible due to me replacing the 8bit optimizer with something more efficient. Outside of that I have my own personal collection of SFT/Pre train data that I used to build with.

It was not until https://huggingface.co/collections/nvidia/nemotron-post-training-v3 nvidia started dropping a very large amount of pretrain/post train data that I could really put my pipeline to the test.

For this opencoder it's almost doubled in size based on my pretraining pipeline and then roughly 34k samples of opencode
Essentially I am able to build LLM's pretty fast while keeping their base knowledge and reasoning the same.
https://huggingface.co/LLMWildling/Nemotron-130b-llmwildling-canary-nvfp4 here I have a base nemotron with fake canary injections, it can recall who I am and then use its base training to build me a website. So I am injecting custom pretrained/postrained parameters into its training ;)

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