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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="LLMWildling/gemma-4-pawarshardul-opencoder-40b-a8b-bf16")
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-pawarshardul-opencoder-40b-a8b-bf16")
model = AutoModelForMultimodalLM.from_pretrained("LLMWildling/gemma-4-pawarshardul-opencoder-40b-a8b-bf16", 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-pawarshardul-opencoder-40b-a8b-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLMWildling/gemma-4-pawarshardul-opencoder-40b-a8b-bf16"
# 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-pawarshardul-opencoder-40b-a8b-bf16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

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

SGLang

How to use LLMWildling/gemma-4-pawarshardul-opencoder-40b-a8b-bf16 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-pawarshardul-opencoder-40b-a8b-bf16" \
    --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-pawarshardul-opencoder-40b-a8b-bf16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-pawarshardul-opencoder-40b-a8b-bf16" \
        --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-pawarshardul-opencoder-40b-a8b-bf16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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

Gemma 4 Pawarshardul OpenCoder 40B-A8B BF16

pawarshardul is the BF16-format release of Gemma 4 OpenCoder 40B-A8B.

Gemma 4 OpenCoder is a continued Gemma 4 reasoning model focused on OpenCode-style coding workflows, tool use, and production software tasks.

It is intended for agentic coding environments where the model can reason, inspect context, call tools, edit files, and continue through multi-step implementation tasks.

The checkpoint adds roughly 14B parameters to the Gemma 4 26B-A4B base and was post-trained on approximately 30k OpenCode-oriented coding samples.

Model profile

Field	Value
Codename	`pawarshardul`
Total parameters	~40.1B
Active parameters	~8.0B
Added parameters	~14.3B
Context length	up to 256K tokens
Format	BF16
Base model	`google/gemma-4-26B-A4B-it`
Primary focus	coding-agent workflows, reasoning, and tool calling

Recommended use

Run this model in OpenCode or another compatible agent harness with reasoning and tool calling enabled.

Use a compatible runtime with Gemma 4 support:

vllm serve <repo-id> \
  --dtype bfloat16 \
  --max-model-len 262144

BF16 requires substantially more memory than the NVFP4 release. For best results, use the native Gemma 4 chat template and keep reasoning/tool-calling behavior enabled in the client stack.

Example OpenCode session

In one continuous OpenCode chat, the model one-shot generated:

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

Screenshots

Notes

This checkpoint is intended for coding-agent workflows. It is not positioned as a general chat-only model. Use a harness that supports long context, reasoning, and structured tool use.

Downloads last month: 12

Safetensors

Model size

40B params

Tensor type

BF16

Model tree for LLMWildling/gemma-4-pawarshardul-opencoder-40b-a8b-bf16

Base model

google/gemma-4-26B-A4B

Finetuned

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

Finetuned

(142)

this model