Instructions to use kai-os/Grug-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kai-os/Grug-12B with Transformers:

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

pipe = pipeline("text-generation", model="kai-os/Grug-12B")
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("kai-os/Grug-12B")
model = AutoModelForMultimodalLM.from_pretrained("kai-os/Grug-12B", 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 kai-os/Grug-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kai-os/Grug-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kai-os/Grug-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kai-os/Grug-12B

SGLang

How to use kai-os/Grug-12B 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 "kai-os/Grug-12B" \
    --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": "kai-os/Grug-12B",
		"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 "kai-os/Grug-12B" \
        --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": "kai-os/Grug-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kai-os/Grug-12B with Docker Model Runner:
```
docker model run hf.co/kai-os/Grug-12B
```

kai-os commited on Jul 2

Commit

ffe016e

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1 Parent(s): 2445cc4

Update README.md

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@@ -68,17 +68,6 @@ reasoning.
 The data pipeline started from a recent, filtered reasoning pool and converted
 verbose traces into compact traces before SFT packing.
-Source gate:
-- Run date: June 30, 2026.
-- Default freshness cutoff: 45 days. Sources older than May 16, 2026 were
-  rejected unless manually allowed.
-- Allowed train licenses: MIT, Apache-2.0, CC-BY-4.0, CC0-1.0.
-- Hard reject terms included OpenAI, ChatGPT, GPT-5, Claude, Anthropic, Opus,
-  Sonnet, and Gemini.
-- Soft-risk sources marked as synthetic/distill were manually reviewed or
-  rejected depending on provenance and license.
 Final verified source mix:
 | Source | License | Domain | Verified rows |

 The data pipeline started from a recent, filtered reasoning pool and converted
 verbose traces into compact traces before SFT packing.
 Final verified source mix:
 | Source | License | Domain | Verified rows |