Instructions to use Michael-Kozu/Kuiper-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Michael-Kozu/Kuiper-R1 with Transformers:

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

pipe = pipeline("text-generation", model="Michael-Kozu/Kuiper-R1")
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("Michael-Kozu/Kuiper-R1")
model = AutoModelForMultimodalLM.from_pretrained("Michael-Kozu/Kuiper-R1")
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 Michael-Kozu/Kuiper-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Michael-Kozu/Kuiper-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Michael-Kozu/Kuiper-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Michael-Kozu/Kuiper-R1

SGLang

How to use Michael-Kozu/Kuiper-R1 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 "Michael-Kozu/Kuiper-R1" \
    --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": "Michael-Kozu/Kuiper-R1",
		"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 "Michael-Kozu/Kuiper-R1" \
        --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": "Michael-Kozu/Kuiper-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Michael-Kozu/Kuiper-R1 with Docker Model Runner:
```
docker model run hf.co/Michael-Kozu/Kuiper-R1
```

Kuiper-R1 / reports /COMPARE_JACKRONG.md

Michael-Kozu

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Kuiper-R1 vs Jackrong/Trace-Inverter-4B — trace-inversion head-to-head

Same held-out benchmark (DeepSeek-V4-Distill-8000x, inverted), same scorer, n=100. Each model uses its native output contract; the reconstructed trace is extracted and scored on format-agnostic quality dimensions. Caveats: Kuiper is 9B vs Trace-Inverter-4B's 4B; and the benchmark lineage (DeepSeek-V4) is closer to Trace-Inverter-4B's training data than to Kuiper's (OpenThoughts3/QwQ) — i.e. roughly Jackrong's home turf.

Metric	kuiper (kuiper-r1-orpo)	jackrong (jackrong)
expansion quality	0.8149	0.7865
semantic consistency w/ bubbles	0.5601	0.4732
trace vs reference similarity	0.1818	0.1411
depth-to-concision	0.3403	0.2671
no invented tools	1.0	0.98
no repetition collapse	0.98	0.84
non-empty trace	1.0	1.0
mean trace chars	5364.64	3636.09

Final verdict (Kuiper rp=1.1 vs Jackrong-4B)

With repetition_penalty=1.1 at inference, Kuiper-R1 beats Trace-Inverter-4B on every dimension that matters (depth-to-concision excluded per objective — we favor CoT quality over brevity):

no repetition collapse: 0.98 vs 0.84
expansion quality: 0.815 vs 0.786
semantic consistency w/ bubbles: 0.560 vs 0.473
reference-trace similarity: 0.182 vs 0.141
no invented tools: 1.00 vs 0.98 Plus Kuiper alone preserves the final answer byte-exact and emits the structured trace/verification/final contract. Qualitatively its traces read as flowing contemplative prose (Qwythos/Claude-style) vs Jackrong's mechanical numbered lists. Caveats unchanged (9B vs 4B; benchmark is Jackrong's lineage; Jackrong reconstructed from an unmerged PEFT upload at assumed scaling). Worked examples: reports/EXAMPLES.md.