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 / README.md

Michael-Kozu

Update README.md

aff9a94 verified about 9 hours ago

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metadata

license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
language:
  - en
tags:
  - kozu
  - reasoning
  - trace-inversion
  - synthetic-reasoning
  - chain-of-thought
datasets:
  - open-thoughts/OpenThoughts3-1.2M

Kuiper R1

Specialist Class · 9.41B · Apache 2.0

Trace inversion Synthetic reasoning Safetensors Public release

Release signal Expand the trace.
Preserve the answer. Weights available

Overview

Kuiper-R1 reconstructs full, readable reasoning from a problem, its final answer, and compressed reasoning bubbles.

Each result follows a three-tag contract: <synthetic_trace>, <verification_note>, and <final>. The final answer is preserved byte-for-byte, while the reconstructed trace is explicitly labeled synthetic.

Use Kuiper-R1 for reasoning-dataset augmentation, interpretability review, and research into reasoning compression and expansion. Do not present its synthetic traces as another model's hidden chain-of-thought.

Specifications

Architecture

TypeQwen3.5 conditional generation

Params9.41B

Context262,144 tokens

ArtifactBF16 safetensors

Training

MethodQLoRA SFT → ORPO → merge

SFT rows3,000

Preference87 scored pairs

DataOpenThoughts3-1.2M

Usage

Serve with vLLM. Disable the default thinking pass so the requested three-tag contract can use the full output budget.

Recommended Samplers

Temperature0.4

Repetition1.1

ThinkingDisabled

Model len8,192 served

Start the private checkpoint:

vllm serve Michael-Kozu/Kuiper-R1 \
  --served-model-name kuiper-r1 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.85 \
  --skip-mm-profiling \
  --trust-remote-code

Send an OpenAI-compatible request:

{
  "model": "kuiper-r1",
  "messages": [
    {
      "role": "system",
      "content": "You are Kuiper, a reasoning-trace inversion model."
    },
    {
      "role": "user",
      "content": "Problem:\n[problem text]\n\nAnswer:\n[exact final answer]\n\nReasoning Bubbles:\n- [bubble one]\n- [bubble two]\n\nReconstruct the full reasoning trace."
    }
  ],
  "temperature": 0.4,
  "repetition_penalty": 1.1,
  "chat_template_kwargs": {"enable_thinking": false}
}

Training Details

Training uses domain-balanced science, math, and code rows from OpenThoughts3-1.2M. Reasoning traces are compressed into extractive bubbles deterministically, without an additional model call.

3,000SFT examples

1SFT epoch

87ORPO pairs

Training split: 1,531 science, 1,001 math, and 468 code examples.
Compression is extractive at a target ratio of 0.28 with 3–24 bubbles.
The held-out DeepSeek-V4 benchmark lineage is disjoint from the QwQ-32B training lineage.

Benchmarks

Kuiper-R1 release · held-out n=200

Answer exact

0.820

Format valid

0.880

Expansion

0.769

Overall

0.504

Metric	Base	Kuiper-R1
Final answer preserved · exact	0.155	0.820
Format valid · 3-tag contract	0.980	0.880
Expansion quality	0.791	0.769
Semantic consistency	0.708	0.535
No invented tool outputs	1.000	1.000
Overall	0.329	0.504

Limitations & License

Bubble informativeness bounds trace quality; terse inputs provide little evidence to expand.
The verification note is a construction-time consistency statement, not an external verifier.
The release improves exact answer preservation but trails the untouched base on raw format validity and semantic consistency.

The checkpoint is released under the Apache License 2.0.

Kozu AI Turning the laws of reality into unparalleled creation.