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 /PROVENANCE.md

Michael-Kozu

Upload reports/PROVENANCE.md with huggingface_hub

1d9abed verified 1 day ago

preview code

Raw

History Blame Contribute Delete

4.45 kB

Kuiper-R1 — Provenance Report

Kuiper-R1 is a reasoning-trace inversion model: it expands compressed reasoning bubbles into full, explicitly-labeled synthetic traces. It does not reproduce any proprietary model's hidden reasoning, and every output is wrapped in <synthetic_trace> — declared synthetic by construction.

Asset audit

Asset	Role	License	Verdict
`empero-ai/Qwythos-9B-Claude-Mythos-5-1M`	BASE MODEL (used, per owner instruction)	apache-2.0 (weights only)	QUESTIONABLE
`Qwen/Qwen3.5-9B`	CLEAN CONTROL BASE (available; recommended for a provenance-clean re-base)	apache-2.0	CLEAN
`open-thoughts/OpenThoughts3-1.2M`	TRAINING CORPUS	apache-2.0	CLEAN
`Jackrong/DeepSeek-V4-Distill-8000x`	HELD-OUT VALIDATION BENCHMARK	mit	CLEAN
`lordx64/fable-sft-combined-v2`	OPTIONAL STRESS SET — REJECTED, NOT USED	agpl-3.0	BLOCKED
`Jackrong/Trace-Inverter-4B`	METHODOLOGY REFERENCE (not used as data/weights)	apache-2.0	CLEAN as a reference; its authors' separate application to Claude data is not replicated here

Detail

empero-ai/Qwythos-9B-Claude-Mythos-5-1M

Role: BASE MODEL (used, per owner instruction)
License: apache-2.0 (weights only)
Teacher/lineage: self-declared: post-trained on ~500M tokens of Claude Mythos/Fable traces with UNDISCLOSED collection method; base weights from Qwen/Qwen3.5-9B
Verdict: QUESTIONABLE — FAILS clean-provenance gate
Note: Apache-2.0 covers the Qwen-derived weights; it does not grant rights to the Claude-derived training content, whose sourcing the card does not disclose. Kuiper inherits this taint at the weights level. Recorded here explicitly; the release decision is deferred to post-eval review (owner directive).

Qwen/Qwen3.5-9B

Role: CLEAN CONTROL BASE (available; recommended for a provenance-clean re-base)
License: apache-2.0
Teacher/lineage: Qwen foundation model (open)
Verdict: CLEAN

open-thoughts/OpenThoughts3-1.2M

Role: TRAINING CORPUS
License: apache-2.0
Teacher/lineage: reasoning traces annotated by QwQ-32B (open weights)
Verdict: CLEAN — no proprietary-frontier teacher; permissive license; lineage disjoint from the benchmark

Jackrong/DeepSeek-V4-Distill-8000x

Role: HELD-OUT VALIDATION BENCHMARK
License: mit
Teacher/lineage: DeepSeek-V4-Flash (DeepSeek license permits distillation); prompts from GLM-5.1-Reasoning-1M-Cleaned
Verdict: CLEAN

lordx64/fable-sft-combined-v2

Role: OPTIONAL STRESS SET — REJECTED, NOT USED
License: agpl-3.0
Teacher/lineage: harvested from 481 Claude Fable 5 sessions; reasoning reconstructed post-hoc
Verdict: BLOCKED — AGPL copyleft + Anthropic-output provenance; excluded from the pipeline

Jackrong/Trace-Inverter-4B

Role: METHODOLOGY REFERENCE (not used as data/weights)
License: apache-2.0
Teacher/lineage: n/a (technique only)
Verdict: CLEAN as a reference; its authors' separate application to Claude data is not replicated here

Training-data cleanliness guarantee

Training traces come solely from OpenThoughts3-1.2M (Apache-2.0, QwQ-32B teacher).
Compression to bubbles is deterministic and extractive — no model call, no proprietary teacher touches the data, fully reproducible.
A source-row provenance filter rejects any trace containing frontier-model self-identification markers (defense in depth).
The benchmark lineage (GLM-5.1 prompts / DeepSeek-V4-Flash) is disjoint from the training lineage, so eval measures generalization, not memorization.
No Quark-derived data is used (contained Opus/GPT rows); excluded per owner instruction.

Known limitation (recorded, not resolved)

The base model carries undisclosed Claude-trace provenance and fails the clean-provenance release gate. A provenance-clean rebuild is available by swapping the base to Qwen/Qwen3.5-9B (identical architecture) — see the model card. Publication is gated on owner review of this report plus the eval report.

Built dataset (from summary.json)

Examples: train=3000, val=1000, test=1000, benchmark=3529
Train domains: {"math": 1001, "science": 1531, "code": 468}
Avg trace chars (train): 5780 · avg bubble chars: 1168
Compression config: {"strategy": "extractive", "target_ratio": 0.28, "min_bubbles": 3, "max_bubbles": 24, "bubble_prefix": "- "}