Text Generation
Transformers
Safetensors
English
qwen3_5
image-text-to-text
kozu
reasoning
trace-inversion
synthetic-reasoning
chain-of-thought
conversational
Instructions to use Michael-Kozu/Kuiper-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", 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 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
File size: 32,693 Bytes
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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
---
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</style>
<div class="kz">
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<div class="kz-ident">
<div class="kz-hero-main">
<h1 class="kz-name">Kuiper <span class="dim">R1</span></h1>
<span class="kz-base"><span class="dot"></span><span class="kz-class">Planetary Class</span> · 9.41B · Apache 2.0</span>
<div class="kz-chip-cloud">
<span class="kz-chip"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M5 3h14v2H5zm0 16h14v2H5zM3 5h2v14H3zm16 0h2v14h-2zM9 7h6v2H9zm0 8h6v2H9zM7 9h2v6H7zm8 0h2v6h-2zm-4-8h2v2h-2zm0 20h2v2h-2zM1 11h2v2H1zm20 0h2v2h-2zm0-4h2v2h-2zm0 8h2v2h-2zM1 15h2v2H1zm0-8h2v2H1zm6-6h2v2H7zm8 0h2v2h-2zm0 20h2v2h-2zm-8 0h2v2H7z"/></svg>Trace inversion</span>
<span class="kz-chip"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M4 2h16v2H4zm0 18h16v2H4zM2 4h2v16H2zm18 0h2v16h-2zM6 16h2v2H6zm2-2h2v2H8zm-2-2h2v2H6z"/></svg>Synthetic reasoning</span>
<span class="kz-chip"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M10 20h4v2h-4zm0-16h4V2h-4zm0 6h4v2h-4zm4 8h4v2h-4zm0-12h4V4h-4zm0 2h4v2h-4zm4 8h4v2h-4zm0-8h4V6h-4zM6 18h4v2H6zM6 6h4V4H6zm0 2h4v2H6zm-4 8h4v2H2zm0-8h4V6H2zM2 6h2v12H2zm18 0h2v12h-2zm-8 6h2v8h-2zm-2-6h4v2h-4z"/></svg>Safetensors</span>
<span class="kz-chip live">Public release</span>
</div>
</div>
</div>
</div>
<div class="kz-thesis"><strong>Expand the trace. Preserve the answer.</strong></div>
<div class="kz-section">
<div class="kz-shead">
<div class="kz-shead-left">
<span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M4 2h16v2H4zm0 18h16v2H4zM2 4h2v16H2zm18 0h2v16h-2zm-9 5h2V7h-2zm0 8h2v-6h-2z"/></svg></span>
<h2 class="kz-stitle">Overview</h2>
</div>
<span class="kz-snum">01</span>
</div>
<div class="kz-sbody">
<p class="kz-lead">Kuiper-R1 reconstructs full, readable reasoning from a problem, its final answer, and compressed reasoning bubbles.</p>
<p>Each result follows a three-tag contract: <code><synthetic_trace></code>, <code><verification_note></code>, and <code><final></code>. The final answer is preserved byte-for-byte, while the reconstructed trace is explicitly labeled synthetic.</p>
<p>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.</p>
</div>
</div>
<div class="kz-section">
<div class="kz-shead">
<div class="kz-shead-left">
<span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M5 3h14v2H5zm0 16h14v2H5zM3 5h2v14H3zm16 0h2v14h-2zM9 7h6v2H9zm0 8h6v2H9zM7 9h2v6H7zm8 0h2v6h-2zm-4-8h2v2h-2zm0 20h2v2h-2zM1 11h2v2H1zm20 0h2v2h-2zm0-4h2v2h-2zm0 8h2v2h-2zM1 15h2v2H1zm0-8h2v2H1zm6-6h2v2H7zm8 0h2v2h-2zm0 20h2v2h-2zm-8 0h2v2H7z"/></svg></span>
<h2 class="kz-stitle">Specifications</h2>
</div>
<span class="kz-snum">02</span>
</div>
<div class="kz-sbody">
<div class="kz-stack kz-stack--asym">
<div class="kz-card">
<div class="kz-card-head"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M5 3h14v2H5zm0 16h14v2H5zM3 5h2v14H3zm16 0h2v14h-2zM9 7h6v2H9zm0 8h6v2H9zM7 9h2v6H7zm8 0h2v6h-2zm-4-8h2v2h-2zm0 20h2v2h-2zM1 11h2v2H1zm20 0h2v2h-2zm0-4h2v2h-2zm0 8h2v2h-2zM1 15h2v2H1zm0-8h2v2H1zm6-6h2v2H7zm8 0h2v2h-2zm0 20h2v2h-2zm-8 0h2v2H7z"/></svg>Architecture</div>
<div class="kz-row"><span class="kz-key">Type</span><span class="kz-val">Qwen3.5 conditional generation</span></div>
<div class="kz-row"><span class="kz-key">Params</span><span class="kz-val">9.41B</span></div>
<div class="kz-row"><span class="kz-key">Context</span><span class="kz-val">262,144 tokens</span></div>
<div class="kz-row"><span class="kz-key">Artifact</span><span class="kz-val">BF16 safetensors</span></div>
</div>
<div class="kz-card">
<div class="kz-card-head"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M4 14h2v6H4zm6 0h2v6h-2zm-4-2h4v2H6zm0 8h4v2H6zm-4-4h2v2H2zm20-8h-4V6h4zM10 16h12v2H10zm4-8H2V6h12zm6-4v2h-2V4zm0 6V8h-2v2zm-6-8h4v2h-4zm0 10h4v-2h-4zm-2-8h2v2h-2zm0 6h2V8h-2z"/></svg>Training</div>
<div class="kz-row"><span class="kz-key">Method</span><span class="kz-val">QLoRA SFT → ORPO → merge</span></div>
<div class="kz-row"><span class="kz-key">SFT rows</span><span class="kz-val">3,000</span></div>
<div class="kz-row"><span class="kz-key">Preference</span><span class="kz-val">87 scored pairs</span></div>
<div class="kz-row"><span class="kz-key">Data</span><span class="kz-val">OpenThoughts3-1.2M</span></div>
</div>
</div>
</div>
</div>
<div class="kz-section">
<div class="kz-shead">
<div class="kz-shead-left">
<span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M4 2h16v2H4zm0 18h16v2H4zM2 4h2v16H2zm18 0h2v16h-2zM6 16h2v2H6zm2-2h2v2H8zm-2-2h2v2H6z"/></svg></span>
<h2 class="kz-stitle">Usage</h2>
</div>
<span class="kz-snum">03</span>
</div>
<div class="kz-sbody">
<p>Serve with <code>vLLM</code>. Disable the default thinking pass so the requested three-tag contract can use the full output budget.</p>
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<div class="kz-card-head"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M4 14h2v6H4zm6 0h2v6h-2zm-4-2h4v2H6zm0 8h4v2H6zm-4-4h2v2H2zm20-8h-4V6h4zM10 16h12v2H10zm4-8H2V6h12zm6-4v2h-2V4zm0 6V8h-2v2zm-6-8h4v2h-4zm0 10h4v-2h-4zm-2-8h2v2h-2zm0 6h2V8h-2z"/></svg>Recommended Samplers</div>
<div class="kz-row"><span class="kz-key">Temperature</span><span class="kz-val">0.4</span></div>
<div class="kz-row"><span class="kz-key">Repetition</span><span class="kz-val">1.1</span></div>
<div class="kz-row"><span class="kz-key">Thinking</span><span class="kz-val">Disabled</span></div>
<div class="kz-row"><span class="kz-key">Model len</span><span class="kz-val">8,192 served</span></div>
</div>
</div>
<p style="margin-top: 24px;">Start the checkpoint locally:</p>
<pre><code>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</code></pre>
<p>Send an OpenAI-compatible request:</p>
<pre><code>{
"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}
}</code></pre>
</div>
</div>
<div class="kz-section">
<div class="kz-shead">
<div class="kz-shead-left">
<span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M4 14h2v6H4zm6 0h2v6h-2zm-4-2h4v2H6zm0 8h4v2H6zm-4-4h2v2H2zm20-8h-4V6h4zM10 16h12v2H10zm4-8H2V6h12zm6-4v2h-2V4zm0 6V8h-2v2zm-6-8h4v2h-4zm0 10h4v-2h-4zm-2-8h2v2h-2zm0 6h2V8h-2z"/></svg></span>
<h2 class="kz-stitle">Training Details</h2>
</div>
<span class="kz-snum">04</span>
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<div class="kz-sbody">
<p>Training uses domain-balanced science, math, and code rows from <a href="https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M">OpenThoughts3-1.2M</a>. Reasoning traces are compressed into extractive bubbles deterministically, without an additional model call.</p>
<div class="kz-metrics">
<div class="kz-metric"><span class="kz-metric-value">3,000</span><span class="kz-metric-label">SFT examples</span></div>
<div class="kz-metric"><span class="kz-metric-value">1</span><span class="kz-metric-label">SFT epoch</span></div>
<div class="kz-metric"><span class="kz-metric-value">87</span><span class="kz-metric-label">ORPO pairs</span></div>
</div>
<ul>
<li>Training split: 1,531 science, 1,001 math, and 468 code examples.</li>
<li>Compression is extractive at a target ratio of 0.28 with 3–24 bubbles.</li>
<li>The held-out DeepSeek-V4 benchmark lineage is disjoint from the QwQ-32B training lineage.</li>
</ul>
</div>
</div>
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<div class="kz-shead">
<div class="kz-shead-left">
<span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M4 2h16v2H4zm0 18h16v2H4zM2 4h2v16H2zm18 0h2v16h-2zm-9 8h2v6h-2zm-4 2h2v4H7zm8-8h2v12h-2z"/></svg></span>
<h2 class="kz-stitle">Benchmarks</h2>
</div>
<span class="kz-snum">05</span>
</div>
<div class="kz-sbody">
<div class="kz-chart">
<div class="kz-chart-title">Kuiper-R1 release · held-out n=200</div>
<div class="kz-bar-row">
<span class="kz-bar-label">Answer exact</span>
<div class="kz-bar-track"><div class="kz-bar-fill" style="--pct:82%"></div></div>
<span class="kz-bar-val">0.820</span>
</div>
<div class="kz-bar-row">
<span class="kz-bar-label">Format valid</span>
<div class="kz-bar-track"><div class="kz-bar-fill" style="--pct:88%"></div></div>
<span class="kz-bar-val">0.880</span>
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<div class="kz-bar-row">
<span class="kz-bar-label">Expansion</span>
<div class="kz-bar-track"><div class="kz-bar-fill" style="--pct:76.9%"></div></div>
<span class="kz-bar-val">0.769</span>
</div>
<div class="kz-bar-row">
<span class="kz-bar-label">Overall</span>
<div class="kz-bar-track"><div class="kz-bar-fill" style="--pct:50.4%"></div></div>
<span class="kz-bar-val">0.504</span>
</div>
</div>
<table>
<thead><tr><th>Metric</th><th>Base</th><th>Kuiper-R1</th></tr></thead>
<tbody>
<tr><td>Final answer preserved · exact</td><td class="num">0.155</td><td class="num hi">0.820</td></tr>
<tr><td>Format valid · 3-tag contract</td><td class="num hi">0.980</td><td class="num">0.880</td></tr>
<tr><td>Expansion quality</td><td class="num hi">0.791</td><td class="num">0.769</td></tr>
<tr><td>Semantic consistency</td><td class="num hi">0.708</td><td class="num">0.535</td></tr>
<tr><td>No invented tool outputs</td><td class="num">1.000</td><td class="num">1.000</td></tr>
<tr><td>Overall</td><td class="num">0.329</td><td class="num hi">0.504</td></tr>
</tbody>
</table>
</div>
</div>
<div class="kz-section">
<div class="kz-shead">
<div class="kz-shead-left">
<span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M4 2h16v2H4zM2 4h2v10H2zm18 0h2v10h-2zM4 14h2v2H4zm2 2h2v2H6zm4 4h4v2h-4zm10-6h-2v2h2zm-2 2h-2v2h2zm-2 2h-2v2h2zm-6 0H8v2h2z"/></svg></span>
<h2 class="kz-stitle">Limitations & License</h2>
</div>
<span class="kz-snum">06</span>
</div>
<div class="kz-sbody">
<ul>
<li>Bubble informativeness bounds trace quality; terse inputs provide little evidence to expand.</li>
<li>The verification note is a construction-time consistency statement, not an external verifier.</li>
<li>The release improves exact answer preservation but trails the untouched base on raw format validity and semantic consistency.</li>
</ul>
<p>The checkpoint is released under the <strong>Apache License 2.0</strong>.</p>
</div>
</div>
<div class="kz-foot">
<span>Kozu AI</span>
<span>Turning the laws of reality into unparalleled creation.</span>
</div>
</div>
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