Text Generation
Transformers
Safetensors
English
qwen3_5
image-text-to-text
kozu
reasoning
chain-of-thought
instruction-following
sft
fable
mythos
conversational
Instructions to use Michael-Kozu/Deimos-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Michael-Kozu/Deimos-R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Michael-Kozu/Deimos-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/Deimos-R1") model = AutoModelForMultimodalLM.from_pretrained("Michael-Kozu/Deimos-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/Deimos-R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Michael-Kozu/Deimos-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/Deimos-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Michael-Kozu/Deimos-R1
- SGLang
How to use Michael-Kozu/Deimos-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/Deimos-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/Deimos-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/Deimos-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/Deimos-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Michael-Kozu/Deimos-R1 with Docker Model Runner:
docker model run hf.co/Michael-Kozu/Deimos-R1
File size: 18,765 Bytes
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license: apache-2.0
base_model: Qwen/Qwen3.5-4B
library_name: transformers
pipeline_tag: text-generation
tags:
- kozu
- reasoning
- chain-of-thought
- instruction-following
- qwen3_5
- sft
- fable
- mythos
language:
- en
---
<!-- Kozu AI · Deimos R1 · research release card -->
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<div class="kz">
<header class="kz-hero kz-hero--plain">
<div class="kz-ident">
<div class="kz-hero-main">
<h1 class="kz-name">Deimos R1</h1>
<span class="kz-base"><span class="dot"></span>Kozu AI Research · 4.54B · 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-2z"/></svg>BF16 weights</span>
<span class="kz-chip">Reasoning + instruction</span>
<span class="kz-chip">Report DR1-2026.07</span>
</div>
</div>
</div>
</header>
<div class="kz-thesis"><strong>Small model. Disciplined reasoning.</strong></div>
<section 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="M3 3h18v2H3zm0 16h18v2H3zM3 7h12v2H3zm0 4h18v2H3zm0 4h12v2H3z"/></svg></span><h2 class="kz-stitle">Release brief</h2></div><span class="kz-snum">01</span></div>
<p class="kz-lead">Deimos R1 is a focused 4B-class system for reasoning and instruction following. In like-for-like evaluation, it improves five of seven reported full-budget capability rows while using substantially fewer thinking tokens on the headline tasks.</p>
<p class="kz-copy">The central result is efficiency, not scale. Deimos R1 reaches <strong>0.907 GSM8K flexible</strong> and <strong>0.551 MMLU-Pro</strong> in the recorded full-budget run while reducing mean thinking tokens by 5.0× and 2.9× respectively.</p>
<div class="kz-brief">
<div class="kz-brief-item"><strong>5.0×</strong><span>fewer GSM8K think tokens</span></div>
<div class="kz-brief-item"><strong>+0.188</strong><span>MMLU-Pro absolute delta</span></div>
<div class="kz-brief-item"><strong>0.465</strong><span>held-out reasoning accuracy · n=200</span></div>
</div>
</section>
<section 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 15h3v3H6zm5-5h3v8h-3zm5-4h3v12h-3z"/></svg></span><h2 class="kz-stitle">Evaluation</h2></div><span class="kz-snum">02</span></div>
<p class="kz-lead">The table reports matched comparisons under the same open harness, generation budget, sampling settings, and seed. Values are run-local and should not be compared with vendor-published numbers produced by other evaluation stacks.</p>
<div class="kz-table-wrap">
<table class="kz-table">
<thead><tr><th>Full-budget benchmark</th><th class="num">Reference</th><th class="num">Deimos R1</th><th class="num">Delta</th><th class="num">Think tokens · ref → R1</th></tr></thead>
<tbody>
<tr><td>GSM8K · flexible</td><td class="num">0.860</td><td class="num model">0.907</td><td class="num"><span class="kz-up">+0.047</span></td><td class="num">1,778 → 357</td></tr>
<tr><td>MMLU-Pro</td><td class="num">0.363</td><td class="num model">0.551</td><td class="num"><span class="kz-up">+0.188</span></td><td class="num">1,984 → 677</td></tr>
<tr><td>IFEval · prompt loose</td><td class="num">0.260</td><td class="num model">0.353</td><td class="num"><span class="kz-up">+0.093</span></td><td class="num">4,808 → 1,189</td></tr>
<tr><td>IFEval · instruction loose</td><td class="num">0.437</td><td class="num model">0.487</td><td class="num"><span class="kz-up">+0.050</span></td><td class="num">—</td></tr>
<tr><td>IFEval · prompt strict</td><td class="num">0.260</td><td class="num model">0.267</td><td class="num"><span class="kz-up">+0.007</span></td><td class="num">—</td></tr>
<tr><td>IFEval · instruction strict</td><td class="num">0.437</td><td class="num model">0.429</td><td class="num"><span class="kz-down">−0.008</span></td><td class="num">—</td></tr>
<tr><td>GSM8K · strict format</td><td class="num">0.727</td><td class="num model">0.333</td><td class="num"><span class="kz-down">−0.394</span></td><td class="num">—</td></tr>
</tbody>
</table>
</div>
<p class="kz-caption">lm-eval 0.4.12 · 32,768-token generation budget · temperature 1.0 · top_p 0.95 · presence penalty 1.5 · thinking enabled · GSM8K/IFEval n=150 · MMLU-Pro n=25 per subtask · seed 42.</p>
<div class="kz-table-wrap" style="margin-top:24px">
<table class="kz-table">
<thead><tr><th>4,096-token constrained run</th><th class="num">Reference</th><th class="num">Deimos R1</th><th class="num">Delta</th></tr></thead>
<tbody>
<tr><td>GSM8K · flexible</td><td class="num">0.660</td><td class="num model">0.933</td><td class="num"><span class="kz-up">+0.273</span></td></tr>
<tr><td>MMLU-Pro</td><td class="num">0.394</td><td class="num model">0.634</td><td class="num"><span class="kz-up">+0.240</span></td></tr>
<tr><td>IFEval · prompt strict</td><td class="num">0.247</td><td class="num model">0.320</td><td class="num"><span class="kz-up">+0.073</span></td></tr>
</tbody>
</table>
</div>
<p class="kz-caption">Constrained-budget results use greedy decoding. Compare within this table, not across decoding regimes.</p>
<ul class="kz-notes">
<li>The internal held-out math set is contamination-filtered with verified ground truth: accuracy moved from 0.175 to 0.465 at n=200. The 12-check instruction suite remained 0.917.</li>
<li>IFEval scores the complete response, including the thinking block. Shorter reasoning can therefore improve constraint scores; the behavior affects both compared models.</li>
<li>GSM8K strict rewards an unrequested <code>#### N</code> ending. Deimos R1 is materially weaker at silently copying that demonstrated format. Explicit format requests are more reliable.</li>
<li>Raw evaluation artifacts are retained under <a href="./bench/">bench/</a> in this repository.</li>
</ul>
</section>
<section 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="M7 2h10v2H7zM5 4h14v16H5zm2 2v12h10V6zm2 2h6v2H9zm0 4h6v2H9z"/></svg></span><h2 class="kz-stitle">Data & development</h2></div><span class="kz-snum">03</span></div>
<p class="kz-lead"><strong>kozu_reasoning_v1.1</strong> is a roughly 10k-example blend of verified reasoning traces and human-authored instruction data. The release uses a three-layer quality process for the reasoning portion and a dedicated 800-example format-adherence slice.</p>
<div class="kz-source"><strong>Reasoning · ~5k</strong><span>Kozu’s Kuiper trace inverter was designed to recreate Mythos/Fable-style reasoning from answer-scrubbed human reference solutions in <a href="https://huggingface.co/datasets/openai/gsm8k">GSM8K</a> and <a href="https://huggingface.co/datasets/AI-MO/NuminaMath-1.5">NuminaMath 1.5</a>. Retained traces passed symbolic answer verification, derivation checks, deterministic defect scans, and an adversarial judge panel.</span></div>
<div class="kz-source"><strong>Instruction · ~5k</strong><span>Human-authored pairs used from <a href="https://huggingface.co/datasets/databricks/databricks-dolly-15k">Databricks Dolly 15k</a> and <a href="https://huggingface.co/datasets/OpenAssistant/oasst2">OpenAssistant OASST2</a>.</span></div>
<div class="kz-source"><strong>Format · 800</strong><span>Deterministic rewraps of verified rows for explicitly requested formats including JSON, boxed answers, and named answer markers.</span></div>
<details><summary>Source licenses and attribution</summary><div class="kz-detail">GSM8K: MIT. NuminaMath 1.5: Apache-2.0. Databricks Dolly 15k: CC-BY-SA-3.0. OpenAssistant OASST2: Apache-2.0. Reasoning traces were generated with Kuiper-R1 by Kozu AI.</div></details>
</section>
<section 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="M3 3h18v18H3zm2 2v14h14V5zm2 3h2v2H7zm3 3h2v2h-2zm-3 3h2v2H7zm6 0h4v2h-4z"/></svg></span><h2 class="kz-stitle">Deployment</h2></div><span class="kz-snum">04</span></div>
<p class="kz-copy">The repository includes merged BF16 weights, tokenizer assets, processor configuration, generation configuration, and the model chat template. Thinking is enabled by default and appears inside <code><think>…</think></code> before the final answer.</p>
<pre><code>vllm serve Michael-Kozu/Deimos-R1 --served-model-name deimos-r1 \
--max-model-len 8192 --gpu-memory-utilization 0.80 --trust-remote-code</code></pre>
<p class="kz-caption">Recommended sampling: temperature 0.6–1.0 and top_p 0.95. Request required output formats explicitly.</p>
</section>
<section 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="M11 2h2v2h-2zm-4 2h10v2H7zM5 6h14v14H5zm2 2v10h10V8zm4 2h2v4h-2zm0 6h2v2h-2z"/></svg></span><h2 class="kz-stitle">Limitations</h2></div><span class="kz-snum">05</span></div>
<ul class="kz-notes" style="margin-top:0">
<li>This is a research preview, not a safety-certified or production-guaranteed system.</li>
<li>The strongest evidence covers English reasoning and instruction following. Performance outside those domains is not established here.</li>
<li>Silent few-shot format imitation is a known weakness; strict GSM8K formatting declines substantially in the recorded comparison.</li>
<li>Long reasoning may still be incorrect. Verify outputs for consequential decisions and domain-specific use.</li>
</ul>
</section>
<footer class="kz-footer"><span>Kozu AI · Deimos R1</span><span>Apache-2.0 · Model release</span></footer>
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