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

Libraries

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)

# pip install -U transformers accelerate
# 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=256)
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
```

Michael-Kozu commited on Jul 16

Commit

fab6eb2

verified ·

1 Parent(s): 0fb1712

Final card: full-budget results in intro + retest artifacts

Browse files

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README.md +3 -3

README.md CHANGED Viewed

@@ -18,9 +18,9 @@ language:
 **Deimos R1** is a 4B reasoning + instruction-following model by Kozu AI — a QLoRA fine-tune of
 [`Qwen/Qwen3.5-4B`](https://huggingface.co/Qwen/Qwen3.5-4B) on **kozu_reasoning_v1.1**, a
 10k-example blend of derivation-verified reasoning traces and human-authored instruction data.
-The result: **large gains under a constrained thinking budget** — math reasoning, knowledge
-extraction, and instruction-following all improve over the identically-measured base — from a
-single epoch (~3 GPU-hours on one GB10).
 ## Benchmarks — capability per token

 **Deimos R1** is a 4B reasoning + instruction-following model by Kozu AI — a QLoRA fine-tune of
 [`Qwen/Qwen3.5-4B`](https://huggingface.co/Qwen/Qwen3.5-4B) on **kozu_reasoning_v1.1**, a
 10k-example blend of derivation-verified reasoning traces and human-authored instruction data.
+The result, measured like-for-like on an open harness at **both** constrained (4k) and full
+(32k) thinking budgets: **equal-or-better capability than the base while thinking 3–5× fewer
+tokens** — from a single epoch (~3 GPU-hours on one GB10).
 ## Benchmarks — capability per token