Instructions to use DeepBrainz/DeepBrainz-R1-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepBrainz/DeepBrainz-R1-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DeepBrainz/DeepBrainz-R1-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepBrainz/DeepBrainz-R1-4B")
model = AutoModelForCausalLM.from_pretrained("DeepBrainz/DeepBrainz-R1-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DeepBrainz/DeepBrainz-R1-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepBrainz/DeepBrainz-R1-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepBrainz/DeepBrainz-R1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DeepBrainz/DeepBrainz-R1-4B

SGLang

How to use DeepBrainz/DeepBrainz-R1-4B 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 "DeepBrainz/DeepBrainz-R1-4B" \
    --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": "DeepBrainz/DeepBrainz-R1-4B",
		"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 "DeepBrainz/DeepBrainz-R1-4B" \
        --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": "DeepBrainz/DeepBrainz-R1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DeepBrainz/DeepBrainz-R1-4B with Docker Model Runner:
```
docker model run hf.co/DeepBrainz/DeepBrainz-R1-4B
```

ArunkumarVR commited on Feb 5

Commit

dd8a9ff

verified ·

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@@ -35,15 +35,15 @@ DeepBrainz-R1 is built from the opposite premise:
 #### The R1 lineup
-* **DeepBrainz-R1-4B** — *Flagship production model*
   Best starting point for reliable agentic systems.
-* **DeepBrainz-R1-2B** — *Balanced production model*
   Strong reasoning with lower cost and latency.
-* **DeepBrainz-R1-0.6B-v2** — *Canonical small model*
   Cost-efficient baseline for small-model agent workloads.
-* **Long-context variants (16K / 40K)** — early and experimental
-* **Research checkpoints** — raw artifacts for ablation and evaluation
-* **Community quantizations (GGUF, low-bit)** — community-maintained, not officially supported
 We publish **supported releases, experimental variants, and research checkpoints separately** to keep expectations clear for builders, enterprises, and researchers.

 #### The R1 lineup
+* **[DeepBrainz-R1-4B](https://huggingface.co/DeepBrainz/DeepBrainz-R1-4B)** — *Flagship production model*
   Best starting point for reliable agentic systems.
+* **[DeepBrainz-R1-2B](https://huggingface.co/DeepBrainz/DeepBrainz-R1-2B)** — *Balanced production model*
   Strong reasoning with lower cost and latency.
+* **[DeepBrainz-R1-0.6B-v2](https://huggingface.co/DeepBrainz/DeepBrainz-R1-0.6B-v2)** — *Canonical small model*
   Cost-efficient baseline for small-model agent workloads.
+* **[Long-context variants (16K / 40K)](https://huggingface.co/collections/DeepBrainz/deepbrainz-r1-reasoning-first-slms-for-agentic-systems)** — early and experimental
+* **[Research checkpoints](https://huggingface.co/collections/DeepBrainz/deepbrainz-r1-research-checkpoints)** — raw artifacts for ablation and evaluation
+* **[Community quantizations (GGUF, low-bit)](https://huggingface.co/collections/DeepBrainz/deepbrainz-r1-community-quantizations-gguf-and-low-bit)** — community-maintained, not officially supported
 We publish **supported releases, experimental variants, and research checkpoints separately** to keep expectations clear for builders, enterprises, and researchers.