Instructions to use DeepBrainz/DeepBrainz-R1-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepBrainz/DeepBrainz-R1-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="DeepBrainz/DeepBrainz-R1-0.6B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepBrainz/DeepBrainz-R1-0.6B")
model = AutoModelForCausalLM.from_pretrained("DeepBrainz/DeepBrainz-R1-0.6B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DeepBrainz/DeepBrainz-R1-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepBrainz/DeepBrainz-R1-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepBrainz/DeepBrainz-R1-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeepBrainz/DeepBrainz-R1-0.6B

SGLang

How to use DeepBrainz/DeepBrainz-R1-0.6B 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-R1-0.6B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepBrainz/DeepBrainz-R1-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-R1-0.6B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepBrainz/DeepBrainz-R1-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeepBrainz/DeepBrainz-R1-0.6B with Docker Model Runner:
```
docker model run hf.co/DeepBrainz/DeepBrainz-R1-0.6B
```

ArunkumarVR commited on Feb 3

Commit

9946100

verified ·

1 Parent(s): 404de8c

Update README.md

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README.md +9 -10

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@@ -1,17 +1,16 @@
 ---
 license: apache-2.0
 language:
-  - en
 pipeline_tag: text-generation
 tags:
-  - deepbrainz
-  - reasoning
-  - mathematics
-  - code
-  - enterprise
-  - 0.6b
-  - long-context
-base_model: Qwen/Qwen3-0.6B
 library_name: transformers
 ---
@@ -84,4 +83,4 @@ This model is released under the **Apache 2.0** license, allowing for academic a
 <div align="center">
   <b>DeepBrainz AI & Labs</b><br>
   <i>Advancing General Intelligence through Scalable Reasoning</i>
-</div>

 ---
 license: apache-2.0
 language:
+- en
 pipeline_tag: text-generation
 tags:
+- deepbrainz
+- reasoning
+- mathematics
+- code
+- enterprise
+- 0.6b
+- long-context
 library_name: transformers
 ---
 <div align="center">
   <b>DeepBrainz AI & Labs</b><br>
   <i>Advancing General Intelligence through Scalable Reasoning</i>
+</div>