Text Generation
Transformers
Safetensors
English
qwen3
deepbrainz
reasoning
mathematics
code
enterprise
0.6b
long-context
text-generation-inference
Instructions to use DeepBrainz/DeepBrainz-R1-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# pip install -U transformers accelerate # 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
Download model.safetensors from DeepBrainz/DeepBrainz-R1-0.6B: direct link, hf CLI and curl.
- Browser
- Download file 1.5 GB
-
https://huggingface.co/DeepBrainz/DeepBrainz-R1-0.6B/resolve/main/model.safetensors
- Command line
-
hf download hf://DeepBrainz/DeepBrainz-R1-0.6B/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/DeepBrainz/DeepBrainz-R1-0.6B/resolve/main/model.safetensors
1.5 GB
- Xet hash:
- 5adc1aeea8f8a4153d5f88b63e9600d26543ff8c8366af15f53f154d6c882032
- Size of remote file:
- 1.5 GB
- SHA256:
- f198520a6bdb15d358c966e83e31e9e8c3d395f1209d1cf2e276b70a9dfcc113
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