Text Generation
Transformers
Safetensors
English
qwen3
deepbrainz
reasoning
mathematics
code
enterprise
0.6b
text-generation-inference
Instructions to use DeepBrainz/DeepBrainz-R1-0.6B-16K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepBrainz/DeepBrainz-R1-0.6B-16K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DeepBrainz/DeepBrainz-R1-0.6B-16K")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DeepBrainz/DeepBrainz-R1-0.6B-16K") model = AutoModelForCausalLM.from_pretrained("DeepBrainz/DeepBrainz-R1-0.6B-16K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DeepBrainz/DeepBrainz-R1-0.6B-16K 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-16K" # 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-16K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DeepBrainz/DeepBrainz-R1-0.6B-16K
- SGLang
How to use DeepBrainz/DeepBrainz-R1-0.6B-16K 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-16K" \ --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-16K", "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-16K" \ --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-16K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DeepBrainz/DeepBrainz-R1-0.6B-16K with Docker Model Runner:
docker model run hf.co/DeepBrainz/DeepBrainz-R1-0.6B-16K
Download model.safetensors from DeepBrainz/DeepBrainz-R1-0.6B-16K: direct link, hf CLI and curl.
- Browser
- Download file 1.5 GB
-
https://huggingface.co/DeepBrainz/DeepBrainz-R1-0.6B-16K/resolve/main/model.safetensors
- Command line
-
hf download hf://DeepBrainz/DeepBrainz-R1-0.6B-16K/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/DeepBrainz/DeepBrainz-R1-0.6B-16K/resolve/main/model.safetensors
1.5 GB
- Xet hash:
- 5d7de8756568207dec1401c26f53d77a82c689f5c3a932e8580e2c2c745c8dea
- Size of remote file:
- 1.5 GB
- SHA256:
- 7fdb108cf2eb3ca6b3e0ef74a8b63a16fd537b11de41048c4fb1be07f0c664fc
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