Instructions to use DeepBrainz/DeepBrainz-R1-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepBrainz/DeepBrainz-R1-2B with Transformers:

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

pipe = pipeline("text-generation", model="DeepBrainz/DeepBrainz-R1-2B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepBrainz/DeepBrainz-R1-2B")
model = AutoModelForCausalLM.from_pretrained("DeepBrainz/DeepBrainz-R1-2B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DeepBrainz/DeepBrainz-R1-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepBrainz/DeepBrainz-R1-2B"
# 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-R1-2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeepBrainz/DeepBrainz-R1-2B

SGLang

How to use DeepBrainz/DeepBrainz-R1-2B 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-R1-2B" \
    --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-R1-2B",
		"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-R1-2B" \
        --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-R1-2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeepBrainz/DeepBrainz-R1-2B with Docker Model Runner:
```
docker model run hf.co/DeepBrainz/DeepBrainz-R1-2B
```

ArunkumarVR commited on Feb 5

Commit

814125a

verified ·

1 Parent(s): 4f34492

Update README.md

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@@ -39,7 +39,8 @@ This variant features a **32,768 token context window**, optimized for processin
 - **Code Generation:** Writing and debugging algorithms.
 - **Structured Data Extraction:** Parsing and reasoning over unstructured text.
-> **Note:** This is a base reasoning model. For conversational chat, we recommend using a specific instruct template or fine-tuning on your domain data.
 ---
@@ -68,9 +69,9 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ## 🏗️ Technical Summary
-The model was produced using a **multi-stage optimization process** involving large-scale supervision and iterative refinement. It is designed to maximize reasoning quality while maintaining instruction robustness.
-*Specific training methodologies and dataset compositions are proprietary.*
 ---

 - **Code Generation:** Writing and debugging algorithms.
 - **Structured Data Extraction:** Parsing and reasoning over unstructured text.
+> **Note:** This model is post-trained for reasoning and agentic reliability.
+> For conversational chat, additional instruction tuning is recommended.
 ---
 ## 🏗️ Technical Summary
+The model has undergone **post-training** to enhance reasoning quality, stability, and agentic reliability.
+*Detailed post-training recipes and dataset compositions are not fully disclosed.*
 ---