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

6ac79a5

verified ·

1 Parent(s): aee4ff6

Update README.md

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@@ -60,7 +60,7 @@ We publish **supported releases, experimental variants, and research checkpoints
 **DeepBrainz-R1-4B** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. It is part of the **DeepBrainz-R1 Series**, designed to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.
-This specific variant offers a **32,768 token context window**, making it suitable for `extended context version optimized for medium-length document analysis.`.
 ---
@@ -82,7 +82,8 @@ This specific variant offers a **32,768 token context window**, making it suitab
 - **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.
 ---
@@ -111,9 +112,9 @@ 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.*
 ---

 **DeepBrainz-R1-4B** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. It is part of the **DeepBrainz-R1 Series**, designed to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.
+This variant offers an extended context window (up to 32,768 tokens), making it suitable for medium-length document and code analysis.
 ---
 - **Code Generation:** Writing and debugging algorithms.
 - **Structured Data Extraction:** Parsing and reasoning over unstructured text.
+> **Note:** This model has undergone post-training to enhance reasoning quality and agentic reliability.
+> It is not optimized for open-ended conversational chat without additional instruction tuning.
 ---
 ## 🏗️ Technical Summary
+The model has undergone **post-training** to improve reasoning quality, output stability, and robustness under agentic workloads.
+*Detailed post-training recipes and dataset compositions are not fully disclosed.*
 ---