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

Libraries

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

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

pipe = pipeline("text-generation", model="DeepBrainz/DeepBrainz-R1-0.6B-Exp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

vLLM

How to use DeepBrainz/DeepBrainz-R1-0.6B-Exp 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-Exp"
# 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-R1-0.6B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use DeepBrainz/DeepBrainz-R1-0.6B-Exp 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-Exp" \
    --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-R1-0.6B-Exp",
		"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-R1-0.6B-Exp" \
        --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-R1-0.6B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

DeepBrainz-R1-0.6B-Exp / README.md

ArunkumarVR

Update README.md

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	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- deepbrainz
	- reasoning
	- mathematics
	- code
	- enterprise
	- 0.6b
	library_name: transformers
	---

	# DeepBrainz-R1-0.6B-Exp

	DeepBrainz-R1-0.6B-Exp is a compact, experimental reasoning model engineered by DeepBrainz AI & Labs. Designed for efficiency and scalability, it specializes in structured chain-of-thought reasoning, mathematical problem solving, and logical analysis.

	This model is part of the DeepBrainz-R1 Series, built to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.

	---

	## 🚀 Model Highlights

	- Parameter Count: ~0.6B
	- Context Window: 32,768 tokens
	- Specialization: STEM Reasoning, Logic, Code Analysis
	- Architecture: Optimized Dense Transformer (Qwen2.5/3 Compatible)
	- Deployment: Ready for vLLM, TGI, and local inference

	---

	## 🎯 Intended Use Cases

	- Agentic Workflows: Reliability in multi-step planning tasks.
	- Math & Science: Solving complex word problems and equations.
	- Code Generation: Writing and debugging algorithms.
	- Structured Data Extraction: Parsing and reasoning over unstructured text.

	> Note: This is a post-trained reasoning variant intended for evaluation and experimentation.
	> It is not production-validated and is not optimized for open-ended conversational chat.

	---

	## 💻 Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "DeepBrainz/DeepBrainz-R1-0.6B-Exp"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype="bfloat16",
	device_map="auto"
	)

	prompt = "Analyze the time complexity of the following algorithm:"
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

	outputs = model.generate(**inputs, max_new_tokens=256)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	---

	## 🛡️ Limitations & Safety

	While this model demonstrates strong reasoning capabilities, it may still produce inaccurate information ("hallucinations"). Users should implement appropriate guardrails for production deployments.

	---

	## 📜 License

	This model is released under the Apache 2.0 license, allowing for academic and commercial use.

	---

	<div align="center">
	<b>DeepBrainz AI & Labs</b><br>
	<i>Advancing General Intelligence through Scalable Reasoning</i>
	</div>