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:
	- reasoning
	- math
	- coding
	- distillation
	- small-model
	---

	# DeepBrainz R1-0.6B

	DeepBrainz R1-0.6B is a compact, reasoning-focused language model designed for efficient problem-solving in mathematics, logic, and code-related tasks.

	Despite its small size, R1-0.6B emphasizes structured reasoning, stepwise problem decomposition, and stable generation behavior, making it well-suited for research, education, and lightweight deployment scenarios.

	---

	## Model Highlights

	- Compact 0.6B parameter model optimized for efficiency
	- Strong focus on reasoning-oriented tasks
	- Stable long-form generation for its size class
	- Compatible with standard Hugging Face inference tooling

	---

	## Intended Use

	This model is intended for:
	- Research and experimentation in reasoning-focused LLMs
	- Educational use and demonstrations
	- Lightweight inference environments
	- Building blocks for agentic or tool-augmented systems

	It is not intended as a general-purpose chat replacement for larger frontier models.

	---

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "DeepBrainz/deepbrainz-r1-0.6b"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id)

	prompt = "Solve step by step: If x + 3 = 7, what is x?"
	inputs = tokenizer(prompt, return_tensors="pt")

	outputs = model.generate(
	**inputs,
	max_new_tokens=256,
	temperature=0.6,
	top_p=0.95,
	do_sample=True,
	)

	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	---

	## Training & Alignment

	R1-0.6B was trained using modern post-training techniques emphasizing reasoning quality and generation stability.
	Specific training details are intentionally abstracted in this public-facing release.

	---

	## Limitations

	Performance is constrained by model size
	Not optimized for open-ended conversational chat
	Best for short-to-medium complexity reasoning tasks

	---

	## License

	Apache 2.0

	---

	## About DeepBrainz

	DeepBrainz builds reasoning-first AI systems focused on efficiency, structure, and real-world problem-solving.

	More evaluations and updates will follow in future releases.