Instructions to use DeepBrainz/DeepBrainz-R1-0.6B-8K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepBrainz/DeepBrainz-R1-0.6B-8K with Transformers:

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

pipe = pipeline("text-generation", model="DeepBrainz/DeepBrainz-R1-0.6B-8K")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepBrainz/DeepBrainz-R1-0.6B-8K")
model = AutoModelForCausalLM.from_pretrained("DeepBrainz/DeepBrainz-R1-0.6B-8K")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DeepBrainz/DeepBrainz-R1-0.6B-8K 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-8K"
# 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-8K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeepBrainz/DeepBrainz-R1-0.6B-8K

SGLang

How to use DeepBrainz/DeepBrainz-R1-0.6B-8K 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-8K" \
    --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-8K",
		"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-8K" \
        --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-8K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeepBrainz/DeepBrainz-R1-0.6B-8K with Docker Model Runner:
```
docker model run hf.co/DeepBrainz/DeepBrainz-R1-0.6B-8K
```

DeepBrainz-R1-0.6B-8K / README.md

ArunkumarVR

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metadata

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-8K

DeepBrainz-R1-0.6B-8K is a compact, high-performance 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: 8,192 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

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DeepBrainz/DeepBrainz-R1-0.6B-8K"

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.

DeepBrainz AI & Labs
Advancing General Intelligence through Scalable Reasoning