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

python

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