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 base reasoning model. For conversational chat, we recommend using a specific instruct template or fine-tuning on your domain data.
💻 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.
Advancing General Intelligence through Scalable Reasoning
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