DeepBrainz-R1-4B-40K

DeepBrainz-R1-4B-40K is a compact, high-performance reasoning model engineered by DeepBrainz AI & Labs. It is part of the DeepBrainz-R1 Series, designed to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.

This specific variant offers a 40,960 token context window, making it suitable for maximum context version designed for repository-level code reasoning..

🚀 Model Highlights

Parameter Count: ~4B
Context Window: 40,960 tokens
Context Type: Extended (RoPE)
Specialization: STEM Reasoning, Logic, Code Analysis
Architecture: Optimized Dense Transformer
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-R1-4B-40K"

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))

🏗️ Technical Summary

The model was produced using a multi-stage optimization process involving large-scale supervision and iterative refinement. It is designed to maximize reasoning quality while maintaining instruction robustness.

Specific training methodologies and dataset compositions are proprietary.

🛡️ 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

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