DeepBrainz-R1-2B-16K

DeepBrainz-R1-2B-16K is a compact, long-context reasoning model in the DeepBrainz-R series, designed for structured problem-solving, analysis, and enterprise research workflows.

The model emphasizes reasoning quality, instruction robustness, and stable behavior over long contexts, while remaining highly cost-efficient to deploy.

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Model Highlights

• 1.7B parameters
• 16K context length
• Optimized for reasoning-centric math and coding tasks
• Designed for modern GPU inference runtimes
• Architecture: Qwen3-compatible (DeepBrainz-R series post-trained and optimized for reasoning-centric workloads)

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Intended Use

• Advanced reasoning systems
• Math and Coding
• Research and evaluation
• Agentic workflows
• Inference-time scaling and test-time compute experiments

Not intended as a general-purpose chat replacement for large frontier models.

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DeepBrainz/DeepBrainz-R1-2B-16K"

tok = AutoTokenizer.from_pretrained(model_id)
mdl = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Solve step by step: If x + 5 = 12, what is x?"
inputs = tok(prompt, return_tensors="pt")

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

print(tok.decode(out[0], skip_special_tokens=True))

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Training Summary

The model was produced using a multi-stage optimization process involving large-scale on-policy optimization and iterative refinement to improve reasoning quality and robustness. Specific training details are intentionally abstracted in this public release.

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Limitations

Performance depends on task complexity and inference configuration. Larger models may outperform R1-2B-16K on extremely complex tasks.

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License

Apache 2.0

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About DeepBrainz

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