EphAsad/Atem-SageCoder-1.5B

TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 14, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

EphAsad/Atem-SageCoder-1.5B is a 1.5 billion parameter code reasoning model, built on the Qwen2.5-1.5B-Instruct architecture and fine-tuned from Atem-Wisdom-1.5B. It specializes in algorithmic and competitive programming tasks by explicitly reasoning through problems in a "think-then-code" approach before generating solutions. This model excels at providing auditable design decisions, edge case analysis, and complexity reasoning for coding challenges, with a maximum context length of 32768 tokens.

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Atem-SageCoder: Think-Then-Code for Algorithmic Problems

Atem-SageCoder is a 1.5 billion parameter model developed by EphAsad, specializing in code reasoning. It is a fine-tuned variant of Atem-Wisdom-1.5B, inheriting its explicit reasoning capabilities and applying them specifically to programming tasks. The model's core innovation is its "think-then-code" approach, where it first reasons through algorithm choice, edge cases, and complexity analysis within a <think> block before producing an implementation.

Key Capabilities

  • Explicit Reasoning: Generates detailed thought processes for coding problems, making its design decisions auditable.
  • Algorithmic Specialization: Fine-tuned on verified competitive programming traces from nvidia/OpenCodeReasoning, making it proficient in algorithmic and data structure challenges.
  • Problem-Solving Depth: Examines edge cases, considers multiple approaches, and explains implementation choices, leading to more robust solutions.
  • Dual Output Format: Provides a reasoning trace for complex problems and direct answers for simpler queries, adapting to problem complexity.

Good for

  • Programming problems requiring detailed reasoning before implementation.
  • Competitive programming and algorithmic tasks.
  • Situations where understanding the model's design decisions is crucial.
  • Code that benefits from edge case analysis or complexity reasoning.

Limitations

  • Primarily focused on competitive programming; less suited for general software engineering tasks.
  • Responses are longer due to reasoning traces, which may impact latency-constrained applications.
  • Strong bias towards Python, as the training data is predominantly in this language.
  • Qualitative evaluation identified some factual concept errors, requiring independent verification for critical applications.

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