CodeDevX/auralis-coder

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 11, 2026License:mitArchitecture:Transformer Open Weights Featherless Exclusive Cold

CodeDevX/auralis-coder is a 1.5 billion parameter causal language model developed by CodeDevX, specifically designed for programming and artificial intelligence tasks. Based on a Qwen tokenizer, it excels at code generation, understanding, and debugging assistance across various programming languages and technical subjects. With a maximum context length of 131,072 tokens, it is optimized for technical content rather than general conversation.

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Auralis-Coder: Specialized Programming and AI Language Model

Auralis-Coder is a 1.5 billion parameter causal language model from CodeDevX, meticulously designed for programming and artificial intelligence applications. Unlike general-purpose conversational models, its core focus is on generating and understanding technical content. The model leverages a Qwen2.5 tokenizer and is trained on a large corpus of programming, educational, and high-quality text, targeting 15 billion tokens.

Key Capabilities

  • Code Generation & Understanding: Proficient in generating, explaining, and understanding code across languages like Python, C/C++, Java, JavaScript, Rust, Go, and SQL.
  • Technical Explanations: Provides assistance with software development concepts, algorithms, data structures, computer science, machine learning, deep learning, and natural language processing.
  • Debugging Assistance: Aids in identifying and resolving issues in code.
  • Specialized Domain: Explicitly designed for programming and AI, it will indicate out-of-domain queries rather than attempting to answer non-technical subjects.
  • High Context Length: Supports a maximum context length of 131,072 tokens, allowing for processing extensive codebases or technical documents.

Good For

  • Developers seeking assistance with code generation, explanation, and debugging.
  • Researchers and students in computer science, AI, and machine learning needing help with technical concepts.
  • Applications requiring a specialized model for programming-related tasks and technical instruction following.

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