kedarcv/Clair-3B

TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 29, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

Clair-3B is a 3-billion parameter, Transformer-based language model developed by Michael Mlungisi Nkomo, optimized for advanced conversational AI, coding assistance, and complex reasoning tasks. With a 4,096-token context window, it features significantly enhanced knowledge in coding and mathematics, strong instruction following, and robust structured data understanding and generation. This model offers multilingual support for over 29 languages and embeds personality directly into its weights for consistent identity.

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Clair-3B: A Specialized 3B Parameter LLM

Clair-3B is a 3-billion parameter, Transformer-based language model developed by Michael Mlungisi Nkomo, designed for advanced conversational AI, coding, and complex reasoning. It stands out due to its significantly enhanced knowledge in coding and mathematics, achieved through specialized training. The model supports a 4,096-token context window and offers multilingual capabilities across 29+ languages.

Key Capabilities

  • Advanced Conversational AI: Engages in natural, contextually aware dialogue with personality embedded directly into its weights, improving identity consistency and resilience to diverse system prompts.
  • Coding & Mathematics: Excels in code generation, explanation, debugging, optimization, and complex mathematical problem-solving with step-by-step explanations.
  • Instruction Following & Structured Output: Demonstrates strong adherence to complex instructions and is proficient in understanding and generating structured data, particularly JSON.
  • Long-Text Generation: Capable of producing coherent, high-quality responses over extended contexts.

Good For

  • Developers seeking a compact yet powerful model for code assistance and mathematical reasoning.
  • Applications requiring robust instruction following and structured data generation (e.g., JSON).
  • Building conversational agents that maintain a consistent personality without relying heavily on system prompts.
  • Use cases demanding multilingual support across a broad range of languages.

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