JibayAi/Jibay_2

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 14, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

JibayAi/Jibay_2 is a 2 billion parameter, open-source language model built upon the Qwen 3 family, featuring a 32,768 token context length. Designed to be simple, trainable, and customizable, it serves as an efficient alternative for developers needing a controllable model without excessive computational overhead. This model excels at fine-tuning for domain-specific data, on-device AI applications, and rapid prototyping, offering strong performance in math reasoning and Python code generation. Its non-MoE architecture simplifies understanding and adaptation for various specialized tasks and languages.

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Jibay 2: A Customizable and Efficient Language Model

Jibay 2, developed by JibayAi, is a lightweight, open-source language model based on the Qwen 3 architecture. Released in Khordad 1405 (May/June 2026), it features 2 billion active parameters and a substantial 32,768 token context length.

Key Capabilities & Features

  • Simple and Trainable: Designed for ease of understanding, debugging, and fine-tuning due to its non-MoE (Mixture of Experts) architecture, where all parameters are active.
  • Efficient Performance: Offers a practical alternative for developers seeking a controllable model without high computational demands.
  • Strong Benchmarks: Achieves notable scores including ~70.0% on GSM8K for math reasoning and ~68.0% on HumanEval for Python code generation.
  • Customization Focus: Can be entirely retrained, extended, or adapted to various fields and languages, including medical/legal text processing, niche code generation, and low-resource language conversational AI.
  • Optimized Usage: Recommends GGUF quantized versions for best performance, especially on consumer hardware.

Good For

  • Fine-tuning on domain-specific datasets.
  • On-device or edge AI applications.
  • Research and educational purposes.
  • Lightweight agentic workflows with function calling capabilities.
  • Rapid prototyping and experimentation across diverse languages and tasks.