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Ornith-1.5-35B-A3B

LLMsAI Models & LLMsOpen source · Web

Ornith-1.5-35B-A3B is a mid-size mixture-of-experts model designed for advanced coding and reasoning tasks. It outperforms similar models in benchmarks, offering efficient parameter activation and self-improvement capabilities.

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Ornith-1.5-35B-A3B is a sophisticated large language model that belongs to the Ornith-1.5 family. It is a mid-size mixture-of-experts model that activates approximately 3 billion parameters per token, providing a balance between performance and computational efficiency. This model is particularly notable for its ability to outperform its peers, such as Qwen 3.6-35B and dense models like Gemma 4-31B, across a variety of coding and agentic benchmarks.

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The model extends the capabilities of its predecessor, Ornith-1.0, by expanding the self-improvement loop. This involves optimizing task generation, scaffold construction, and solution rollouts, allowing the model to continuously generate new training tasks and discover effective strategies for solving them. Reinforcement learning is employed to improve the policy, making Ornith-1.5-35B-A3B a dynamic and evolving tool.

Ornith-1.5-35B-A3B excels in various benchmarks, including Terminal-Bench 2.1, SWE-bench, and GPQA Diamond, showcasing its superior performance in coding and reasoning tasks. The model is designed to handle context windows of up to 262,144 tokens, with the capability to extend this limit using RoPE scaling, making it suitable for long-context applications.

The model is open-source and can be served using recent runtimes such as Transformers, vLLM, and SGLang. It supports OpenAI-compatible endpoints, allowing seamless integration with existing AI frameworks and tools. This makes Ornith-1.5-35B-A3B a versatile choice for developers and researchers looking to leverage advanced AI capabilities in their projects.

  • CategoryAI Models & LLMs
  • Pricing ModelOpen source
  • DeploymentCloud
  • PlatformsWeb

  • Mixture-of-experts model

  • 3B activated parameters per token

  • Self-improvement loop

  • Reinforcement learning

  • Open-source

  • Supports long context windows

  • OpenAI-compatible endpoints

  • Superior benchmark performance

  1. Access model page: Visit the Hugging Face model card

  2. Authenticate: Ensure you have the necessary credentials

  3. Configure: Set up the model with recommended parameters

  4. Send prompts: Use the model for coding and reasoning tasks

  • Advanced Coding
  • Reasoning Tasks
  • Agentic Coding
  • Long-Context Applications
  • AI Research

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