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Die Ornith-1.0-Kollektion auf Hugging Face bietet spezialisierte Large Language Models (LLMs), die auf den Bereich des 'Agentic Coding' fokussiert sind. Diese Modelle sollen Entwickler bei komplexen Programmieraufgaben unterstützen, indem sie nicht nur Code generieren, sondern auch logische Schlussfolgerungen für die Problemlösung anwenden.
Die Plattform Hugging Face dient hier als Hosting-Umgebung für die Modellgewichte und Dokumentationen. Die Sammlung wird aktiv gepflegt und regelmäßig aktualisiert, um den Anforderungen an moderne KI-gestützte Entwicklungsumgebungen gerecht zu werden.
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Link:deep-reinforce.com
Ornith-1.0 is an open-source model family tailored for agentic coding, spanning parameter sizes from 9B to 397B. Built on Gemma 4 and Qwen 3.5, the architecture trains models to jointly generate both code solutions and the task-specific scaffolds directing them.
Repository:ornith-ai/Ornith-1
The Ornith-1 repository provides an open-source model family designed for agentic tasks using autonomous self-improvement. Spanning Ornith-1.0 and Ornith-1.5, the project implements self-scaffolding and automated generation of training rollouts for reinforcement learning.
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Link:ornith-ai
ornith-ai has released Ornith-1.5-397B, an open-source multimodal mixture-of-experts model under the MIT licence. With 397 billion parameters and an end-to-end self-improvement loop, it delivers coding and reasoning benchmark performance comparable to top proprietary models.
Article:Luis Chavez-Mattos
Deep Reinforce has introduced Ornith 1.5 35B-A3B, a mixture-of-experts model activating roughly 3 billion parameters per token out of 35 billion. According to published benchmarks, it surpasses direct competitors like Qwen3.6-35B across coding and agentic tasks, while closing the gap on much larger models such as Qwen3.5-397B.
Link:Anomaly
OpenCode is an open-source AI coding agent running across terminals, IDEs, and desktop environments, offering integration with over 75 model providers.
Link:Poolside
Poolside has released Laguna-S-2.1, a Mixture-of-Experts language model featuring 118B total parameters with approximately 8B activated parameters per token. Targeted at software engineering and long-horizon agentic workflows, the model is available under the permissive OpenMDW-1.1 license.
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