ornith-ai/Ornith-1 resource

Health v2 · maintenance only

Flags: no_releases young

Adoption not part of the score

1958 stars · 191 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Ornith is a family of open-weight large language models (397B scale) designed for agentic tasks, released under MIT license with weights hosted on HuggingFace. The repo documents two generations: Ornith-1.0, which jointly optimizes task scaffolds and solution rollouts, and Ornith-1.5, which adds a full self-improvement loop that proposes tasks, generates scaffolds, and produces rollouts for reinforcement learning.

Use cases

• run an open-weight model for autonomous agent tasks
• fine-tune or extend a self-improving agentic LLM
• compare open models against proprietary frontier models on agent benchmarks
• study self-scaffolding and RL-based self-improvement for LLMs
• deploy a permissively licensed large model without regional restrictions

When to choose

• you need open, MIT-licensed model weights for agentic workloads
• you want to research self-improvement loops and scaffold optimization
• you need a large open model as an alternative to closed frontier models

When to avoid

• you need a small model that runs on consumer hardware
• you want a ready-made agent application rather than model weights
• you lack the GPU infrastructure to serve a 397B-parameter model

Facets

dataset · maturity active

llm-training agent-framework machine-learning llm-inference large-language-models reinforcement-learning machine-learning python cross-platform model-weights self-improvement agentic-tasks scaffolding open-weights mit-license huggingface ai-agents gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ornith-ai/Ornith-1")