Ornith‑1.0‑35B: Open‑Source Agentic Coding Model Takes Flight
deepreinforce-ai/Ornith-1.0-35B-GGUF ↗
Ornith‑1.0‑35B is a 35 billion‑parameter, MIT‑licensed model released by the DeepReinforce AI team as part of the Ornith‑1.0 family. Distributed in the GGUF format, it is optimized for single‑GPU (8×80 GB) deployment and targets the text‑generation pipeline with a strong focus on agentic coding and reasoning.
The model is trained with a novel self‑improving reinforcement‑learning approach that generates scaffolds for better reasoning. By default it emits a `<think> … </think>` block that contains chain‑of‑thought traces before the final answer, and it can emit well‑formed `<tool_call>` blocks that are parsed into OpenAI‑style `tool_calls`. Serving requires recent runtimes: Transformers ≥ 5.8.1, vLLM ≥ 0.19.1, or SGLang ≥ 0.5.9. Benchmarks show state‑of‑the‑art performance on coding‑centric suites such as Terminal‑Bench 2.1 (64.5 % score), SWE‑Bench Verified (75 %+), NL2Repo (high scores with 400 K context), and ClawEval, often matching or surpassing proprietary models.
The README provides quick‑start scripts for vLLM, SGLang, and direct Transformers loading, as well as examples of OpenAI‑compatible chat completions, tool‑calling, and integration with agent frameworks like OpenHands, Hermes, and OpenClaw. Its reasoning parser and tool‑call parser make it a drop‑in replacement for any OpenAI‑compatible endpoint, enabling developers to build sophisticated code‑generation agents, automated debugging assistants, and multi‑step programming workflows without proprietary APIs.
Project Ideas
- Build a VS Code extension that leverages Ornith‑1.0‑35B to generate, test, and refactor code snippets directly within the editor.
- Create an autonomous terminal assistant that can run shell commands, list files, and write scripts using the model’s built‑in tool‑calling capability.
- Develop a code‑review bot that reads pull‑request diffs, executes static analysis tools, and suggests fixes through multi‑step reasoning traces.
- Implement an AI pair‑programmer web service that streams both the chain‑of‑thought reasoning and the final code, enabling transparent collaborative coding.
- Design a documentation generator that writes example code, executes it in a sandbox, and incorporates the execution output into the final documentation using Ornith’s reasoning and tool‑call features.