--- title: "Ornith-1.0-9B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/80667786-2895-41cb-b1d8-f6f6ae12215d" updated: "2026-11-13" --- # Ornith-1.0-9B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF copy of a 9B open-source coding-agent model, trained with reinforcement learning to write solutions and the scaffolding around them. Built for single-GPU use by developers running agents locally. Ornith-1.0-9B-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ornith-ai ## About ### What it is Ornith-1.0-9B is the smallest model in the Ornith family, a set of open-source coding-agent models that the card calls self-improving. This repository holds its GGUF copy, which the card says is designed for efficient use on a single GPU. The family's base models are Gemma 4 and Qwen 3.5, which the authors trained further. ### Who it suits It suits developers who want a coding agent model on their own machine and researchers who follow how reinforcement learning is applied to agents. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a local coding agent on one GPU | Ask it to fix a failing test in a repository | | Developer tools | Tool builders | Connect the model to an agent setup of their own | Test it in a terminal-based coding agent | | Research | ML researchers | Study training that improves both the scaffold and the solution | Compare it with the Qwen3.5-9B scores in the card | Anyone who needs a chat model for writing or general questions can skip it, since the card positions it for agentic coding. ### Where to use it You run it on your own computer, behind an agent setup that calls the model. - Windows, macOS or Linux, with Python, for downloading the files - A single GPU, the deployment the card designs it for - Terminus-2 and Claude Code, the two agent setups named in the card's benchmark table | Use case | Who | What they get back | | --- | --- | --- | | Fix a bug in a repository | Developers | Edits and commands proposed across several steps | | Run terminal tasks | Agent builders | A model to test on terminal-based task suites | | Study agent training | Researchers | A small checkpoint trained with reinforcement learning | ### How to set it up You need a way to fetch the files, a GPU and a runtime that opens GGUF files. - Python, for the Hugging Face hub tools - A single GPU - Free disk space, about 4.5 GB for a 4-bit copy by estimate - A local runtime that reads GGUF files 1. Install the Hugging Face hub tools. 2. Download the repository files. 3. Open the file in your GGUF runtime and connect it to your coding agent setup. ```sh pip install -U huggingface_hub hf download ornith-ai/Ornith-1.0-9B-GGUF ``` ### How to use it The part of the card shown here gives no run command, so use is a matter of loading the file in your runtime and pointing your agent at it. Give the agent a coding task and let it work through the steps. ### Example You hand your agent the task, "Make the failing test in this repository pass." The model reads files, runs commands and proposes edits over several turns, and your agent applies them. ### For newcomers and experts A coding agent does more than write code. It runs commands and edits files step by step through an app, and this model is built to drive one. A good first try is to load the file and ask it a short coding question before wiring it into an agent. For someone who knows the field, the card says the training uses reinforcement learning on two things at once: the solution attempts and the scaffold that drives them. Optimizing both together is meant to find better search paths. The card reports results on four coding benchmarks, among them SWE-Bench and OpenClaw, and those figures are the authors' own. The model proposes a scaffold → The scaffold drives solution attempts → Reinforcement learning scores the pair → Training favors better search paths ### Good to know The card's table gives 69.4 on SWE-bench Verified for this model against 53.2 for Qwen3.5-9B. The chart shows the Terminal-Bench 2.1 scores with the Terminus-2 setup, as the card reports them. Terminal-Bench 2.1 score with Terminus-2 | Label | Value | | --- | --- | | Ornith-1.0-9B | 43.1 points | | Qwen3.5-9B | 21.3 points | | Qwen3.5-35B | 41.4 points | | Gemma4-12B | 21 points | | Gemma4-31B | 42.1 points | - License: MIT, which the card describes as open worldwide with no regional limits - Cost to run: none named, but you need a GPU - Data it reaches: it downloads files from Hugging Face. What the model touches while coding depends on the agent setup you connect. ## README ````markdown # Ornith-1.0-9B-GGUF GGUF copy of a 9B open-source coding-agent model, trained with reinforcement learning to write solutions and the scaffolding around them. Built for single-GPU use by developers running agents locally. Made by ornith-ai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Ornith-1.0-9B is the smallest model in the Ornith family, a set of open-source coding-agent models that the card calls self-improving. This repository holds its GGUF copy, which the card says is designed for efficient use on a single GPU. The family's base models are Gemma 4 and Qwen 3.5, which the authors trained further. Who it suits: It suits developers who want a coding agent model on their own machine and researchers who follow how reinforcement learning is applied to agents. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a local coding agent on one GPU | Ask it to fix a failing test in a repository Developer tools | Tool builders | Connect the model to an agent setup of their own | Test it in a terminal-based coding agent Research | ML researchers | Study training that improves both the scaffold and the solution | Compare it with the Qwen3.5-9B scores in the card ``` Anyone who needs a chat model for writing or general questions can skip it, since the card positions it for agentic coding. Where to use it: You run it on your own computer, behind an agent setup that calls the model. - Windows, macOS or Linux, with Python, for downloading the files - A single GPU, the deployment the card designs it for - Terminus-2 and Claude Code, the two agent setups named in the card's benchmark table ```table Use case | Who | What they get back Fix a bug in a repository | Developers | Edits and commands proposed across several steps Run terminal tasks | Agent builders | A model to test on terminal-based task suites Study agent training | Researchers | A small checkpoint trained with reinforcement learning ``` How to set it up: You need a way to fetch the files, a GPU and a runtime that opens GGUF files. - Python, for the Hugging Face hub tools - A single GPU - Free disk space, about 4.5 GB for a 4-bit copy by estimate - A local runtime that reads GGUF files 1. Install the Hugging Face hub tools. 2. Download the repository files. 3. Open the file in your GGUF runtime and connect it to your coding agent setup. ```sh pip install -U huggingface_hub hf download ornith-ai/Ornith-1.0-9B-GGUF ``` How to use it: The part of the card shown here gives no run command, so use is a matter of loading the file in your runtime and pointing your agent at it. Give the agent a coding task and let it work through the steps. Example: You hand your agent the task, "Make the failing test in this repository pass." The model reads files, runs commands and proposes edits over several turns, and your agent applies them. For newcomers and experts: A coding agent does more than write code. It runs commands and edits files step by step through an app, and this model is built to drive one. A good first try is to load the file and ask it a short coding question before wiring it into an agent. For someone who knows the field, the card says the training uses reinforcement learning on two things at once: the solution attempts and the scaffold that drives them. Optimizing both together is meant to find better search paths. The card reports results on four coding benchmarks, among them SWE-Bench and OpenClaw, and those figures are the authors' own. ```flow The model proposes a scaffold The scaffold drives solution attempts Reinforcement learning scores the pair Training favors better search paths ``` Good to know: The card's table gives 69.4 on SWE-bench Verified for this model against 53.2 for Qwen3.5-9B. The chart shows the Terminal-Bench 2.1 scores with the Terminus-2 setup, as the card reports them. ```chart Title: Terminal-Bench 2.1 score with Terminus-2 Ornith-1.0-9B: 43.1 points Qwen3.5-9B: 21.3 points Qwen3.5-35B: 41.4 points Gemma4-12B: 21 points Gemma4-31B: 42.1 points ``` - License: MIT, which the card describes as open worldwide with no regional limits - Cost to run: none named, but you need a GPU - Data it reaches: it downloads files from Hugging Face. What the model touches while coding depends on the agent setup you connect. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 9B - Weights: about 4.5 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 2,357,551 - Likes: 702 - License: mit - Quantized versions: 2 - Tags: transformers, gguf, text-generation, conversational ## Links - Model: https://huggingface.co/ornith-ai/Ornith-1.0-9B-GGUF - Files: https://huggingface.co/ornith-ai/Ornith-1.0-9B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ornith-ai/Ornith-1.0-9B-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [ornith-ai/Ornith-1.0-9B-GGUF on Hugging Face](https://huggingface.co/ornith-ai/Ornith-1.0-9B-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Ornith-1.0-9B-GGUF (this page) | 2,357,551 | 702 | 9B | mit | 2026-06-25 | | [Ornith-1.5-9B-GGUF](https://mindket.com/p/5dd265f6-9a3e-450b-ad2a-b49a78e2700a) | 5,772,425 | 673 | 9.2B | mit | 2026-08-24 | | [Ornith-1.5-35B-A3B-GGUF](https://mindket.com/p/c98822f4-2255-450a-89ce-df15fc0003a1) | 4,164,876 | 603 | 35.5B | mit | 2026-08-24 | | [Qwopus3.8-27B-Flash-V2-GGUF](https://mindket.com/p/f4542d06-f1ba-46a9-8148-58a34e8911b6) | 897,675 | 75 | 461M | apache-2.0 | 2026-09-21 | | [Spark-X2.6-4B-GGUF](https://mindket.com/p/31f3aff1-a9fb-4808-ad4b-6907e965fb45) | 687,668 | 310 | 4.4B | apache-2.0 | 2026-11-06 | | [gpt-oss-20b-GGUF](https://mindket.com/p/99b8d5c9-4750-4943-807b-082810629e1f) | 614,134 | 909 | 20.9B | apache-2.0 | 2025-12-19 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · loach · 2026-07-30](https://bsky.app/profile/loach2009.bsky.social/post/3mrurpqyrps2u): deepreinforce-ai/Ornith-1.0-9B-GGUF を llama-server で立てて、Tailscale 経由で外部から安全にアクセスできるように構成中。 会社のpcで claude code router 経由で Implementer / Explorer エージェントを作ってルーティング。実装と探索をローカルLLMに投げれば、大幅にトークン節約できて一日中快適に回りそう！ - [Bluesky · デイリーHuggingFaceトレンド · 2026-07-02](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mpnyrbhr6i2p): 今日のHuggingFaceトレンド deepreinforce-ai/Ornith-1.0-9B-GGUF エージェントによるコーディングを目的とした自己改善型のオープンソースモデル、Ornith-1.0-9Bを提供しています。 単一GPUで動作する軽量な設計であり、推論能力とツール呼び出し機能を備えています。 コーディング関連のベンチマークで高い性能を達成しています。 - [Bluesky · @aichina.news · 2026-10-19](https://bsky.app/profile/aichina.news/post/3myarcbzpyh2u): A 9B model in GGUF at 4-bit fits in 5–6 GB, so it'll run on a laptop — that's the whole pitch. No benchmarks, no provenance, licence just says "other". Hard to see this displacing Qwen2.5-7B for anyone past the first afternoon unless the base model turns out to be something. - [Bluesky · デイリーHuggingFaceトレンド · 2026-07-04](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mpsxspa5do2p): 今日のHuggingFaceトレンド deepreinforce-ai/Ornith-1.0-9B-GGUF エージェント的なコーディング向けの自己改善型オープンソースモデルOrnith-1.0-9Bを提供します。 単一GPUで効率的に動作する軽量なモデルです。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-06-30](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mpixqrfmln2a): 今日のHuggingFaceトレンド deepreinforce-ai/Ornith-1.0-9B-GGUF エージェント的なコーディング向けに設計された自己改善型のオープンソースモデルOrnith-1.0-9Bを提供するためのリポジトリです。 単一GPUで効率的に動作する軽量なモデルであり、推論能力とツール呼び出し機能を備えているのが特徴です。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-06-28](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mpdvnbiqib26): 今日のHuggingFaceトレンド deepreinforce-ai/Ornith-1.0-9B-GGUF エージェントによるコーディングを目的とした、自己改善型のオープンソースモデルOrnith-1.0-9Bを提供するためのリポジトリです。 シングルGPUで効率的に動作する軽量な設計であり、思考プロセスを含む推論能力を備えたコーディング特化型モデルとなっています。 - [Bluesky · probbrain.com · 2026-06-26](https://bsky.app/profile/probbrain.bsky.social/post/3mp7s5kawqm2g): deepreinforce-ai/Ornith-1.0-9B-GGUF · 142♥ · 1779 dl ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-06-25 - First published: 2026-06-25 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ornith-ai/Ornith-1.0-9B-GGUF on Hugging Face](https://huggingface.co/ornith-ai/Ornith-1.0-9B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/80667786-2895-41cb-b1d8-f6f6ae12215d) - [Its page on Mindket](https://mindket.com/p/80667786-2895-41cb-b1d8-f6f6ae12215d)