--- title: "Ornith-1.5-35B-A3B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c98822f4-2255-450a-89ce-df15fc0003a1" updated: "2026-11-12" --- # Ornith-1.5-35B-A3B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a 35B mixture-of-experts text model tuned for coding and agent work, using only about 3B parameters per token. This repository holds its GGUF files for local use. Ornith-1.5-35B-A3B-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. 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-1.5-35B-A3B is a text generation model from ornith-ai, and this repository holds it in GGUF format. It is a mixture-of-experts model, so only about 3B of its 35.5B parameters work on each token. It extends Ornith-1.0, which was built on Qwen3.5 and Gemma4. Version 1.5 adds a training loop where the model generates its own tasks, builds scaffolds and learns from solution attempts through reinforcement learning. ### Who it suits Developers who want a coding and agent model they can keep on their own machine, and people who test local models against coding benchmarks. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers and tool builders | Run it locally as a coding assistant | Ask for a fix to a failing script | | DevOps | Teams that automate terminal work | Try it as the model behind a terminal agent | Plan the steps of a command line task | | Research | People comparing open models | Score it on coding and agent tests | Compare it with other models of similar size | | Hobby | Local model enthusiasts | Load the GGUF file in a local runner | Chat about code without a hosted service | Skip it if your computer has little free disk space, since the 4-bit weights come to about 17.8 GB by Hugging Face estimate. ### Where to use it - Windows, macOS or Linux with Python, to download the files - A local runner that reads GGUF files - Terminal agent setups, which the card used for its coding benchmarks | Use case | Who | What they get back | | --- | --- | --- | | Coding help | Developers | Code and explanations from a model that activates few parameters per token | | Agentic coding | Agent builders | A model the authors scored on terminal and agent tasks | | Model comparison | Researchers | A GGUF copy to test against other open models | ### How to set it up - Python and pip to run the download commands - About 17.8 GB of free disk space for the 4-bit weights - A local runner that can open GGUF files 1. Install the Hugging Face tools. 2. Download the files from this repository. 3. Open the GGUF file in your local runner. The model card page has the run notes. ```bash pip install -U huggingface_hub hf download ornith-ai/Ornith-1.5-35B-A3B-GGUF ``` ### How to use it Load the downloaded file in your runner, then chat with it or point a coding agent at it. The source shows no run command, so follow your runner for the exact steps. ### Example You ask it for a fix to a failing command line script and paste the error. You can expect a text reply with an explanation and code, since the authors tuned it for coding and agent tasks. ### For newcomers and experts If you are new to local models, think of the GGUF file as the model packed to run on your own computer. Download it, load it in a runner, and start with a short coding question. If you know the field, this is a sparse mixture-of-experts model with 35.5B parameters and about 3B active per token. The authors say Ornith-1.5 jointly optimizes task generation, scaffold construction and solution rollouts, instead of using fixed human-made tasks and hand-built setups. Download the GGUF file → Load it in a local runner → Send a coding or agent prompt → Read the generated reply ### Good to know - The authors report beating Qwen 3.6-35B on their coding and agentic benchmarks, and these numbers come from them - Other people have published 3 quantized versions and no fine-tuned versions, according to Hugging Face Terminal-Bench 2.1 score with the Terminus-2 setup | Label | Value | | --- | --- | | Ornith-1.5-35B-A3B | 67.8 points | | Ornith-1.0-35B-A3B | 64.2 points | | Qwen3.5-397B | 53.5 points | | Qwen3.6-35B-A3B | 52.5 points | | Muse-Glimmer-30B | 51.7 points | | Gemma-4-31B | 42.1 points | - License: MIT - Cost to run: none named - Data it reaches: the files you download and the prompts you give it, with no other access named - Not for: the source states no restrictions ## README ````markdown # Ornith-1.5-35B-A3B-GGUF Runs a 35B mixture-of-experts text model tuned for coding and agent work, using only about 3B parameters per token. This repository holds its GGUF files for local use. 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-1.5-35B-A3B is a text generation model from ornith-ai, and this repository holds it in GGUF format. It is a mixture-of-experts model, so only about 3B of its 35.5B parameters work on each token. It extends Ornith-1.0, which was built on Qwen3.5 and Gemma4. Version 1.5 adds a training loop where the model generates its own tasks, builds scaffolds and learns from solution attempts through reinforcement learning. Who it suits: Developers who want a coding and agent model they can keep on their own machine, and people who test local models against coding benchmarks. ```table Industry | Who uses it | How they use it | Example Software | Developers and tool builders | Run it locally as a coding assistant | Ask for a fix to a failing script DevOps | Teams that automate terminal work | Try it as the model behind a terminal agent | Plan the steps of a command line task Research | People comparing open models | Score it on coding and agent tests | Compare it with other models of similar size Hobby | Local model enthusiasts | Load the GGUF file in a local runner | Chat about code without a hosted service ``` Skip it if your computer has little free disk space, since the 4-bit weights come to about 17.8 GB by Hugging Face estimate. Where to use it: - Windows, macOS or Linux with Python, to download the files - A local runner that reads GGUF files - Terminal agent setups, which the card used for its coding benchmarks ```table Use case | Who | What they get back Coding help | Developers | Code and explanations from a model that activates few parameters per token Agentic coding | Agent builders | A model the authors scored on terminal and agent tasks Model comparison | Researchers | A GGUF copy to test against other open models ``` How to set it up: - Python and pip to run the download commands - About 17.8 GB of free disk space for the 4-bit weights - A local runner that can open GGUF files 1. Install the Hugging Face tools. 2. Download the files from this repository. 3. Open the GGUF file in your local runner. The model card page has the run notes. ```bash pip install -U huggingface_hub hf download ornith-ai/Ornith-1.5-35B-A3B-GGUF ``` How to use it: Load the downloaded file in your runner, then chat with it or point a coding agent at it. The source shows no run command, so follow your runner for the exact steps. Example: You ask it for a fix to a failing command line script and paste the error. You can expect a text reply with an explanation and code, since the authors tuned it for coding and agent tasks. For newcomers and experts: If you are new to local models, think of the GGUF file as the model packed to run on your own computer. Download it, load it in a runner, and start with a short coding question. If you know the field, this is a sparse mixture-of-experts model with 35.5B parameters and about 3B active per token. The authors say Ornith-1.5 jointly optimizes task generation, scaffold construction and solution rollouts, instead of using fixed human-made tasks and hand-built setups. ```flow Download the GGUF file Load it in a local runner Send a coding or agent prompt Read the generated reply ``` Good to know: - The authors report beating Qwen 3.6-35B on their coding and agentic benchmarks, and these numbers come from them - Other people have published 3 quantized versions and no fine-tuned versions, according to Hugging Face ```chart Title: Terminal-Bench 2.1 score with the Terminus-2 setup Ornith-1.5-35B-A3B: 67.8 points Ornith-1.0-35B-A3B: 64.2 points Qwen3.5-397B: 53.5 points Qwen3.6-35B-A3B: 52.5 points Muse-Glimmer-30B: 51.7 points Gemma-4-31B: 42.1 points ``` - License: MIT - Cost to run: none named - Data it reaches: the files you download and the prompts you give it, with no other access named - Not for: the source states no restrictions ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 35.5B - Weights: about 17.8 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 4,164,876 - Likes: 603 - License: mit - Quantized versions: 3 - Tags: transformers, gguf, text-generation, conversational ## Links - Model: https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF - Files: https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-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-1.5-35B-A3B-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-1.5-35B-A3B-GGUF on Hugging Face](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-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-1.5-35B-A3B-GGUF (this page) | 4,164,876 | 603 | 35.5B | mit | 2026-08-24 | | [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-1.0-9B-GGUF](https://mindket.com/p/80667786-2895-41cb-b1d8-f6f6ae12215d) | 2,357,551 | 702 | 9B | mit | 2026-06-25 | | [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 · デイリーHuggingFaceトレンド · 2026-08-26](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mty4zin4xu2s): 今日のHuggingFaceトレンド ornith-ai/Ornith-1.5-35B-A3B-GGUF エンドツーエンドの自己改善による基盤モデルの構築を目的としています。 タスク生成、スキャフォールド構築、ソリューションロールアウトの共同最適化と強化学習により、継続的に性能を向上させる手法を導入しています。 特にコーディングやエージェント能力に優れたMoEモデルであるOrnith-1.5-35B-A3Bを提供しています。 - [Bluesky · 積猫🗡️🐈 · 2026-08-20](https://bsky.app/profile/tsumineko.at.mitsuse.jp/post/3mtimpjukxs26): ornith-ai/Ornith-1.5-35B-A3B-GGUF huggingface.co/ornith-ai/Or... ## 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: 2026-08-24 - First published: 2026-08-18 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ornith-ai/Ornith-1.5-35B-A3B-GGUF on Hugging Face](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c98822f4-2255-450a-89ce-df15fc0003a1) - [Its page on Mindket](https://mindket.com/p/c98822f4-2255-450a-89ce-df15fc0003a1)