--- title: "Ornith-1.5-9B-GGUF by AtomicChat" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e8c0edff-085e-4d2a-98f1-7bfdabe54557" updated: "2026-11-13" --- # Ornith-1.5-9B-GGUF by AtomicChat > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF builds of Ornith 1.5 9B, a model that reads images and text, in sizes from 2.81 GB to 17.9 GB plus a vision projector. Pick the file that fits your graphics memory and run it locally. Ornith-1.5-9B-GGUF by AtomicChat 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 AtomicChat ## About ### What it is AtomicChat converted Ornith-1.5-9B, a 9B model that takes images and text, into GGUF files for local runners. The files range from 2.81 GB to 17.9 GB. The AD files use a bit layout for each tensor, tuned for this model and built with the team's own importance matrix. A separate vision projector file adds image input. The Atomic Chat app offers toggles for thinking. ### Who it suits It suits people who want to run a 9B image and text model on their own graphics card and need a file that matches their memory. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | People running models offline | Choose the file that fits their graphics memory | Take the 5.93 GB file on an 8 GB card | | Vision tasks | Users who ask about pictures | Load the vision projector beside the language file | Ask the model about a screenshot | | Research | Quantization researchers | Compare AD files with stock presets that share one matrix | Read the KL divergence and top-1 columns | You can skip it if you have less than 4 GB of video memory, since the card's smallest suggestion is for 4 GB. ### Where to use it - llama.cpp, the runner the card measured with - Atomic Chat, the app from the same team - Windows, macOS and Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Local assistant | Developers and hobbyists | A 9B model that answers in text on their own machine | | Picture questions | Users with the projector file | Answers that take an image into account | | File choice | People with limited memory | A file matched to 4, 6, 8, 12, 16 or 24 GB | ### How to set it up You need these first: - Python, to use Hugging Face's download client - Graphics memory that fits the file you pick - llama.cpp or the Atomic Chat app 1. Install or update the Hugging Face download client. 2. Download the repository's files to your computer. 3. Pick a file from the card's memory table. 4. Load it in your runner, and add the vision projector if you want images. ```sh pip install -U huggingface_hub hf download AtomicChat/Ornith-1.5-9B-GGUF ``` ### How to use it You load one language file in llama.cpp or Atomic Chat and talk to the model. Leave out the projector for text only. The text available here cuts off before the run steps, so follow the model card and its guide for the exact options. | Tool | What it does | | --- | --- | | BF16 file | The reference build at 17.9 GB, for 24 GB and up | | Q8\_0 file | A 9.53 GB build for 16 GB | | AD-Q5\_K-Q4\_K file | A 5.93 GB build for 8 GB, which the card calls the sweet spot | | AD-Q4\_K-IQ4\_XS file | A 5.61 GB build for a tight 8 GB | | Vision projector file | A 0.92 GB file that adds image input | ### Example You have an 8 GB graphics card, so you take the 5.93 GB AD-Q5\_K-Q4\_K file and load it with the vision projector. The card measured this file at a top-1 match of 93.10% against the reference. ### For newcomers and experts A smaller file fits less memory but loses some accuracy. A good first try is the file the card lists for your amount of video memory, with a short chat. For readers who know the field, the card reports two measures against the BF16 reference. Mean KL divergence is better when lower. Top-1 counts how often a file chooses the same next word as the reference does. Tests used a held-out neutral text set at 4096 context on one RTX 5090 with llama.cpp b10505. The plain presets were built with that same matrix, so any gap comes from the AD layout alone. Check your graphics memory → Pick a file from the card's table → Download it → Load it, with the projector for images → Chat with the model ### Good to know - Sizes: the listed sizes already count the 0.9 GB projector and about 1 GB of overhead at 8k context. - Smallest file: the card publishes its two smallest files for completeness and says the smallest acts like a different model. - Warning: the 2.81 GB file agrees with the reference on only 53.74% of tokens. - Cut: the text available here ends before the card's chart discussion. - Counts: Hugging Face lists 9B parameters and about 4.5 GB at 4-bit as an estimate. Top-1 agreement with the BF16 reference, higher is better | Label | Value | | --- | --- | | Q8\_0 | 97.94% | | Q6\_K | 96.54% | | AD-Q5\_K-Q4\_K | 93.10% | | Q5\_K\_M | 92.80% | | AD-Q4\_K-IQ4\_XS | 91.93% | | IQ3\_M | 84.18% | | AD-IQ2\_XXS-IQ1\_M | 53.74% | - License: MIT. - Cost to run: none named. You need graphics memory that fits the file. - Data it reaches: the prompts and images you give it on your own machine. The text available here names no network use. - Not for: real work with the two smallest files, which the card publishes only for completeness. ## README ````markdown # Ornith-1.5-9B-GGUF by AtomicChat GGUF builds of Ornith 1.5 9B, a model that reads images and text, in sizes from 2.81 GB to 17.9 GB plus a vision projector. Pick the file that fits your graphics memory and run it locally. Made by AtomicChat 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: AtomicChat converted Ornith-1.5-9B, a 9B model that takes images and text, into GGUF files for local runners. The files range from 2.81 GB to 17.9 GB. The AD files use a bit layout for each tensor, tuned for this model and built with the team's own importance matrix. A separate vision projector file adds image input. The Atomic Chat app offers toggles for thinking. Who it suits: It suits people who want to run a 9B image and text model on their own graphics card and need a file that matches their memory. ```table Industry | Who uses it | How they use it | Example Local AI | People running models offline | Choose the file that fits their graphics memory | Take the 5.93 GB file on an 8 GB card Vision tasks | Users who ask about pictures | Load the vision projector beside the language file | Ask the model about a screenshot Research | Quantization researchers | Compare AD files with stock presets that share one matrix | Read the KL divergence and top-1 columns ``` You can skip it if you have less than 4 GB of video memory, since the card's smallest suggestion is for 4 GB. Where to use it: - llama.cpp, the runner the card measured with - Atomic Chat, the app from the same team - Windows, macOS and Linux with Python, to download the files ```table Use case | Who | What they get back Local assistant | Developers and hobbyists | A 9B model that answers in text on their own machine Picture questions | Users with the projector file | Answers that take an image into account File choice | People with limited memory | A file matched to 4, 6, 8, 12, 16 or 24 GB ``` How to set it up: You need these first: - Python, to use Hugging Face's download client - Graphics memory that fits the file you pick - llama.cpp or the Atomic Chat app 1. Install or update the Hugging Face download client. 2. Download the repository's files to your computer. 3. Pick a file from the card's memory table. 4. Load it in your runner, and add the vision projector if you want images. ```sh pip install -U huggingface_hub hf download AtomicChat/Ornith-1.5-9B-GGUF ``` How to use it: You load one language file in llama.cpp or Atomic Chat and talk to the model. Leave out the projector for text only. The text available here cuts off before the run steps, so follow the model card and its guide for the exact options. ```table Tool | What it does BF16 file | The reference build at 17.9 GB, for 24 GB and up Q8_0 file | A 9.53 GB build for 16 GB AD-Q5_K-Q4_K file | A 5.93 GB build for 8 GB, which the card calls the sweet spot AD-Q4_K-IQ4_XS file | A 5.61 GB build for a tight 8 GB Vision projector file | A 0.92 GB file that adds image input ``` Example: You have an 8 GB graphics card, so you take the 5.93 GB AD-Q5_K-Q4_K file and load it with the vision projector. The card measured this file at a top-1 match of 93.10% against the reference. For newcomers and experts: A smaller file fits less memory but loses some accuracy. A good first try is the file the card lists for your amount of video memory, with a short chat. For readers who know the field, the card reports two measures against the BF16 reference. Mean KL divergence is better when lower. Top-1 counts how often a file chooses the same next word as the reference does. Tests used a held-out neutral text set at 4096 context on one RTX 5090 with llama.cpp b10505. The plain presets were built with that same matrix, so any gap comes from the AD layout alone. ```flow Check your graphics memory Pick a file from the card's table Download it Load it, with the projector for images Chat with the model ``` Good to know: - Sizes: the listed sizes already count the 0.9 GB projector and about 1 GB of overhead at 8k context. - Smallest file: the card publishes its two smallest files for completeness and says the smallest acts like a different model. - Warning: the 2.81 GB file agrees with the reference on only 53.74% of tokens. - Cut: the text available here ends before the card's chart discussion. - Counts: Hugging Face lists 9B parameters and about 4.5 GB at 4-bit as an estimate. ```chart Title: Top-1 agreement with the BF16 reference, higher is better Q8_0: 97.94% Q6_K: 96.54% AD-Q5_K-Q4_K: 93.10% Q5_K_M: 92.80% AD-Q4_K-IQ4_XS: 91.93% IQ3_M: 84.18% AD-IQ2_XXS-IQ1_M: 53.74% ``` - License: MIT. - Cost to run: none named. You need graphics memory that fits the file. - Data it reaches: the prompts and images you give it on your own machine. The text available here names no network use. - Not for: real work with the two smallest files, which the card publishes only for completeness. ## On Hugging Face, 14 November 2026 - Task: image text to text - Library: gguf - Parameters: 9B - Weights: about 4.5 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 41,746 - Likes: 83 - License: mit - Based on: ornith-ai/Ornith-1.5-9B - Tags: gguf, llama.cpp, vision, agentic-coding, imatrix, conversational, image-text-to-text ## Links - Model: https://huggingface.co/AtomicChat/Ornith-1.5-9B-GGUF - Files: https://huggingface.co/AtomicChat/Ornith-1.5-9B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download AtomicChat/Ornith-1.5-9B-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [AtomicChat/Ornith-1.5-9B-GGUF on Hugging Face](https://huggingface.co/AtomicChat/Ornith-1.5-9B-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-1.5-9B-GGUF by AtomicChat (this page) | 41,746 | 83 | 9B | mit | 2026-10-28 | | [Ripple-65B-Preview-GGUF](https://mindket.com/p/00143f02-5553-4372-8620-836dbb7dfec1) | 100,818 | 53 | 64.8B | apache-2.0 | 2026-11-06 | | [MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF](https://mindket.com/p/81b8b624-a1fa-4440-b7f9-b44fdafef92b) | 1,548 | 2 | 9B | apache-2.0 | 2026-10-15 | | [Groot-19B-A3B-TTQ-GGUF](https://mindket.com/p/6a828233-6f05-4485-bd2e-8181cc50d297) | 6,541 | 4 | 19B | apache-2.0 | 2026-11-06 | | [Qwen3.8-27B-Ridge-GGUF](https://mindket.com/p/f254bd76-fee2-4e04-80ac-a73f9621c088) | 292,932 | 369 | 27.3B | apache-2.0 | 2026-08-15 | | [KAT-Coder-V2.5-Dev GGUF](https://mindket.com/p/e8ca97ed-d610-4d96-bb2f-d351f4b671c4) | 234,191 | 148 | 34.7B | apache-2.0 | 2026-07-24 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-10-28 - First published: 2026-08-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open AtomicChat/Ornith-1.5-9B-GGUF on Hugging Face](https://huggingface.co/AtomicChat/Ornith-1.5-9B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e8c0edff-085e-4d2a-98f1-7bfdabe54557) - [Its page on Mindket](https://mindket.com/p/e8c0edff-085e-4d2a-98f1-7bfdabe54557)