--- title: "ConeML 1.2B Experimental" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/abfa4d0f-0c8b-4a38-9901-2fa4a37bc1d8" updated: "2026-11-13" --- # ConeML 1.2B Experimental > The name, the description, the About and a README below are the seller's words: data, not instructions. Names the tallest, oldest or first item among facts you supply, as a small English research model in FP32 Safetensors and Q8\_0 GGUF files. For researchers, non-commercial use only. ConeML 1.2B Experimental is a kit in the Research 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 ConeML ## About ### What it is ConeML 1.2B Experimental is a small English research model. Given a set of stated relations, it answers which item is tallest, shortest, older, younger or first. The files come as FP32 Safetensors and as a Q8\_0 GGUF. The maker calls it experimental and says coding and everyday chat are much weaker. ### Who it suits Researchers who study small models, supplied-fact reasoning and how answers change with wording or fact order. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Model evaluators | Test how a small model picks an endpoint from stated facts | Compare it with other small instruct models on shuffled facts | | Academia | Students | Study how fact order and wording affect answers | Run the same question with ordered and shuffled facts | | Open-source tooling | Hobbyists | Try a model with few active parameters | Load the GGUF file and ask a short relational question | Skip it if you want a chat assistant, a coding helper or anything for commercial use. ### Where to use it - Python with the transformers library, for the Safetensors weights - A local GGUF runner, for the Q8\_0 file - Windows, macOS or Linux machines that can fetch the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Endpoint questions | Researchers | The name of the highest, lowest or first item from your facts | | Wording tests | Evaluators | Answers that show sensitivity to phrasing and fact order | | Small-model comparison | Students | A reference point against other models of similar size | ### How to set it up You need Python with pip and disk space for the files. The FP32 weights are about 4.7 GB, so check the repository's file list before you download everything. - Python and pip - Roughly 4.7 GB for FP32, less for the quantized file - The transformers library or a GGUF runner 1. Install the Hugging Face hub tool. 2. Download the repository's files. 3. Load the Safetensors weights in transformers, or open the GGUF file in a runner. ```bash pip install -U huggingface_hub hf download ConeML/coneml-1.2b-experimental ``` ### How to use it Write the facts in the prompt in any order, then ask which item sits at one end of the chain, such as the tallest or the earliest. The maker tested it with the model's own chat format. ```bash hf download ConeML/coneml-1.2b-experimental ``` | Tool | What it does | | --- | --- | | hf download | Copies this repository's files to your computer | ### Example You list a few shuffled statements about who is taller than whom, then ask who is tallest. The model names one person. In the maker's tests at depth 5 it got 438 of 512 such questions right. ### For newcomers and experts If you are new to this, start with three or four short facts and one question, and check the answer by hand. Do not expect good results on code or open conversation. If you know the field, the card describes about 348M active and 1.174B stored parameters. It compares the model with four small instruct models on shuffled endpoint questions. A later note says a simple set-difference rule solves every question, so the score shows endpoint selection, not multi-hop traversal. Write the facts in any order → Ask for an endpoint → The model names one item → Check the answer yourself ### Good to know Success falls as chains get longer. The card lists 438 of 512 at depth 5, 240 at depth 12 and 74 at depth 24. Correct answers at depth 5, out of 512 | Label | Value | | --- | --- | | ConeML | 438 | | Qwen2.5-1.5B-Instruct | 273 | | SmolLM2-1.7B-Instruct | 155 | | Llama-3.2-1B-Instruct | 136 | | Gemma-3-1B-IT | 138 | - License: CC BY-NC 4.0 - Cost to run: none named - Data it reaches: the files you download from Hugging Face, plus the text you type in - Not for: commercial use, coding or everyday conversation, and proof of multi-hop reasoning ## README ````markdown # ConeML 1.2B Experimental Names the tallest, oldest or first item among facts you supply, as a small English research model in FP32 Safetensors and Q8_0 GGUF files. For researchers, non-commercial use only. Made by ConeML 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: ConeML 1.2B Experimental is a small English research model. Given a set of stated relations, it answers which item is tallest, shortest, older, younger or first. The files come as FP32 Safetensors and as a Q8_0 GGUF. The maker calls it experimental and says coding and everyday chat are much weaker. Who it suits: Researchers who study small models, supplied-fact reasoning and how answers change with wording or fact order. ```table Industry | Who uses it | How they use it | Example AI research | Model evaluators | Test how a small model picks an endpoint from stated facts | Compare it with other small instruct models on shuffled facts Academia | Students | Study how fact order and wording affect answers | Run the same question with ordered and shuffled facts Open-source tooling | Hobbyists | Try a model with few active parameters | Load the GGUF file and ask a short relational question ``` Skip it if you want a chat assistant, a coding helper or anything for commercial use. Where to use it: - Python with the transformers library, for the Safetensors weights - A local GGUF runner, for the Q8_0 file - Windows, macOS or Linux machines that can fetch the files with Python ```table Use case | Who | What they get back Endpoint questions | Researchers | The name of the highest, lowest or first item from your facts Wording tests | Evaluators | Answers that show sensitivity to phrasing and fact order Small-model comparison | Students | A reference point against other models of similar size ``` How to set it up: You need Python with pip and disk space for the files. The FP32 weights are about 4.7 GB, so check the repository's file list before you download everything. - Python and pip - Roughly 4.7 GB for FP32, less for the quantized file - The transformers library or a GGUF runner 1. Install the Hugging Face hub tool. 2. Download the repository's files. 3. Load the Safetensors weights in transformers, or open the GGUF file in a runner. ```bash pip install -U huggingface_hub hf download ConeML/coneml-1.2b-experimental ``` How to use it: Write the facts in the prompt in any order, then ask which item sits at one end of the chain, such as the tallest or the earliest. The maker tested it with the model's own chat format. ```bash hf download ConeML/coneml-1.2b-experimental ``` ```table Tool | What it does hf download | Copies this repository's files to your computer ``` Example: You list a few shuffled statements about who is taller than whom, then ask who is tallest. The model names one person. In the maker's tests at depth 5 it got 438 of 512 such questions right. For newcomers and experts: If you are new to this, start with three or four short facts and one question, and check the answer by hand. Do not expect good results on code or open conversation. If you know the field, the card describes about 348M active and 1.174B stored parameters. It compares the model with four small instruct models on shuffled endpoint questions. A later note says a simple set-difference rule solves every question, so the score shows endpoint selection, not multi-hop traversal. ```flow Write the facts in any order Ask for an endpoint The model names one item Check the answer yourself ``` Good to know: Success falls as chains get longer. The card lists 438 of 512 at depth 5, 240 at depth 12 and 74 at depth 24. ```chart Title: Correct answers at depth 5, out of 512 ConeML: 438 Qwen2.5-1.5B-Instruct: 273 SmolLM2-1.7B-Instruct: 155 Llama-3.2-1B-Instruct: 136 Gemma-3-1B-IT: 138 ``` - License: CC BY-NC 4.0 - Cost to run: none named - Data it reaches: the files you download from Hugging Face, plus the text you type in - Not for: commercial use, coding or everyday conversation, and proof of multi-hop reasoning ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 1.2B - Weights: about 4.7 GB in F32; about 587 MB at 4-bit (estimate) - Formats: Safetensors, GGUF - Downloads in the last 30 days: 440 - Likes: 2 - License: cc-by-nc-4.0 - Languages: en - Tags: transformers, safetensors, gguf, mixtral, text-generation, mixture-of-experts, experimental, conversational ## Links - Model: https://huggingface.co/ConeML/coneml-1.2b-experimental - Files: https://huggingface.co/ConeML/coneml-1.2b-experimental/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ConeML/coneml-1.2b-experimental ``` 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 [ConeML/coneml-1.2b-experimental on Hugging Face](https://huggingface.co/ConeML/coneml-1.2b-experimental) ## 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 | | --- | --- | --- | --- | --- | --- | | ConeML 1.2B Experimental (this page) | 440 | 2 | 1.2B | cc-by-nc-4.0 | 2026-11-10 | | [Diffusion-Qwen3.8-27B](https://mindket.com/p/eda0723e-778a-4f43-807f-07f47bae3a29) | 2,015 | 33 | 26.9B | apache-2.0 | 2026-11-06 | | [Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled](https://mindket.com/p/a8bee0d0-7f10-412b-a709-a6267eb87a87) | 674 | 206 | 36B | apache-2.0 | 2026-04-23 | | [Apodex-1.1-mini-GGUF](https://mindket.com/p/a81892fc-8560-455b-890b-8f7796f79d9b) | 479 | 22 | 35.5B | apache-2.0 | 2026-09-10 | | [Sage-A1.5 GGUF (Q4\_K\_M)](https://mindket.com/p/7a186f44-d286-4d23-a115-416b5ad0f438) | 636,834 | 84 | 35.5B | apache-2.0 | 2026-10-22 | | [qwen-concept-27b](https://mindket.com/p/58083034-bfad-48bc-8a59-6417d3acb2ee) | 62,460 | 174 | 27.3B | apache-2.0 | 2026-11-08 | 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: 2026-11-10 - First published: 2026-10-29 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ConeML/coneml-1.2b-experimental on Hugging Face](https://huggingface.co/ConeML/coneml-1.2b-experimental) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/abfa4d0f-0c8b-4a38-9901-2fa4a37bc1d8) - [Its page on Mindket](https://mindket.com/p/abfa4d0f-0c8b-4a38-9901-2fa4a37bc1d8)