--- title: "mine:o2-small" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/03fc6b43-2624-4d8f-a9bb-cf985f6a0e95" updated: "2026-11-12" --- # mine:o2-small > The name, the description, the About and a README below are the seller's words: data, not instructions. A small 355M English chat model from MineAI Technology that follows a simple User and Assistant format. A research-grade model that stops cleanly and writes short code, with no added knowledge. mine:o2-small 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 MineAITechnology ## About ### What it is mine:o2-small SFT is a 355M-parameter chat model from MineAI Technology, developed by Bilal Mehtab. It is a decoder-only model in the GPT-NeoX family with a 4096-token context window. It was fine-tuned in three short rounds on about 50M training tokens, and it answers in a User and Assistant format. ### Who it suits Learners and researchers who want a tiny model they can run and inspect, and developers who need a lightweight chat baseline. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | ML researchers | Study a small decoder-only chat model and its limits | A baseline for a small-model comparison | | Education | Students | Learn how fine-tuning shapes behavior | A walkthrough of three SFT rounds | | Software | Developers | Test a chat format on a tiny model | A prototype that stops cleanly | | Hobby | Tinkerers | Run a model on modest hardware | A local chat toy | If you need accurate facts or reasoning, you can skip it, since the card describes it as a small research model. ### Where to use it - Windows, macOS or Linux with Python, using the transformers library - Local experiments on a laptop, given the small size - Teaching and benchmarking setups that compare small models | Use case | Who | What they get back | | --- | --- | --- | | Short chat answers | Individuals | Brief replies that end cleanly | | Short code snippets | Learners | Small pieces of code | | Safety behavior checks | Researchers | Clear refusals of plainly harmful requests | ### How to set it up - Python, to run the Hugging Face download tool - The transformers library to load the model - Modest memory, since the model has about 355M parameters 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Load the model with transformers and follow the card for the prompt format. ```bash pip install -U huggingface_hub hf download MineAITechnology/mine-o2-small ``` ### How to use it Write your message as a User turn and let the model continue as the Assistant. The card says it follows this simple chat format, stops cleanly and knows who made it. ### Example You write a User turn asking for a short Python function that adds two numbers. The model answers as the Assistant with a few lines of code and then stops, but do not rely on it for facts. ### For newcomers and experts Start with one short instruction and read the reply critically. The card is frank that this is a small research model, so treat it as a toy or a baseline. It uses 24 Transformer blocks with a hidden size of 1024, 16 attention heads and rotary position embeddings. Input and output embeddings are tied, and the vocabulary has 50,257 tokens. The card ranks it level with its base model and behind SmolLM2 360M and Qwen2.5 0.5B on standard tests. Write a User turn → Model continues as the Assistant → It ends with its end-of-answer token → A short reply comes back ### Good to know - License: Apache 2.0, and the card asks you to check the data licences before redistributing derived data - Cost to run: none named - Data it reaches: downloads files from Hugging Face, then runs on your own machine - Not for: added knowledge or reasoning, which the card says fine-tuning did not provide - Limits: the card ranks it below larger peers, so expect weaker answers ## README ````markdown # mine:o2-small A small 355M English chat model from MineAI Technology that follows a simple User and Assistant format. A research-grade model that stops cleanly and writes short code, with no added knowledge. Made by MineAITechnology 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: mine:o2-small SFT is a 355M-parameter chat model from MineAI Technology, developed by Bilal Mehtab. It is a decoder-only model in the GPT-NeoX family with a 4096-token context window. It was fine-tuned in three short rounds on about 50M training tokens, and it answers in a User and Assistant format. Who it suits: Learners and researchers who want a tiny model they can run and inspect, and developers who need a lightweight chat baseline. ```table Industry | Who uses it | How they use it | Example Research | ML researchers | Study a small decoder-only chat model and its limits | A baseline for a small-model comparison Education | Students | Learn how fine-tuning shapes behavior | A walkthrough of three SFT rounds Software | Developers | Test a chat format on a tiny model | A prototype that stops cleanly Hobby | Tinkerers | Run a model on modest hardware | A local chat toy ``` If you need accurate facts or reasoning, you can skip it, since the card describes it as a small research model. Where to use it: - Windows, macOS or Linux with Python, using the transformers library - Local experiments on a laptop, given the small size - Teaching and benchmarking setups that compare small models ```table Use case | Who | What they get back Short chat answers | Individuals | Brief replies that end cleanly Short code snippets | Learners | Small pieces of code Safety behavior checks | Researchers | Clear refusals of plainly harmful requests ``` How to set it up: - Python, to run the Hugging Face download tool - The transformers library to load the model - Modest memory, since the model has about 355M parameters 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Load the model with transformers and follow the card for the prompt format. ```bash pip install -U huggingface_hub hf download MineAITechnology/mine-o2-small ``` How to use it: Write your message as a User turn and let the model continue as the Assistant. The card says it follows this simple chat format, stops cleanly and knows who made it. Example: You write a User turn asking for a short Python function that adds two numbers. The model answers as the Assistant with a few lines of code and then stops, but do not rely on it for facts. For newcomers and experts: Start with one short instruction and read the reply critically. The card is frank that this is a small research model, so treat it as a toy or a baseline. It uses 24 Transformer blocks with a hidden size of 1024, 16 attention heads and rotary position embeddings. Input and output embeddings are tied, and the vocabulary has 50,257 tokens. The card ranks it level with its base model and behind SmolLM2 360M and Qwen2.5 0.5B on standard tests. ```flow Write a User turn Model continues as the Assistant It ends with its end-of-answer token A short reply comes back ``` Good to know: - License: Apache 2.0, and the card asks you to check the data licences before redistributing derived data - Cost to run: none named - Data it reaches: downloads files from Hugging Face, then runs on your own machine - Not for: added knowledge or reasoning, which the card says fine-tuning did not provide - Limits: the card ranks it below larger peers, so expect weaker answers ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Downloads in the last 30 days: 0 - Likes: 8 - License: apache-2.0 - Languages: en - Tags: transformers, text-generation, causal-lm, chat, gpt-neox, small-language-model, mine-o2, model-index ## Links - Model: https://huggingface.co/MineAITechnology/mine-o2-small - Files: https://huggingface.co/MineAITechnology/mine-o2-small/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download MineAITechnology/mine-o2-small ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [MineAITechnology/mine-o2-small on Hugging Face](https://huggingface.co/MineAITechnology/mine-o2-small) ## 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 | | --- | --- | --- | --- | --- | --- | | mine:o2-small (this page) | 0 | 8 | — | apache-2.0 | 2026-11-01 | | [Maple-Preview](https://mindket.com/p/3ee0840f-eaf7-4a88-a790-788a9c9f032d) | 1,867 | 433 | 20.2B | mit | 2026-08-04 | | [Kiyo-65M](https://mindket.com/p/41f070ee-1394-4748-845b-ecb0e0d53d8e) | 1,179 | 5 | 65M | apache-2.0 | 2026-10-23 | | [KLM 1.5](https://mindket.com/p/bffc34a5-c64b-418e-a26e-f869a269c8c1) | 1,603 | 31 | 99M | apache-2.0 | 2026-11-01 | | [KLM 1.6](https://mindket.com/p/8b218890-16d1-4ff9-b98e-3eea5e8533e8) | 909 | 60 | 99M | apache-2.0 | 2026-11-09 | | [CherryPit-1.2-2B](https://mindket.com/p/0bb34d27-c0ed-4bcc-a9b2-190ab2f83299) | 451 | 6 | 2.5B | apache-2.0 | 2026-11-06 | 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: 2026-11-01 - First published: 2026-11-01 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open MineAITechnology/mine-o2-small on Hugging Face](https://huggingface.co/MineAITechnology/mine-o2-small) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/03fc6b43-2624-4d8f-a9bb-cf985f6a0e95) - [Its page on Mindket](https://mindket.com/p/03fc6b43-2624-4d8f-a9bb-cf985f6a0e95)