--- title: "GPT-X3-150M" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/36e26f24-6570-49bc-9611-515f3e31cfe9" updated: "2026-11-13" --- # GPT-X3-150M > The name, the description, the About and a README below are the seller's words: data, not instructions. A 149M-parameter base text model from Axiomic Labs that continues the text you give it. The card reports an Intelligence Index of 33.05, ahead of SmolLM2-135M at 27.13. About 598 MB in F32. GPT-X3-150M 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 AxiomicLabs ## About ### What it is GPT-X3-150M is the third base model from Axiomic Labs, trained from scratch with the team's own TrainWork tools. It uses the TX5 design, which mixes two kinds of attention layers. It has 149M parameters and works in English. As a base model it continues text rather than following chat instructions. The card says it tops the authors' own small model leaderboard. ### Who it suits It suits researchers, students and developers who want a tiny language model to study, fine tune or use in quick tests. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Small model researchers | Compare it with other models under 150M parameters | Check its Intelligence Index against SmolLM2-135M | | Education | Students learning about language models | Study a hybrid attention design at small scale | Read the layer layout of 24 layers in a 3 to 1 mix | | Prototyping | Developers testing pipelines | Use a tiny base model for fast trials | Complete a short prompt with a 598 MB model | You can skip it if you want a chat assistant, since it is a base model and not tuned to follow instructions. ### Where to use it - Python scripts that use the transformers library - Fine tuning and evaluation setups for small models - Windows, macOS and Linux with Python | Use case | Who | What they get back | | --- | --- | --- | | Text completion | Developers | A continuation of the text they give it | | Benchmark comparison | Researchers | Scores to set beside other small models | | Architecture study | Students | A working example of a 3 to 1 hybrid stack | ### How to set it up You need these first: - Python on Windows, macOS or Linux - The transformers library - About 598 MB of disk space for the F32 weights 1. Install or update the Hugging Face download client. 2. Download the model files to your computer. 3. Load the model in your own transformers code. 4. Read the full model card for any extra loading options. ```sh pip install -U huggingface_hub hf download AxiomicLabs/GPT-X3-150M ``` ### How to use it You load the model in Python and give it the start of a text to continue. The card text available here shows no code, and the architecture is custom, so check the full card for how to load it. | Tool | What it does | | --- | --- | | Intelligence Index | The card's combined score across four benchmark groups | | Open SLM Leaderboard | The authors' own board, which the card's results follow | | HellaSwag and PIQA | Common sense benchmarks that feed the index | | ARC | Science question benchmarks, counted as one group | | ArithMark-3 | An arithmetic benchmark with a lower weight in the index | ### Example You open the card's table and compare the model's Intelligence Index of 33.05 with SmolLM2-135M at 27.13. The card says it got there on 75B training tokens, while SmolLM2-135M used 2T. ### For newcomers and experts A base model predicts what text comes next, so it works best when you give it the start of a sentence. A good first try is a short prompt and a few generated words. For readers who know the field, the stack has 24 layers, 18 of them GDN2 and 6 of them gated GQA, in a 3 to 1 pattern. The vocabulary has 65,536 tokens from the LFM2.5 tokenizer. Training used a fixed nine source mix, a six token objective for the first 30 percent, and Muon with AdamW. Download the model files → Load them with transformers → Give it the start of a text → It predicts the next tokens → Read the continuation ### Good to know - Claims: the top ranking and the first under 150M claim come from the authors' own board and card. - Index: the score corrects four benchmark groups for lucky guesses and counts the arithmetic one at 0.65, against 1.00 for the other three. - Training: about 75B compute tokens, with about 187.5B raw tokens in total. - Context: the config lists a block size of 2048 tokens. - Cut: the later config lines are cut off in the text available here. - Counts: Hugging Face lists 149M parameters, 1 quantized and 1 fine tuned version from others. Intelligence Index from the card's table, higher is better | Label | Value | | --- | --- | | GPT-X3-150M | 33.05 | | LFM2.5-350M-Base | 30.50 | | Boris-2-Preview-1 | 27.91 | | cagliostro-v3.5 | 27.49 | | SmolLM2-135M | 27.13 | | GPT-2 Medium | 19.68 | | GPT-2 (124M) | 13.58 | - License: Apache 2.0. - Cost to run: none named. The weights take about 598 MB in F32. - Data it reaches: the text you give it on your own machine. The text available here names no network use. - Not for: chat or instruction following, since the card calls it a base model. ## README ````markdown # GPT-X3-150M A 149M-parameter base text model from Axiomic Labs that continues the text you give it. The card reports an Intelligence Index of 33.05, ahead of SmolLM2-135M at 27.13. About 598 MB in F32. Made by AxiomicLabs 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: GPT-X3-150M is the third base model from Axiomic Labs, trained from scratch with the team's own TrainWork tools. It uses the TX5 design, which mixes two kinds of attention layers. It has 149M parameters and works in English. As a base model it continues text rather than following chat instructions. The card says it tops the authors' own small model leaderboard. Who it suits: It suits researchers, students and developers who want a tiny language model to study, fine tune or use in quick tests. ```table Industry | Who uses it | How they use it | Example Research | Small model researchers | Compare it with other models under 150M parameters | Check its Intelligence Index against SmolLM2-135M Education | Students learning about language models | Study a hybrid attention design at small scale | Read the layer layout of 24 layers in a 3 to 1 mix Prototyping | Developers testing pipelines | Use a tiny base model for fast trials | Complete a short prompt with a 598 MB model ``` You can skip it if you want a chat assistant, since it is a base model and not tuned to follow instructions. Where to use it: - Python scripts that use the transformers library - Fine tuning and evaluation setups for small models - Windows, macOS and Linux with Python ```table Use case | Who | What they get back Text completion | Developers | A continuation of the text they give it Benchmark comparison | Researchers | Scores to set beside other small models Architecture study | Students | A working example of a 3 to 1 hybrid stack ``` How to set it up: You need these first: - Python on Windows, macOS or Linux - The transformers library - About 598 MB of disk space for the F32 weights 1. Install or update the Hugging Face download client. 2. Download the model files to your computer. 3. Load the model in your own transformers code. 4. Read the full model card for any extra loading options. ```sh pip install -U huggingface_hub hf download AxiomicLabs/GPT-X3-150M ``` How to use it: You load the model in Python and give it the start of a text to continue. The card text available here shows no code, and the architecture is custom, so check the full card for how to load it. ```table Tool | What it does Intelligence Index | The card's combined score across four benchmark groups Open SLM Leaderboard | The authors' own board, which the card's results follow HellaSwag and PIQA | Common sense benchmarks that feed the index ARC | Science question benchmarks, counted as one group ArithMark-3 | An arithmetic benchmark with a lower weight in the index ``` Example: You open the card's table and compare the model's Intelligence Index of 33.05 with SmolLM2-135M at 27.13. The card says it got there on 75B training tokens, while SmolLM2-135M used 2T. For newcomers and experts: A base model predicts what text comes next, so it works best when you give it the start of a sentence. A good first try is a short prompt and a few generated words. For readers who know the field, the stack has 24 layers, 18 of them GDN2 and 6 of them gated GQA, in a 3 to 1 pattern. The vocabulary has 65,536 tokens from the LFM2.5 tokenizer. Training used a fixed nine source mix, a six token objective for the first 30 percent, and Muon with AdamW. ```flow Download the model files Load them with transformers Give it the start of a text It predicts the next tokens Read the continuation ``` Good to know: - Claims: the top ranking and the first under 150M claim come from the authors' own board and card. - Index: the score corrects four benchmark groups for lucky guesses and counts the arithmetic one at 0.65, against 1.00 for the other three. - Training: about 75B compute tokens, with about 187.5B raw tokens in total. - Context: the config lists a block size of 2048 tokens. - Cut: the later config lines are cut off in the text available here. - Counts: Hugging Face lists 149M parameters, 1 quantized and 1 fine tuned version from others. ```chart Title: Intelligence Index from the card's table, higher is better GPT-X3-150M: 33.05 LFM2.5-350M-Base: 30.50 Boris-2-Preview-1: 27.91 cagliostro-v3.5: 27.49 SmolLM2-135M: 27.13 GPT-2 Medium: 19.68 GPT-2 (124M): 13.58 ``` - License: Apache 2.0. - Cost to run: none named. The weights take about 598 MB in F32. - Data it reaches: the text you give it on your own machine. The text available here names no network use. - Not for: chat or instruction following, since the card calls it a base model. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 149M - Weights: about 598 MB in F32; about 75 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,000 - Likes: 104 - License: apache-2.0 - Languages: en - Quantized versions: 1 - Fine-tunes: 1 - Tags: transformers, safetensors, gptx3, text-generation, gptx, gdn2, hybrid, math, language-model, gpt-x3, gpt-x3-150m, open_slm_leaderboard ## Links - Model: https://huggingface.co/AxiomicLabs/GPT-X3-150M - Files: https://huggingface.co/AxiomicLabs/GPT-X3-150M/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download AxiomicLabs/GPT-X3-150M ``` 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 [AxiomicLabs/GPT-X3-150M on Hugging Face](https://huggingface.co/AxiomicLabs/GPT-X3-150M) ## 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 | | --- | --- | --- | --- | --- | --- | | GPT-X3-150M (this page) | 3,000 | 104 | 149M | apache-2.0 | 2026-10-25 | | [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 | | [Phi-4](https://mindket.com/p/24d9cdd5-f11c-4640-9fa0-0804022f7709) | 236,972 | 2,343 | 14.7B | mit | 2026-07-14 | | [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: 2026-10-25 - First published: 2026-09-23 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open AxiomicLabs/GPT-X3-150M on Hugging Face](https://huggingface.co/AxiomicLabs/GPT-X3-150M) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/36e26f24-6570-49bc-9611-515f3e31cfe9) - [Its page on Mindket](https://mindket.com/p/36e26f24-6570-49bc-9611-515f3e31cfe9)