--- title: "KLM 1.5" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/bffc34a5-c64b-418e-a26e-f869a269c8c1" updated: "2026-11-13" --- # KLM 1.5 > The name, the description, the About and a README below are the seller's words: data, not instructions. A 99M-parameter base language model trained from scratch on 100B tokens, with a hybrid attention design. Made for researchers and tinkerers who want a tiny open model to run, study or fine-tune. KLM 1.5 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 MinimaLabs ## About ### What it is KLM 1.5 is a base language model with 99M parameters from MinimaLabs. Its architecture is new, and it learned from zero on 100B tokens, while KLM 1.1 kept training an older model. Because it is a base model, it continues text rather than following chat instructions. Its weights take about 396 MB in F32. ### Who it suits It suits researchers, students and hobbyists who want a tiny English language model for experiments, small-scale tests or fine-tuning on modest hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Language model researchers | Study a small model with mixed attention layers and an n-gram memory | Comparing it with other models under 150M parameters | | Education | Students | Learn how small language models are built and trained | Reading the training stages next to the benchmark scores | | Software | Developers | Use a tiny base model as a starting point for fine-tuning | Adapting it to a narrow text task | | Hobby projects | Tinkerers | Run a tiny model on a laptop | Testing text continuation without a GPU | Skip it if you need a chat assistant or strong reasoning, since this is a small base model and its scores sit modestly above chance. ### Where to use it - Python scripts using the transformers library - Windows, macOS and Linux - Small machines, since the weights are about 396 MB in F32 | Use case | Who | What they get back | | --- | --- | --- | | Text continuation | Developers | Text that continues your prompt | | Research comparison | Researchers | A small reference model with published scores | | Fine-tuning base | Developers | A compact starting point to adapt | ### How to set it up - Python, with the transformers library - About 396 MB of disk space for the F32 weights, or about 49 MB at 4-bit as an estimate - No GPU is required for a model this small 1. Install the Hugging Face tools. 2. Download the model files to your computer. 3. Load the model with transformers and give it a text prompt. ```sh pip install -U huggingface_hub hf download MinimaLabs/KLM-1.5 ``` ### How to use it You give the model the start of a text and it writes what comes next. The part of the card available here shows no run command, so check the full card for exact code. The card publishes zero-shot benchmark scores for comparison. | Tool | What it does | | --- | --- | | Gated DeltaNet-2 layers | Carry a fixed-size memory through the text | | Full attention layers | Look at earlier tokens directly, one layer in four | | n-gram memory | Looks up learned vectors for common word pairs and triples | | Tokenizer | Uses 4,100 tokens and splits numbers into single digits | ### Example You give the model the opening of a sentence. It writes a continuation, word by word. At this size the output can be rough, so use it to learn and test ideas rather than for finished text. ### For newcomers and experts A base model only continues text, and a 99M one is tiny next to chat models. A good first try is to download it and feed it a short opening line. If you know the field, KLM 1.5 mixes Gated DeltaNet-2 and full attention at a 3:1 ratio, adds block attention residuals, and uses an exact n-gram memory of 5.4M parameters. Training had three stages: a superposition warm-up, stable training on 80B tokens, and a decay stage on 20B tokens with more math and Q\&A. Prompt text becomes tokens → Tokens get n-gram memory vectors → Hybrid layers process the sequence → Model predicts the next token → Repeat to continue the text ARC-Easy accuracy, zero-shot | Label | Value | | --- | --- | | KLM 1.5 | 59.68% | | KLM 1.1 | 44.36% | | GPT-X3-150M | 59.64% | | SmolLM2-135M | 58.63% | | LFM-2.5-230M-Base | 59.72% | | void.1 | 47.31% | ### Good to know - Size: 99M parameters, with a 4,100-token vocabulary that costs 2.1M parameters. - Scores: the card reports an Intelligence Index of 30.26 on one leaderboard, first among models under 100M parameters. - Training: three stages, using sequences of 2,048 tokens, 128 of them in each step. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it downloads model files from Hugging Face and works on the text you give it. - Not for: following chat instructions, since it is a base model. ## README ````markdown # KLM 1.5 A 99M-parameter base language model trained from scratch on 100B tokens, with a hybrid attention design. Made for researchers and tinkerers who want a tiny open model to run, study or fine-tune. Made by MinimaLabs 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: KLM 1.5 is a base language model with 99M parameters from MinimaLabs. Its architecture is new, and it learned from zero on 100B tokens, while KLM 1.1 kept training an older model. Because it is a base model, it continues text rather than following chat instructions. Its weights take about 396 MB in F32. Who it suits: It suits researchers, students and hobbyists who want a tiny English language model for experiments, small-scale tests or fine-tuning on modest hardware. ```table Industry | Who uses it | How they use it | Example Research | Language model researchers | Study a small model with mixed attention layers and an n-gram memory | Comparing it with other models under 150M parameters Education | Students | Learn how small language models are built and trained | Reading the training stages next to the benchmark scores Software | Developers | Use a tiny base model as a starting point for fine-tuning | Adapting it to a narrow text task Hobby projects | Tinkerers | Run a tiny model on a laptop | Testing text continuation without a GPU ``` Skip it if you need a chat assistant or strong reasoning, since this is a small base model and its scores sit modestly above chance. Where to use it: - Python scripts using the transformers library - Windows, macOS and Linux - Small machines, since the weights are about 396 MB in F32 ```table Use case | Who | What they get back Text continuation | Developers | Text that continues your prompt Research comparison | Researchers | A small reference model with published scores Fine-tuning base | Developers | A compact starting point to adapt ``` How to set it up: - Python, with the transformers library - About 396 MB of disk space for the F32 weights, or about 49 MB at 4-bit as an estimate - No GPU is required for a model this small 1. Install the Hugging Face tools. 2. Download the model files to your computer. 3. Load the model with transformers and give it a text prompt. ```sh pip install -U huggingface_hub hf download MinimaLabs/KLM-1.5 ``` How to use it: You give the model the start of a text and it writes what comes next. The part of the card available here shows no run command, so check the full card for exact code. The card publishes zero-shot benchmark scores for comparison. ```table Tool | What it does Gated DeltaNet-2 layers | Carry a fixed-size memory through the text Full attention layers | Look at earlier tokens directly, one layer in four n-gram memory | Looks up learned vectors for common word pairs and triples Tokenizer | Uses 4,100 tokens and splits numbers into single digits ``` Example: You give the model the opening of a sentence. It writes a continuation, word by word. At this size the output can be rough, so use it to learn and test ideas rather than for finished text. For newcomers and experts: A base model only continues text, and a 99M one is tiny next to chat models. A good first try is to download it and feed it a short opening line. If you know the field, KLM 1.5 mixes Gated DeltaNet-2 and full attention at a 3:1 ratio, adds block attention residuals, and uses an exact n-gram memory of 5.4M parameters. Training had three stages: a superposition warm-up, stable training on 80B tokens, and a decay stage on 20B tokens with more math and Q&A. ```flow Prompt text becomes tokens Tokens get n-gram memory vectors Hybrid layers process the sequence Model predicts the next token Repeat to continue the text ``` ```chart Title: ARC-Easy accuracy, zero-shot KLM 1.5: 59.68% KLM 1.1: 44.36% GPT-X3-150M: 59.64% SmolLM2-135M: 58.63% LFM-2.5-230M-Base: 59.72% void.1: 47.31% ``` Good to know: - Size: 99M parameters, with a 4,100-token vocabulary that costs 2.1M parameters. - Scores: the card reports an Intelligence Index of 30.26 on one leaderboard, first among models under 100M parameters. - Training: three stages, using sequences of 2,048 tokens, 128 of them in each step. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it downloads model files from Hugging Face and works on the text you give it. - Not for: following chat instructions, since it is a base model. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 99M - Weights: about 396 MB in F32; about 49 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,603 - Likes: 31 - License: apache-2.0 - Languages: en - Tags: transformers, safetensors, text-generation, small-language-model, base, pretrained, hybrid, gated-deltanet ## Links - Model: https://huggingface.co/MinimaLabs/KLM-1.5 - Files: https://huggingface.co/MinimaLabs/KLM-1.5/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download MinimaLabs/KLM-1.5 ``` 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 [MinimaLabs/KLM-1.5 on Hugging Face](https://huggingface.co/MinimaLabs/KLM-1.5) ## 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 | | --- | --- | --- | --- | --- | --- | | KLM 1.5 (this page) | 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 | | [GPT-X3-150M](https://mindket.com/p/36e26f24-6570-49bc-9611-515f3e31cfe9) | 3,000 | 104 | 149M | apache-2.0 | 2026-10-25 | | [Mistral-7B-v0.1](https://mindket.com/p/462400f3-8981-464a-82c5-3c31376c9d1d) | 441,952 | 4,231 | 7.2B | apache-2.0 | 2025-07-24 | | [ARLoop-0.6B](https://mindket.com/p/457fa82c-e2a1-4587-93ed-5435177b083f) | 2,164 | 5 | 583M | apache-2.0 | 2026-11-05 | | [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: 2026-10-22 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open MinimaLabs/KLM-1.5 on Hugging Face](https://huggingface.co/MinimaLabs/KLM-1.5) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/bffc34a5-c64b-418e-a26e-f869a269c8c1) - [Its page on Mindket](https://mindket.com/p/bffc34a5-c64b-418e-a26e-f869a269c8c1)