What are the hardware requirements for LowOnMind 1M?

LowOnMind 1M requires a context window of 0.5K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed LowOnMind 1M and how large is it?

LowOnMind 1M is developed by DedeProGames, a model with 1m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate LowOnMind 1M?

LowOnMind 1M is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LowOnMind 1M by DedeProGames — 0.5K context

Name: LowOnMind 1M
Author: DedeProGames

LowOnMind 1M is an open-source language model by DedeProGames. Features: 1m LLM, Context: 0.5K, License: apache-2.0.

Custom code Dataset:huggingfacefw/fineweb-... En Lowonmind Pretrained-from-scratch Region:us Safetensors Scaling-limits Tiny-lm

Model Card on HF 🤗: https://huggingface.co/SLM-Archive/LowOnMind-1M

LowOnMind 1M Parameters and Internals

LLM Name	LowOnMind 1M
Repository 🤗	https://huggingface.co/SLM-Archive/LowOnMind-1M
Model Size	1m
Required VRAM	0 GB
Updated	2026-10-03
Maintainer	DedeProGames
Model Type	lowonmind
Model Files	0.0 GB
Supported Languages	en
Model Architecture	LowOnMindForCausalLM
License	apache-2.0
Context Length	512
Model Max Length	512
Transformers Version	5.16.1
Tokenizer Class	TokenizersBackend
Padding Token	<\|pad\|>
Vocabulary Size	1024

Rank the LowOnMind 1M capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to LowOnMind 1M

Best Alternatives	Context / RAM	Downloads	Likes
LowOnMind 1M	0.5K / 0 GB	503	3

Note: green Score (e.g. "73.2") means that the model is better than SLM-Archive/LowOnMind-1M.