What are the hardware requirements for Linux As A Model 32M?

Linux As A Model 32M requires approximately 0.1 GB of VRAM and supports a context window of 1K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Linux As A Model 32M and how large is it?

Linux As A Model 32M is developed by mjbommar, a model with 32m 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 Linux As A Model 32M?

Linux As A Model 32M is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Linux As A Model 32M by mjbommar — VRAM 0.1GB, 1K context

Name: Linux As A Model 32M
Author: mjbommar

Linux As A Model 32M is an open-source language model by mjbommar. Features: 32m LLM, VRAM: 0.1GB, Context: 1K, License: mit, LLM Explorer Score: 0.13.

En Endpoints compatible Gpl2 Linux Llama Mit Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/mjbommar/linux-as-a-model-32M

Linux As A Model 32M Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

text generation

Additional Notes

Model trained to demonstrate legal and ethical implications of training AI models on open-source data. The focus is on commenting the OSI's stance on 'open' models.

Supported Languages

en (proficient)

Training Details

Data Sources:

Linux version 1.0 source code

Model Architecture:

Llama2

LLM Name	Linux As A Model 32M
Repository 🤗	https://huggingface.co/mjbommar/linux-as-a-model-32M
Model Size	32m
Required VRAM	0.1 GB
Updated	2026-07-20
Maintainer	mjbommar
Model Type	llama
Model Files	0.1 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	mit
Context Length	1024
Model Max Length	1024
Transformers Version	4.41.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|pad\|>
Vocabulary Size	32769
Torch Data Type	float32

Rank the Linux As A Model 32M 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

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Note: green Score (e.g. "73.2") means that the model is better than mjbommar/linux-as-a-model-32M.