What are the hardware requirements for Tiny Edu 166M?

Tiny Edu 166M requires approximately 0.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tiny Edu 166M and how large is it?

Tiny Edu 166M is developed by SlitherCode, a model with 166m 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 Tiny Edu 166M?

Tiny Edu 166M is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tiny Edu 166M by SlitherCode — VRAM 0.6GB

Name: Tiny Edu 166M
Author: SlitherCode

Tiny Edu 166M is an open-source language model by SlitherCode. Features: 166m LLM, VRAM: 0.6GB, License: mit, LLM Explorer Score: 0.23.

Custom-architecture Custom code En Fineweb-edu Parchment Pretrained Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/SlitherCode/tiny-edu-166m

Tiny Edu 166M Parameters and Internals

LLM Name	Tiny Edu 166M
Repository 🤗	https://huggingface.co/SlitherCode/tiny-edu-166m
Model Size	166m
Required VRAM	0.6 GB
Updated	2026-08-08
Maintainer	SlitherCode
Model Type	parchment
Model Files	0.6 GB
Supported Languages	en
Model Architecture	ParchmentForCausalLM
License	mit
Transformers Version	5.8.1
Tokenizer Class	TiktokenTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	100277

Rank the Tiny Edu 166M capabilities

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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