What are the hardware requirements for Tiny Ko 124M Base?

Tiny Ko 124M Base requires approximately 0.2 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tiny Ko 124M Base and how large is it?

Tiny Ko 124M Base is developed by minpeter, a model with 124m 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 Ko 124M Base?

Tiny Ko 124M Base is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tiny Ko 124M Base by minpeter — VRAM 0.2GB, 2K context

Name: Tiny Ko 124M Base
Author: minpeter

Tiny Ko 124M Base is an open-source language model by minpeter. Features: 124m LLM, VRAM: 0.2GB, Context: 2K, LLM Explorer Score: 0.19.

Arxiv:1910.09700 Dataset:huggingfacetb/smollm-c... Dataset:minpeter/tiny-ko-corpu... Endpoints compatible Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/minpeter/tiny-ko-124m-base

Tiny Ko 124M Base Parameters and Internals

LLM Name	Tiny Ko 124M Base
Repository 🤗	https://huggingface.co/minpeter/tiny-ko-124m-base
Model Size	124m
Required VRAM	0.2 GB
Updated	2026-07-19
Maintainer	minpeter
Model Type	llama
Model Files	0.2 GB 0.0 GB
Model Architecture	LlamaForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.52.4
Tokenizer Class	PreTrainedTokenizer
Padding Token	<\|pad_token\|>
Vocabulary Size	32018
Torch Data Type	bfloat16

Rank the Tiny Ko 124M Base 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

Best Alternatives to Tiny Ko 124M Base

Best Alternatives	Context / RAM	Downloads	Likes
Tiny Ko 124M Base Muon	2K / 0.2 GB	54	1

Note: green Score (e.g. "73.2") means that the model is better than minpeter/tiny-ko-124m-base.