What are the hardware requirements for LiLM1 230M Base?

LiLM1 230M Base requires approximately 0.5 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed LiLM1 230M Base and how large is it?

LiLM1 230M Base is developed by glouriousgautam, a model with 230m 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 LiLM1 230M Base?

LiLM1 230M Base is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LiLM1 230M Base by glouriousgautam — VRAM 0.5GB, 4K context

Name: LiLM1 230M Base
Author: glouriousgautam

LiLM1 230M Base is an open-source language model by glouriousgautam. Features: 230m LLM, VRAM: 0.5GB, Context: 4K, License: apache-2.0, LLM Explorer Score: 0.32.

Base-model Custom code En Feature-extraction Lilm Region:us Safetensors Small-language-model

Model Card on HF 🤗: https://huggingface.co/glouriousgautam/LiLM1-230M-base

LiLM1 230M Base Parameters and Internals

LLM Name	LiLM1 230M Base
Repository 🤗	https://huggingface.co/glouriousgautam/LiLM1-230M-base
Model Size	230m
Required VRAM	0.5 GB
Updated	2026-09-04
Maintainer	glouriousgautam
Model Type	lilm
Model Files	0.5 GB
Supported Languages	en
Model Architecture	LilmForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	5.10.1
Tokenizer Class	GPT2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	49152
Errors	replace

Rank the LiLM1 230M 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 LiLM1 230M Base

Best Alternatives	Context / RAM	Downloads	Likes
LiLM1 230M	4K / 0.5 GB	339	1

Note: green Score (e.g. "73.2") means that the model is better than glouriousgautam/LiLM1-230M-base.