What are the hardware requirements for LFM2.5 230M Chess?

LFM2.5 230M Chess requires approximately 0.5 GB of VRAM and supports a context window of 125K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed LFM2.5 230M Chess and how large is it?

LFM2.5 230M Chess is developed by mlabonne, 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 LFM2.5 230M Chess?

LFM2.5 230M Chess is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LFM2.5 230M Chess by mlabonne — VRAM 0.5GB, 125K context

Name: LFM2.5 230M Chess
Author: mlabonne

LFM2.5 230M Chess is an open-source language model by mlabonne. Features: 230m LLM, VRAM: 0.5GB, Context: 125K, License: other, LLM Explorer Score: 0.39.

Base model:finetune:liquidai/l... Base model:liquidai/lfm2.5-230... Chess Conversational En Endpoints compatible Lfm2 Lfm2.5 Liquid Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/mlabonne/LFM2.5-230M-Chess

LFM2.5 230M Chess Parameters and Internals

LLM Name	LFM2.5 230M Chess
Repository 🤗	https://huggingface.co/mlabonne/LFM2.5-230M-Chess
Base Model(s)	LiquidAI/LFM2.5-230M-Base LiquidAI/LFM2.5-230M-Base
Model Size	230m
Required VRAM	0.5 GB
Updated	2026-09-18
Maintainer	mlabonne
Model Type	lfm2
Model Files	0.5 GB 0.0 GB
Supported Languages	en
Model Architecture	Lfm2ForCausalLM
License	other
Context Length	128000
Model Max Length	128000
Transformers Version	4.57.6
Tokenizer Class	TokenizersBackend
Padding Token	<\|pad\|>
Vocabulary Size	66508

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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

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