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

LFM2.5 230M Openjev 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 Openjev and how large is it?

LFM2.5 230M Openjev is developed by iromu, 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 Openjev?

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

LFM2.5 230M Openjev by iromu — VRAM 0.5GB, 125K context

Name: LFM2.5 230M Openjev
Author: iromu

LFM2.5 230M Openjev is an open-source language model by iromu. Features: 230m LLM, VRAM: 0.5GB, Context: 125K, License: apache-2.0, LLM Explorer Score: 0.32.

Base model:adapter:liquidai/lf... Base model:liquidai/lfm2.5-230... Classification Conversational Dataset:zefancai/open-jev Decision En Endpoints compatible Lfm Lfm2 Lfm2.5 Lora Region:us Safetensors Sft Typed-decision

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

LFM2.5 230M Openjev Parameters and Internals

LLM Name	LFM2.5 230M Openjev
Repository 🤗	https://huggingface.co/iromu/LFM2.5-230M-openjev
Base Model(s)	LiquidAI/LFM2.5-230M-Base LiquidAI/LFM2.5-230M-Base
Model Size	230m
Required VRAM	0.5 GB
Updated	2026-11-03
Maintainer	iromu
Model Type	lfm2
Model Files	0.5 GB
Supported Languages	en
Model Architecture	Lfm2ForCausalLM
License	apache-2.0
Context Length	128000
Model Max Length	128000
Transformers Version	5.12.1
Tokenizer Class	TokenizersBackend
Padding Token	<\|pad\|>
Vocabulary Size	65536
LoRA Model	Yes

Rank the LFM2.5 230M Openjev 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

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Note: green Score (e.g. "73.2") means that the model is better than iromu/LFM2.5-230M-openjev.