What are the hardware requirements for V5 Babylm 25K Lower Spa Ell Sequential Interleaved?

V5 Babylm 25K Lower Spa Ell Sequential Interleaved requires approximately 0.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate V5 Babylm 25K Lower Spa Ell Sequential Interleaved?

V5 Babylm 25K Lower Spa Ell Sequential Interleaved is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

V5 Babylm 25K Lower Spa Ell Sequential Interleaved by nikitastheo — VRAM 0.5GB

Name: V5 Babylm 25K Lower Spa Ell Sequential Interleaved
Author: nikitastheo

V5 Babylm 25K Lower Spa Ell Sequential Interleaved is an open-source language model by nikitastheo. Features: 123.9m LLM, VRAM: 0.5GB, LLM Explorer Score: 0.31.

Endpoints compatible Gpt2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/nikitastheo/v5-babylm-25k-lower-spa-ell-sequential_interleaved

V5 Babylm 25K Lower Spa Ell Sequential Interleaved Parameters and Internals

LLM Name	V5 Babylm 25K Lower Spa Ell Sequential Interleaved
Repository 🤗	https://huggingface.co/nikitastheo/v5-babylm-25k-lower-spa-ell-sequential_interleaved
Model Size	123.9m
Required VRAM	0.5 GB
Updated	2026-09-19
Maintainer	nikitastheo
Model Type	gpt2
Model Files	0.5 GB
Model Architecture	GPT2LMHeadModel
Transformers Version	4.57.6
Vocabulary Size	50048
Activation Function	gelu

Rank the V5 Babylm 25K Lower Spa Ell Sequential Interleaved 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 V5 Babylm 25K Lower Spa Ell Sequential Interleaved

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...Srp Ell Sequential Interleaved	0K / 0.5 GB	309	1

Note: green Score (e.g. "73.2") means that the model is better than nikitastheo/v5-babylm-25k-lower-spa-ell-sequential_interleaved.