What are the hardware requirements for All MiniLM L12 V2?

All MiniLM L12 V2 requires a context window of 0.5K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed All MiniLM L12 V2 and how large is it?

All MiniLM L12 V2 is developed by Xenova. 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 All MiniLM L12 V2?

All MiniLM L12 V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

All MiniLM L12 V2 by Xenova — 0.5K context

Name: All MiniLM L12 V2
Author: Xenova

All MiniLM L12 V2 is an open-source language model by Xenova. Features: LLM, Context: 0.5K, LLM Explorer Score: 0.08.

Base model:quantized:sentence-... Base model:sentence-transforme... Bert Feature-extraction Onnx Region:us Transformers.js

Model Card on HF 🤗: https://huggingface.co/Xenova/all-MiniLM-L12-v2

All MiniLM L12 V2 Parameters and Internals

Model Type

feature-extraction

Additional Notes

The model is adapted to use ONNX weights compatible with Transformers.js for web applications. For web readiness, conversion to ONNX using Hugging Face Optimum is recommended, and maintaining weights in a subfolder named 'onnx'.

LLM Name	All MiniLM L12 V2
Repository 🤗	https://huggingface.co/Xenova/all-MiniLM-L12-v2
Base Model(s)	sentence-transformers/all-MiniLM-L12-v2 sentence-transformers/all-MiniLM-L12-v2
Updated	2026-07-18
Maintainer	Xenova
Model Type	bert
Model Architecture	AutoModel
Context Length	512
Model Max Length	512
Transformers Version	4.29.2
Tokenizer Class	BertTokenizer
Padding Token	[PAD]
Vocabulary Size	30522

Rank the All MiniLM L12 V2 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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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Tiny Random Detr	1K / 0.2 GB	15	0
Opengpt2 Pytorch Forward	1K / 6 GB	2	1
Finsent Transformer	0.5K / 0.4 GB	1	1
Bert Chinese L 12 H 768 A 12	0.5K / 0.4 GB	9	1
Simbert Chinese Base	0.5K / 0.4 GB	11	0
Simbert Chinese Tiny	0.5K / 0 GB	9	0
Bert Tiny	0.5K / 0 GB	1360936	147

Note: green Score (e.g. "73.2") means that the model is better than Xenova/all-MiniLM-L12-v2.