all MiniLM L6 v2

ProviderXenova
Categoryfeature-extraction
Licenseapache-2.0
Downloads2.8K
Stars0

Overview

all-MiniLM-L6-v2 is a lightweight, efficient sentence-transformer model designed for mapping text to a 384-dimensional dense vector space. For developers, its primary value lies in its high throughput and low latency, making it an ideal choice for real-time semantic search, clustering, and sentence similarity tasks where deploying a massive LLM would be overkill. It balances performance and footprint effectively, allowing for easy integration into edge devices or serverless functions without heavy GPU overhead. Compared to larger BERT-based models, it offers a significant speedup while maintaining competitive accuracy for general-purpose embedding tasks, particularly when building RAG pipelines or vector databases.

Highlights

  • High-speed semantic embeddings for real-time search
  • Low memory footprint ideal for edge deployment
  • Optimized for sentence similarity and clustering
  • Seamless integration with vector databases and RAG
  • Apache-2.0 license for flexible commercial use

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Xenova/all-MiniLM-L6-v2")
tokenizer = AutoTokenizer.from_pretrained("Xenova/all-MiniLM-L6-v2")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download Xenova/all-MiniLM-L6-v2

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download Xenova/all-MiniLM-L6-v2 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Xenova/all-MiniLM-L6-v2')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/Xenova/all-MiniLM-L6-v2

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Xenova/all-MiniLM-L6-v2

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Xenova/all-MiniLM-L6-v2')
tokenizer = AutoTokenizer.from_pretrained('Xenova/all-MiniLM-L6-v2')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model Xenova/all-MiniLM-L6-v2

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model Xenova/all-MiniLM-L6-v2 README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Xenova/all-MiniLM-L6-v2')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/Xenova/all-MiniLM-L6-v2.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Xenova/all-MiniLM-L6-v2.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'Xenova/all-MiniLM-L6-v2')

Full Documentation

来源: HuggingFace

---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: transformers.js
license: apache-2.0
---

https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 with ONNX weights to be compatible with Transformers.js.

Usage (Transformers.js)

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

bash
npm i @huggingface/transformers

You can then use the model to compute embeddings like this:

js
import { pipeline } from '@huggingface/transformers';

// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');


// Compute sentence embeddings
const sentences = ['This is an example sentence', 'Each sentence is converted'];
const output = await extractor(sentences, { pooling: 'mean', normalize: true });
console.log(output);
// Tensor {
//   dims: [ 2, 384 ],
//   type: 'float32',
//   data: Float32Array(768) [ 0.04592696577310562, 0.07328180968761444, ... ],
//   size: 768
// }

You can convert this Tensor to a nested JavaScript array using .tolist():

js
console.log(output.tolist());
// [
//   [ 0.04592696577310562, 0.07328180968761444, 0.05400655046105385, ... ],
//   [ 0.08188057690858841, 0.10760223120450974, -0.013241755776107311, ... ]
// ]

Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

View official source
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