multilingual-e5-large:fp32

embedding

ollama pull home-rag/multilingual-e5-large:fp32

curl http://localhost:11434/api/embed \
  -d '{
    "model": "home-rag/multilingual-e5-large:fp32",
    "input": "Why is the sky blue?"
  }'

import ollama

response = ollama.embed(
    model='home-rag/multilingual-e5-large:fp32',
    input='The sky is blue because of Rayleigh scattering',
)
print(response.embeddings)

import ollama from 'ollama'

const response = await ollama.embed({
  model: 'home-rag/multilingual-e5-large:fp32',
  input: 'The sky is blue because of Rayleigh scattering',
})
console.log(response.embeddings)

Models

View all →

Name

1 model

Size / Usage

Context

Input

multilingual-e5-large:fp32

2.2GB · 512 context window · Text · 3 days ago

multilingual-e5-large:fp32

2.2GB

512

Text

Readme

Multilingual E5 Large

ARCH: BERT // PARAMS: 558.8M // PRECISION: FP32 // CAPABILITY: EMBEDDING

Hardware & Environment

Designed for local multi-GPU systems and small GPU grids.

Target hardware: AMD Instinct MI50, MI60, MI100 and MI210 series
Recommended memory: 4+ GB HBM / VRAM

Technical Specifications

Model: Multilingual E5 Large
Origin: Hugging Face intfloat/multilingual-e5-large
Precision: FP32
Context: 512 tokens
Embedding Length: 1024 dimensions

Quick Start

ollama run home-rag/multilingual-e5-large:fp32 "your text here"