Instructions to use phate334/multilingual-e5-large-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use phate334/multilingual-e5-large-gguf with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("phate334/multilingual-e5-large-gguf")

sentences = [
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium."
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

llama-cpp-python

How to use phate334/multilingual-e5-large-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="phate334/multilingual-e5-large-gguf",
	filename="multilingual-e5-large-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use phate334/multilingual-e5-large-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf phate334/multilingual-e5-large-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf phate334/multilingual-e5-large-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf phate334/multilingual-e5-large-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf phate334/multilingual-e5-large-gguf:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf phate334/multilingual-e5-large-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf phate334/multilingual-e5-large-gguf:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf phate334/multilingual-e5-large-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf phate334/multilingual-e5-large-gguf:F16

Use Docker

docker model run hf.co/phate334/multilingual-e5-large-gguf:F16

LM Studio
Jan
Ollama
How to use phate334/multilingual-e5-large-gguf with Ollama:
```
ollama run hf.co/phate334/multilingual-e5-large-gguf:F16
```

Unsloth Studio new

How to use phate334/multilingual-e5-large-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for phate334/multilingual-e5-large-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for phate334/multilingual-e5-large-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for phate334/multilingual-e5-large-gguf to start chatting

Docker Model Runner
How to use phate334/multilingual-e5-large-gguf with Docker Model Runner:
```
docker model run hf.co/phate334/multilingual-e5-large-gguf:F16
```

Lemonade

How to use phate334/multilingual-e5-large-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull phate334/multilingual-e5-large-gguf:F16

Run and chat with the model

lemonade run user.multilingual-e5-large-gguf-F16

List all available models

lemonade list

phate334/multilingual-e5-large-gguf

This model was converted to GGUF format from intfloat/multilingual-e5-large using llama.cpp.

Run it

Deploy using Docker

$ docker run -p 8080:8080 -v ./multilingual-e5-large-q4_k_m.gguf:/multilingual-e5-large-q4_k_m.gguf ghcr.io/ggerganov/llama.cpp:server--b1-4b9afbb --host 0.0.0.0 --embedding -m /multilingual-e5-large-q4_k_m.gguf

or Docker Compose

services:
  e5-f16:
    image: ghcr.io/ggerganov/llama.cpp:server--b1-4b9afbb
    ports:
      - 8080:8080
    volumes:
      - ./multilingual-e5-large-f16.gguf:/multilingual-e5-large-f16.gguf
    command: --host 0.0.0.0 --embedding -m /multilingual-e5-large-f16.gguf
  e5-q4:
    image: ghcr.io/ggerganov/llama.cpp:server--b1-4b9afbb
    ports:
      - 8081:8080
    volumes:
      - ./multilingual-e5-large-q4_k_m.gguf:/multilingual-e5-large-q4_k_m.gguf
    command: --host 0.0.0.0 --embedding -m /multilingual-e5-large-q4_k_m.gguf

Downloads last month: 225

GGUF

Model size

0.6B params

Architecture

bert

Hardware compatibility

4-bit

16-bit

Model tree for phate334/multilingual-e5-large-gguf

Base model

intfloat/multilingual-e5-large

Quantized

(29)

this model