Instructions to use Fabcc/french-bge-m3-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fabcc/french-bge-m3-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Fabcc/french-bge-m3-gguf",
	filename="french-bge-m3-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Fabcc/french-bge-m3-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Fabcc/french-bge-m3-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fabcc/french-bge-m3-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Fabcc/french-bge-m3-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fabcc/french-bge-m3-gguf:Q4_K_M

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 Fabcc/french-bge-m3-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Fabcc/french-bge-m3-gguf:Q4_K_M

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 Fabcc/french-bge-m3-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Fabcc/french-bge-m3-gguf:Q4_K_M

Use Docker

docker model run hf.co/Fabcc/french-bge-m3-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use Fabcc/french-bge-m3-gguf with Ollama:
```
ollama run hf.co/Fabcc/french-bge-m3-gguf:Q4_K_M
```

Unsloth Studio

How to use Fabcc/french-bge-m3-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 Fabcc/french-bge-m3-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 Fabcc/french-bge-m3-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Fabcc/french-bge-m3-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use Fabcc/french-bge-m3-gguf with Docker Model Runner:
```
docker model run hf.co/Fabcc/french-bge-m3-gguf:Q4_K_M
```

Lemonade

How to use Fabcc/french-bge-m3-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Fabcc/french-bge-m3-gguf:Q4_K_M

Run and chat with the model

lemonade run user.french-bge-m3-gguf-Q4_K_M

List all available models

lemonade list

French BGE-M3 - GGUF

This repository contains GGUF quantizations for antoinelouis/french-bge-m3, a French-pruned and optimized version of BAAI's bge-m3 model.

Overview

Original Architecture: BAAI/bge-m3
French Pruned Version: antoinelouis/french-bge-m3
Embedding Dimensions: 1024
Context Length: 8,192 tokens
Vocabulary Size: 37,200 tokens (pruned for French & Latin languages)

Files Included

Filename	Quantization	Size	Recommended Use Case
`french-bge-m3-f16.gguf`	F16	~700 MB	Full precision baseline
`french-bge-m3-q8_0.gguf`	Q8_0	~360 MB	Recommended (99.9% quality, small footprint)
`french-bge-m3-q5_k_m.gguf`	Q5_K_M	~260 MB	Medium quantization
`french-bge-m3-q4_k_m.gguf`	Q4_K_M	~220 MB	Ultra lightweight

Quick Start with llama.cpp

Local file execution:

llama-server -m french-bge-m3-q8_0.gguf --embedding --port 8080 -ngl 99

Direct loading via Hugging Face (`-hf`):

llama-server -hf "fabcc/french-bge-m3-GGUF:french-bge-m3-q8_0.gguf" --embedding --port 8080 -ngl 99

Test with OpenAI API Client (curl)

curl http://127.0.0.1:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Ceci est un document de test en français.",
    "model": "french-bge-m3"
  }'

Credits & Attributions

French Pruning & Optimization: Antoine Louis (antoinelouis/french-bge-m3)
Original Model & Architecture: BAAI (BAAI/bge-m3)
GGUF Conversion & Quantization: fabcc

Downloads last month: 162

GGUF

Model size

0.3B params

Architecture

bert

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for Fabcc/french-bge-m3-gguf

Base model

BAAI/bge-m3

Quantized

antoinelouis/french-bge-m3