Instructions to use pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("pyarn/bge-reranker-v2-m3-Q5_K_M-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]

Transformers

How to use pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pyarn/bge-reranker-v2-m3-Q5_K_M-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 pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_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 pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_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 pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M

Use Docker

docker model run hf.co/pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M

LM Studio
Jan
Ollama
How to use pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF with Ollama:
```
ollama run hf.co/pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M
```

Unsloth Studio

How to use pyarn/bge-reranker-v2-m3-Q5_K_M-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 pyarn/bge-reranker-v2-m3-Q5_K_M-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 pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF to start chatting

Docker Model Runner
How to use pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M
```

Lemonade

How to use pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.bge-reranker-v2-m3-Q5_K_M-GGUF-Q5_K_M

List all available models

lemonade list

Atomic Chat

Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`

by tomaarsen HF Staff - opened Apr 2, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

tomaarsen

Apr 2, 2025

Hello!

Pull Request overview

Update metadata to set pipeline tag to the new text-ranking
Update metadata to set library name to sentence-transformers

Changes

This is an automated pull request to update the metadata of the model card. We recently introduced the text-ranking pipeline tag for models that are used for ranking tasks, and we have a suspicion that this model is one of them. I also updated added metadata to specify that this model can be loaded with the sentence-transformers library, as it should be possible to load any model compatible with transformers AutoModelForSequenceClassification.

Feel free to verify that it works with the following:

pip install sentence-transformers

from sentence_transformers import CrossEncoder

model = CrossEncoder("pyarn/bge-reranker-v2-m3-Q5_K_M-GGUF")
scores = model.predict([
    ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
    ("How many people live in Berlin?", "Berlin is well known for its museums."),
])
print(scores)

Feel free to respond if you have questions or concerns.

Tom Aarsen

Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`432df5f4

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