Instructions to use MoMonir/SFR-Embedding-Mistral-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MoMonir/SFR-Embedding-Mistral-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("MoMonir/SFR-Embedding-Mistral-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 MoMonir/SFR-Embedding-Mistral-GGUF with Transformers:

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

pipe = pipeline("feature-extraction", model="MoMonir/SFR-Embedding-Mistral-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MoMonir/SFR-Embedding-Mistral-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MoMonir/SFR-Embedding-Mistral-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 MoMonir/SFR-Embedding-Mistral-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MoMonir/SFR-Embedding-Mistral-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 MoMonir/SFR-Embedding-Mistral-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MoMonir/SFR-Embedding-Mistral-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 MoMonir/SFR-Embedding-Mistral-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MoMonir/SFR-Embedding-Mistral-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 MoMonir/SFR-Embedding-Mistral-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MoMonir/SFR-Embedding-Mistral-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MoMonir/SFR-Embedding-Mistral-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use MoMonir/SFR-Embedding-Mistral-GGUF with Ollama:
```
ollama run hf.co/MoMonir/SFR-Embedding-Mistral-GGUF:Q4_K_M
```

Unsloth Studio

How to use MoMonir/SFR-Embedding-Mistral-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 MoMonir/SFR-Embedding-Mistral-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 MoMonir/SFR-Embedding-Mistral-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MoMonir/SFR-Embedding-Mistral-GGUF to start chatting

Docker Model Runner
How to use MoMonir/SFR-Embedding-Mistral-GGUF with Docker Model Runner:
```
docker model run hf.co/MoMonir/SFR-Embedding-Mistral-GGUF:Q4_K_M
```

Lemonade

How to use MoMonir/SFR-Embedding-Mistral-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MoMonir/SFR-Embedding-Mistral-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SFR-Embedding-Mistral-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

MoMonir/SFR-Embedding-Mistral-GGUF

This model was converted to GGUF format from Salesforce/SFR-Embedding-Mistral using llama.cpp
Refer to the original model card for more details on the model.

Note: This is an Embedding Model

For more information about Embedding check OpenAI Embedding Document

Downloads last month: 40

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

View +1 variant

MoMonir
/

SFR-Embedding-Mistral-GGUF

MoMonir/SFR-Embedding-Mistral-GGUF

Note: This is an Embedding Model

Spaces using MoMonir/SFR-Embedding-Mistral-GGUF 11