- Libraries
- sentence-transformers
How to use Impulse2000/multilingual-e5-large-instruct-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Impulse2000/multilingual-e5-large-instruct-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 Impulse2000/multilingual-e5-large-instruct-GGUF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="Impulse2000/multilingual-e5-large-instruct-GGUF") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Impulse2000/multilingual-e5-large-instruct-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Impulse2000/multilingual-e5-large-instruct-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 Impulse2000/multilingual-e5-large-instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Impulse2000/multilingual-e5-large-instruct-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Impulse2000/multilingual-e5-large-instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Impulse2000/multilingual-e5-large-instruct-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 Impulse2000/multilingual-e5-large-instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Impulse2000/multilingual-e5-large-instruct-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 Impulse2000/multilingual-e5-large-instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Impulse2000/multilingual-e5-large-instruct-GGUF:F16
Use Docker
docker model run hf.co/Impulse2000/multilingual-e5-large-instruct-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use Impulse2000/multilingual-e5-large-instruct-GGUF with Ollama:
ollama run hf.co/Impulse2000/multilingual-e5-large-instruct-GGUF:F16
- Unsloth Studio
How to use Impulse2000/multilingual-e5-large-instruct-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 Impulse2000/multilingual-e5-large-instruct-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 Impulse2000/multilingual-e5-large-instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Impulse2000/multilingual-e5-large-instruct-GGUF to start chatting
- Docker Model Runner
How to use Impulse2000/multilingual-e5-large-instruct-GGUF with Docker Model Runner:
docker model run hf.co/Impulse2000/multilingual-e5-large-instruct-GGUF:F16
- Lemonade
How to use Impulse2000/multilingual-e5-large-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull Impulse2000/multilingual-e5-large-instruct-GGUF:F16
Run and chat with the model
lemonade run user.multilingual-e5-large-instruct-GGUF-F16
List all available models
lemonade list
- Atomic Chat
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Impulse2000/multilingual-e5-large-instruct-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]