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Impulse2000
/
multilingual-e5-large-instruct-GGUF

Feature Extraction
sentence-transformers
GGUF
Transformers
mteb
llama-cpp
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use Impulse2000/multilingual-e5-large-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • 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", dtype="auto")
  • llama-cpp-python

    How to use Impulse2000/multilingual-e5-large-instruct-GGUF with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="Impulse2000/multilingual-e5-large-instruct-GGUF",
    	filename="multilingual-e5-large-instruct-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 Impulse2000/multilingual-e5-large-instruct-GGUF with llama.cpp:

    Install from brew
    brew install llama.cpp
    # 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
    Install from WinGet (Windows)
    winget install llama.cpp
    # 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
    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 new

    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
multilingual-e5-large-instruct-GGUF
1.73 GB
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  • 1 contributor
History: 4 commits
Impulse2000's picture
Impulse2000
Update README.md
453e62a verified over 1 year ago
  • .gitattributes
    1.56 kB
    Added gguf files over 1 year ago
  • README.md
    133 kB
    Update README.md over 1 year ago
  • multilingual-e5-large-instruct-f16.gguf
    1.13 GB
    xet
    Added gguf files over 1 year ago
  • multilingual-e5-large-instruct-q8_0.gguf
    603 MB
    xet
    Added gguf files over 1 year ago