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second-state
/
All-MiniLM-L6-v2-Embedding-GGUF

Feature Extraction
sentence-transformers
GGUF
Transformers
English
bert
sentence-similarity
Model card Files Files and versions
xet
Community
1

Instructions to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("second-state/All-MiniLM-L6-v2-Embedding-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 second-state/All-MiniLM-L6-v2-Embedding-GGUF with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="second-state/All-MiniLM-L6-v2-Embedding-GGUF")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("second-state/All-MiniLM-L6-v2-Embedding-GGUF")
    model = AutoModel.from_pretrained("second-state/All-MiniLM-L6-v2-Embedding-GGUF")
  • llama-cpp-python

    How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="second-state/All-MiniLM-L6-v2-Embedding-GGUF",
    	filename="all-MiniLM-L6-v2-Q2_K.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 second-state/All-MiniLM-L6-v2-Embedding-GGUF with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama-cli -hf second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama-cli -hf second-state/All-MiniLM-L6-v2-Embedding-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 second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf second-state/All-MiniLM-L6-v2-Embedding-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 second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
    Use Docker
    docker model run hf.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
  • LM Studio
  • Jan
  • Ollama

    How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with Ollama:

    ollama run hf.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
  • Unsloth Studio new

    How to use second-state/All-MiniLM-L6-v2-Embedding-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 second-state/All-MiniLM-L6-v2-Embedding-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 second-state/All-MiniLM-L6-v2-Embedding-GGUF to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for second-state/All-MiniLM-L6-v2-Embedding-GGUF to start chatting
  • Docker Model Runner

    How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with Docker Model Runner:

    docker model run hf.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
  • Lemonade

    How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
    Run and chat with the model
    lemonade run user.All-MiniLM-L6-v2-Embedding-GGUF-Q4_K_M
    List all available models
    lemonade list
All-MiniLM-L6-v2-Embedding-GGUF
300 MB
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  • 3 contributors
History: 13 commits
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juntaoyuan
Update README.md
544f204 verified about 2 years ago
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