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tensorblock
/
gte-Qwen2-1.5B-instruct-GGUF

Sentence Similarity
sentence-transformers
GGUF
Transformers
mteb
Qwen2
TensorBlock
GGUF
Eval Results (legacy)
conversational
Model card Files Files and versions
xet
Community

Instructions to use tensorblock/gte-Qwen2-1.5B-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use tensorblock/gte-Qwen2-1.5B-instruct-GGUF with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("tensorblock/gte-Qwen2-1.5B-instruct-GGUF")
    
    sentences = [
        "That is a happy person",
        "That is a happy dog",
        "That is a very happy person",
        "Today is a sunny day"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Transformers

    How to use tensorblock/gte-Qwen2-1.5B-instruct-GGUF with Transformers:

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("tensorblock/gte-Qwen2-1.5B-instruct-GGUF", dtype="auto")
  • llama-cpp-python

    How to use tensorblock/gte-Qwen2-1.5B-instruct-GGUF with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="tensorblock/gte-Qwen2-1.5B-instruct-GGUF",
    	filename="gte-Qwen2-1.5B-instruct-Q2_K.gguf",
    )
    
    llm.create_chat_completion(
    	messages = "{\n    \"source_sentence\": \"That is a happy person\",\n    \"sentences\": [\n        \"That is a happy dog\",\n        \"That is a very happy person\",\n        \"Today is a sunny day\"\n    ]\n}"
    )
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use tensorblock/gte-Qwen2-1.5B-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 tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
    # Run inference directly in the terminal:
    llama-cli -hf tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
    # Run inference directly in the terminal:
    llama-cli -hf tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
    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 tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
    # Run inference directly in the terminal:
    ./llama-cli -hf tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
    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 tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
    Use Docker
    docker model run hf.co/tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
  • LM Studio
  • Jan
  • Ollama

    How to use tensorblock/gte-Qwen2-1.5B-instruct-GGUF with Ollama:

    ollama run hf.co/tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
  • Unsloth Studio new

    How to use tensorblock/gte-Qwen2-1.5B-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 tensorblock/gte-Qwen2-1.5B-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 tensorblock/gte-Qwen2-1.5B-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 tensorblock/gte-Qwen2-1.5B-instruct-GGUF to start chatting
  • Docker Model Runner

    How to use tensorblock/gte-Qwen2-1.5B-instruct-GGUF with Docker Model Runner:

    docker model run hf.co/tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
  • Lemonade

    How to use tensorblock/gte-Qwen2-1.5B-instruct-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull tensorblock/gte-Qwen2-1.5B-instruct-GGUF:Q2_K
    Run and chat with the model
    lemonade run user.gte-Qwen2-1.5B-instruct-GGUF-Q2_K
    List all available models
    lemonade list
gte-Qwen2-1.5B-instruct-GGUF
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  • 1 contributor
History: 7 commits
morriszms's picture
morriszms
Keep Q2_K/Q3_K_M gguf only
bc3b329 verified 4 months ago
  • .gitattributes
    2.37 kB
    Upload folder using huggingface_hub over 1 year ago
  • README.md
    141 kB
    Update README.md 11 months ago
  • gte-Qwen2-1.5B-instruct-Q2_K.gguf
    752 MB
    xet
    Upload folder using huggingface_hub over 1 year ago
  • gte-Qwen2-1.5B-instruct-Q3_K_M.gguf
    924 MB
    xet
    Upload folder using huggingface_hub over 1 year ago