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cstr
/
all-mpnet-base-v2-GGUF

Feature Extraction
GGUF
English
embeddings
ggml
text-embeddings
bert
crispembed
Model card Files Files and versions
xet
Community

Instructions to use cstr/all-mpnet-base-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use cstr/all-mpnet-base-v2-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 cstr/all-mpnet-base-v2-GGUF:IQ4_XS
    # Run inference directly in the terminal:
    llama cli -hf cstr/all-mpnet-base-v2-GGUF:IQ4_XS
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf cstr/all-mpnet-base-v2-GGUF:IQ4_XS
    # Run inference directly in the terminal:
    llama cli -hf cstr/all-mpnet-base-v2-GGUF:IQ4_XS
    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 cstr/all-mpnet-base-v2-GGUF:IQ4_XS
    # Run inference directly in the terminal:
    ./llama-cli -hf cstr/all-mpnet-base-v2-GGUF:IQ4_XS
    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 cstr/all-mpnet-base-v2-GGUF:IQ4_XS
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf cstr/all-mpnet-base-v2-GGUF:IQ4_XS
    Use Docker
    docker model run hf.co/cstr/all-mpnet-base-v2-GGUF:IQ4_XS
  • LM Studio
  • Jan
  • Ollama

    How to use cstr/all-mpnet-base-v2-GGUF with Ollama:

    ollama run hf.co/cstr/all-mpnet-base-v2-GGUF:IQ4_XS
  • Unsloth Studio

    How to use cstr/all-mpnet-base-v2-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 cstr/all-mpnet-base-v2-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 cstr/all-mpnet-base-v2-GGUF to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for cstr/all-mpnet-base-v2-GGUF to start chatting
  • Atomic Chat new
  • Docker Model Runner

    How to use cstr/all-mpnet-base-v2-GGUF with Docker Model Runner:

    docker model run hf.co/cstr/all-mpnet-base-v2-GGUF:IQ4_XS
  • Lemonade

    How to use cstr/all-mpnet-base-v2-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull cstr/all-mpnet-base-v2-GGUF:IQ4_XS
    Run and chat with the model
    lemonade run user.all-mpnet-base-v2-GGUF-IQ4_XS
    List all available models
    lemonade list
all-mpnet-base-v2-GGUF
773 MB
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  • 1 contributor
History: 12 commits
cstr's picture
cstr
importance matrix (calibration)
91d24d5 verified about 1 month ago
  • .gitattributes
    1.91 kB
    importance matrix (calibration) about 1 month ago
  • README.md
    2.61 kB
    Add model card for all-mpnet-base-v2 GGUF 4 months ago
  • all-mpnet-base-v2-imatrix-ab.txt
    411 Bytes
    A/B summary (cos vs gold) about 1 month ago
  • all-mpnet-base-v2-iq4_xs.gguf
    71.6 MB
    xet
    iq4_xs +imatrix (cos_vs_f16=0.9810) about 1 month ago
  • all-mpnet-base-v2-q4_k-imatrix.gguf
    74.2 MB
    xet
    q4_k +imatrix (cos_vs_f16=0.9817) about 1 month ago
  • all-mpnet-base-v2-q4_k.gguf
    74.2 MB
    xet
    v2: Ollama format with relative attention bias 3 months ago
  • all-mpnet-base-v2-q8_0.gguf
    117 MB
    xet
    v2: Ollama format with relative attention bias 3 months ago
  • all-mpnet-base-v2.gguf
    436 MB
    xet
    v2: Ollama format with relative attention bias 3 months ago
  • all-mpnet-base-v2.imatrix
    263 kB
    xet
    importance matrix (calibration) about 1 month ago