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ewin-reg
/
WeMM-Embedding-2B-GGUF

Feature Extraction
GGUF
sentence-transformers
English
Chinese
qwen3_5
llama-cpp
unsloth
text-embeddings
multimodal-embedding
code-search
flatquant
schurscale
quantized
q4_k_m
q5_k_m
q6_k
q8_0
int4
int8
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use ewin-reg/WeMM-Embedding-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use ewin-reg/WeMM-Embedding-2B-GGUF with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("ewin-reg/WeMM-Embedding-2B-GGUF", trust_remote_code=True)
    
    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]
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use ewin-reg/WeMM-Embedding-2B-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 ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf ewin-reg/WeMM-Embedding-2B-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 ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf ewin-reg/WeMM-Embedding-2B-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 ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
    Use Docker
    docker model run hf.co/ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
  • LM Studio
  • Jan
  • Ollama

    How to use ewin-reg/WeMM-Embedding-2B-GGUF with Ollama:

    ollama run hf.co/ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
  • Unsloth Desktop
  • Docker Model Runner

    How to use ewin-reg/WeMM-Embedding-2B-GGUF with Docker Model Runner:

    docker model run hf.co/ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
  • Lemonade

    How to use ewin-reg/WeMM-Embedding-2B-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull ewin-reg/WeMM-Embedding-2B-GGUF:Q4_K_M
    Run and chat with the model
    lemonade run user.WeMM-Embedding-2B-GGUF-Q4_K_M
    List all available models
    lemonade list
  • Atomic Chat
WeMM-Embedding-2B-GGUF
21 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 29 commits
ewin-reg's picture
ewin-reg
docs: synchronize empirical benchmarks and vision encoder architecture notes
a219b89 verified 13 days ago
  • gguf
    feat: add GGUF Q6_K quantized embedding weights 17 days ago
  • pytorch
    feat: add PyTorch INT8 dynamic quantized safetensors 17 days ago
  • research
    feat: publish 2026 SchurScale-Global Q4_K_M GGUF 17 days ago
  • .gitattributes
    2.01 kB
    feat: add GGUF Q6_K quantized embedding weights 17 days ago
  • README.md
    18.3 kB
    docs: synchronize empirical benchmarks and vision encoder architecture notes 13 days ago
  • config.json
    2.85 kB
    feat: sync config.json configuration 17 days ago
  • modules.json
    106 Bytes
    feat: sync modules.json configuration 17 days ago
  • sentence_bert_config.json
    950 Bytes
    feat: sync sentence_bert_config.json configuration 17 days ago
  • tokenizer.json
    20 MB
    xet
    feat: sync tokenizer.json configuration 17 days ago
  • tokenizer_config.json
    1.15 kB
    feat: sync tokenizer_config.json configuration 17 days ago