Instructions to use SciTools/embedding 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 SciTools/embedding with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf SciTools/embedding:F16 --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf SciTools/embedding:F16 --embeddings
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible embeddings server: ./build/bin/llama serve -hf SciTools/embedding:F16 --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use SciTools/embedding with Ollama:
ollama run hf.co/SciTools/embedding:F16
- Unsloth Desktop
- Docker Model Runner
How to use SciTools/embedding with Docker Model Runner:
docker model run hf.co/SciTools/embedding:F16
- Lemonade
How to use SciTools/embedding with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SciTools/embedding:F16
Run and chat with the model
lemonade run user.embedding-F16
List all available models
lemonade list
- Atomic Chat
Put license front matter at the top so HF registers it
Browse files
README.md
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A small English embedding model by BAAI (Beijing Academy of Artificial Intelligence).
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This repository hosts the versions of the model used by Understand to generate embeddings for semantic search, allowing users to search their codebase by meaning rather than exact keyword matches.
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(cosine similarity 1.00000). Note the ONNX path used mean pooling while the
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license: mit
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A small English embedding model by BAAI (Beijing Academy of Artificial Intelligence).
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This repository hosts the versions of the model used by Understand to generate embeddings for semantic search, allowing users to search their codebase by meaning rather than exact keyword matches.
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(cosine similarity 1.00000). Note the ONNX path used mean pooling while the
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GGUF uses BGE's specified CLS pooling, so indexes built with one are not
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comparable with the other.
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