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
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible embeddings server: ./llama-server -hf SciTools/embedding:F16 --embeddings
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 embeddings server: ./build/bin/llama-server -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
A small English embedding model by BAAI (Beijing Academy of Artificial Intelligence).
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.
bge-small-en-v1.5-f16.ggufβ GGUF (F16), served by ullama with--embeddings. Used by Understand 2026 and later. 384 dimensions, CLS pooling, 512-token context.bge-small-en-v1.5.onnx+bge-small-en-v1.5-tokenizer.jsonβ ONNX, used by the retired undaiserver. Kept for older releases.
The GGUF was converted from BAAI/bge-small-en-v1.5 with llama.cpp's
convert_hf_to_gguf.py; its vectors match the reference implementation
(cosine similarity 1.00000). Note the ONNX path used mean pooling while the
GGUF uses BGE's specified CLS pooling, so indexes built with one are not
comparable with the other.
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ollama run hf.co/SciTools/embedding:F16