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

How to use SciTools/embedding 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 SciTools/embedding:F16
# Run inference directly in the terminal:
llama cli -hf SciTools/embedding:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SciTools/embedding:F16
# Run inference directly in the terminal:
llama cli -hf SciTools/embedding:F16

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 SciTools/embedding:F16
# Run inference directly in the terminal:
./llama-cli -hf SciTools/embedding:F16

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 SciTools/embedding:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SciTools/embedding:F16

Use Docker

docker model run hf.co/SciTools/embedding:F16

LM Studio
Jan
Ollama
How to use SciTools/embedding with Ollama:
```
ollama run hf.co/SciTools/embedding:F16
```

Unsloth Studio

How to use SciTools/embedding 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 SciTools/embedding 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 SciTools/embedding to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SciTools/embedding to start chatting

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.gguf — 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.json — 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.

Downloads last month: 16

GGUF

Model size

33.2M params

Architecture

bert

Hardware compatibility

16-bit

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