How to use microsoft/bitnet-embedding-270m with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/bitnet-embedding-270m", device_map="auto")
How to use microsoft/bitnet-embedding-270m with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf microsoft/bitnet-embedding-270m:BF16 --embeddings
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf microsoft/bitnet-embedding-270m:BF16 --embeddings
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible embeddings server: ./llama-server -hf microsoft/bitnet-embedding-270m:BF16 --embeddings
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 microsoft/bitnet-embedding-270m:BF16 --embeddings
How to use microsoft/bitnet-embedding-270m with Ollama:
ollama run hf.co/microsoft/bitnet-embedding-270m:BF16
How to use microsoft/bitnet-embedding-270m with Docker Model Runner:
docker model run hf.co/microsoft/bitnet-embedding-270m:BF16
How to use microsoft/bitnet-embedding-270m with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull microsoft/bitnet-embedding-270m:BF16
lemonade run user.bitnet-embedding-270m-BF16
lemonade list
The community tab is the place to discuss and collaborate with the HF community!
