Libraries Transformers How to use microsoft/bitnet-embedding-0.6b with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("microsoft/bitnet-embedding-0.6b", dtype="auto") llama-cpp-python How to use microsoft/bitnet-embedding-0.6b with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="microsoft/bitnet-embedding-0.6b",
filename="bitnet-embeddings-0.6b-bf16-i2_s.gguf",
)
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output) Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use microsoft/bitnet-embedding-0.6b 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 microsoft/bitnet-embedding-0.6b:BF16
# Run inference directly in the terminal:
llama cli -hf microsoft/bitnet-embedding-0.6b:BF16 Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf microsoft/bitnet-embedding-0.6b:BF16
# Run inference directly in the terminal:
llama cli -hf microsoft/bitnet-embedding-0.6b:BF16 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 microsoft/bitnet-embedding-0.6b:BF16
# Run inference directly in the terminal:
./llama-cli -hf microsoft/bitnet-embedding-0.6b:BF16 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 microsoft/bitnet-embedding-0.6b:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf microsoft/bitnet-embedding-0.6b:BF16 Use Docker docker model run hf.co/microsoft/bitnet-embedding-0.6b:BF16 LM Studio Jan Ollama How to use microsoft/bitnet-embedding-0.6b with Ollama:
ollama run hf.co/microsoft/bitnet-embedding-0.6b:BF16 Unsloth Studio How to use microsoft/bitnet-embedding-0.6b 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 microsoft/bitnet-embedding-0.6b 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 microsoft/bitnet-embedding-0.6b to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for microsoft/bitnet-embedding-0.6b to start chatting Atomic Chat new Docker Model Runner How to use microsoft/bitnet-embedding-0.6b with Docker Model Runner:
docker model run hf.co/microsoft/bitnet-embedding-0.6b:BF16 Lemonade How to use microsoft/bitnet-embedding-0.6b with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull microsoft/bitnet-embedding-0.6b:BF16 Run and chat with the model lemonade run user.bitnet-embedding-0.6b-BF16 List all available models lemonade list 
