Instructions to use nomic-ai/nomic-embed-code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use nomic-ai/nomic-embed-code-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="nomic-ai/nomic-embed-code-GGUF", filename="nomic-embed-code.Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use nomic-ai/nomic-embed-code-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf nomic-ai/nomic-embed-code-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf nomic-ai/nomic-embed-code-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf nomic-ai/nomic-embed-code-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf nomic-ai/nomic-embed-code-GGUF:Q4_K_M
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 nomic-ai/nomic-embed-code-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf nomic-ai/nomic-embed-code-GGUF:Q4_K_M
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 nomic-ai/nomic-embed-code-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf nomic-ai/nomic-embed-code-GGUF:Q4_K_M
Use Docker
docker model run hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use nomic-ai/nomic-embed-code-GGUF with Ollama:
ollama run hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M
- Unsloth Studio new
How to use nomic-ai/nomic-embed-code-GGUF 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 nomic-ai/nomic-embed-code-GGUF 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 nomic-ai/nomic-embed-code-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nomic-ai/nomic-embed-code-GGUF to start chatting
- Docker Model Runner
How to use nomic-ai/nomic-embed-code-GGUF with Docker Model Runner:
docker model run hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M
- Lemonade
How to use nomic-ai/nomic-embed-code-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nomic-ai/nomic-embed-code-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.nomic-embed-code-GGUF-Q4_K_M
List all available models
lemonade list
Unable to use these with llama.cpp
{'error': {'message': 'Llama model must be created with embedding=True to call this method', 'type': 'internal_server_error', 'param': None, 'code': None}}
First off, thank you for uploading these! Is this intended? I don't seem to be able to use these GGUF with llama.cpp
I've tried Q_4 and Q_8 variants. Thanks.
It seems like you are using the llama-cpp-python server. This model will not be supported by it until they update their llama.cpp dependency, as the version of llama.cpp they are currently using does not read the pooling type from this model, and there is no argument to the server in order to specify it. If you would like this model to be supported by llama-cpp-python, please open an issue there. (At a minimum you will need --embedding true, but that alone will not give you the expected results.)
Please consider using the official llama.cpp server from here: https://github.com/ggml-org/llama.cpp/releases