Obtaining and quantizing models

The Hugging Face platform hosts thousands of models compatible with llama.cpp:

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You can use any llama.cpp-compatible model from Hugging Face using this CLI argument: -hf <user>/<model>[:quant]. For example:

llama cli -hf ggml-org/gemma-3-1b-it-GGUF

You can use the same CLI invocation to download from other sites, by pointing the MODEL_ENDPOINT environment variable to an endpoint compatible with the Hugging Face API. llama.cpp can also run models you have downloaded locally to your filesystem.

After downloading a model, use the CLI tools to run it locally - see below.

llama.cpp requires the model to be stored in the GGUF file format. Models in other data formats can be converted to GGUF using the convert_*.py Python scripts in this repo. To learn more about model quantization, read this documentation

The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp:

Use the GGUF-my-repo space to convert to GGUF format and quantize model weights to smaller sizes
Use the GGUF-my-LoRA space to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123)
Use the GGUF-editor space to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
Use the Inference Endpoints to directly host llama.cpp in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)