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Native models used by AnythingLLM

This folder is specifically created as a local cache and storage folder that is used for native models that can run on a CPU.

Currently, AnythingLLM uses this folder for the following parts of the application.

Embedding

When your embedding engine preference is native we will use the ONNX all-MiniLM-L6-v2 model built by Xenova on HuggingFace.co. This model is a quantized and WASM version of the popular all-MiniLM-L6-v2 which produces a 384-dimension vector.

If you are using the native embedding engine your vector database should be configured to accept 384-dimension models if that parameter is directly editable (Pinecone only).

Text generation (LLM selection)

Important

Use of a locally running LLM model is experimental and may behave unexpectedly, crash, or not function at all. We suggest for production-use of a local LLM model to use a purpose-built inference server like LocalAI or LMStudio.

Tip

We recommend at least using a 4-bit or 5-bit quantized model for your LLM. Lower quantization models tend to just output unreadable garbage.

If you would like to use a local Llama compatible LLM model for chatting you can select any model from this HuggingFace search filter

Requirements

  • Model must be in the latest GGUF format
  • Model should be compatible with latest llama.cpp
  • You should have the proper RAM to run such a model. Requirement depends on model size.

Where do I put my GGUF model?

Important

If running in Docker you should be running the container to a mounted storage location on the host machine so you can update the storage files directly without having to re-download or re-build your docker container. See suggested Docker config

All local models you want to have available for LLM selection should be placed in the storage/models/downloaded folder. Only .gguf files will be allowed to be selected from the UI.