CUDA VS Hugging Face

For my fellow Windows shills, here's how you actually build it on windows: Before steps: 1. (For Nvidia GPU users) Install cuda toolkit https://developer.nvidia.com/cuda-downloads 2. Download the model somewhere: https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/llama-2-13b-chat.ggmlv3.q4_0.bin In Windows Terminal with Powershell:

    git clone https://github.com/ggerganov/llama.cpp.

- Source: Hacker News / 10 months ago

I use Ubuntu and configuring nvidia drivers is very easy installing from here https://developer.nvidia.com/cuda-downloads. Source: 10 months ago

You have posted almost no information about your Hardware and what exactly you have done. Do you actually have NVIDIA? Have you actually installed CUDA? Also when exactly do you get the error, while installed the python package or later? Source: 11 months ago

EDIT: LINK TO CUDA-toolkit: https://developer.nvidia.com/cuda-downloads. Source: 11 months ago

It's worth noting that you'll need a recent release of llama.cpp to run GGML models with GPU acceleration here is the latest build for CUDA 12.1), and you'll need to install a recent CUDA version if you haven't already (here is the CUDA 12.1 toolkit installer -- mind, it's over 3 GB). Source: 12 months ago

HuggingFaceEmbeddings is a function that we use for converting our documents to vector which is called embedding, you can use any embedding model from huggingface, it will load the model on your local computer and create embeddings(you can use external api/service to create embeddings), then we just pass this to context and create index and store them into folder so we can reuse them and don't need to recalculate it. - Source: dev.to / 22 days ago

The only requirement for this tutorial is to have an Hugging Face account. In order to get it:. - Source: dev.to / 28 days ago

Finally, you'll need to download a compatible language model and copy it to the ~/llama.cpp/models directory. Head over to Hugging Face and search for a GGUF-formatted model that fits within your device's available RAM. I'd recommend starting with TinyLlama-1.1B. - Source: dev.to / about 1 month ago

At this point, probably everyone has heard about OpenAI, GPT-4, Claude or any of the popular Large Language Models (LLMs). However, using these LLMs in a production environment can be expensive or nondeterministic regarding its results. I guess that is the downside of being good at everything; you could be better at performing one specific task. This is where HuggingFace can utilized. HuggingFace provides... - Source: dev.to / about 1 month ago

New models can be added by downloading GGUF format models to the models sub-directory from https://huggingface.co/. - Source: dev.to / about 2 months ago