Hugging Face mentions (254)

• Add Vector Search to Your Site

  We will use the OpenAI embeddings API to convert the content of the blog posts into vector embeddings. You will need to sign up for an API key on the OpenAI website to use the API. You will need to provide your credit card information as there is a cost associated with using the API. You can review the pricing on the OpenAI website. There are alternatives to generate embeddings. Hugging Face provides... - Source: dev.to / 5 days ago

• How to run any LLM model with Hugging-Face 🤗

  Hugging-face 🤗 is a repository to host all the LLM models available in the world. https://huggingface.co/. - Source: dev.to / 12 days ago

• Chat with your Github Repo using llama_index and chainlit

  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 / about 1 month ago

• AI enthusiasm - episode #1🚀

  The only requirement for this tutorial is to have an Hugging Face account. In order to get it:. - Source: dev.to / about 2 months ago

• Hosting Your Own AI Chatbot on Android Devices

  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 2 months ago

Lexbe eDiscovery Platform mentions (0)