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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
Replika - Your Ai friend
Doodle - Make meetings happen. With Doodle, scheduling becomes quick and easy.
LangChain - Framework for building applications with LLMs through composability
Calendly - Say goodbye to phone and email tag for finding the perfect meeting time with Calendly. It's 100% free, super easy to use and you'll love our customer service.
Haystack NLP Framework - Haystack is an open source NLP framework to build applications with Transformer models and LLMs.
Clara - Clara is a virtual employee that schedules your meetings, getting you to the work that matters, faster.
