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I was extremely disappointed. Despite its claims of being a powerful writing tool, it failed to deliver on its promises. The suggestions it provided were often irrelevant and unhelpful, and the content it generated was frequently plagiarized and of poor quality. I would strongly advise against using this tool
I had high hopes for this tool, but it was a complete letdown. It's slow, unintuitive, and produces work that's riddled with errors. I found myself spending more time trying to fix the issues caused by the tool than actually writing
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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 / 12 days ago
The only requirement for this tutorial is to have an Hugging Face account. In order to get it:. - Source: dev.to / 18 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 / 24 days 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 / 24 days ago
New models can be added by downloading GGUF format models to the models sub-directory from https://huggingface.co/. - Source: dev.to / about 1 month ago
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