Dify.AI VS Hugging Face
Compare Dify.AI VS Hugging Face and see what are their differences
Employee Monitoring Software with Screenshots, Internet, Activity and Time Tracking
featured
Dify.AI
Vectara Neural Search
Haystack NLP Framework
Prompts
LangChain
2000 Large Language Models (LLM) Prompts
Langfuse
Boogie
Hugging Face
Replika
LangChain
Mitsuku
Haystack NLP Framework
Civitai
Kuki
Cleverbot.io
Hugging FaceNo Hugging Face videos yet. You could help us improve this page by suggesting one.
Based on our record, Hugging Face seems to be a lot more popular than Dify.AI. While we know about 253 links to Hugging Face, we've tracked only 3 mentions of Dify.AI. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Xorbits Inference (Xinference) is an open-source platform to streamline the operation and integration of a wide array of AI models. With Xinference, you’re empowered to run inference using any open-source LLMs, embedding models, and multimodal models either in the cloud or on your own premises, and create robust AI-driven applications. It provides a RESTful API compatible with OpenAI API, Python SDK, CLI, and... - Source: dev.to / 4 months ago
If you are looking to develop QnA or chat based apps then check out https://dify.ai. Do a quick check and see if it fit your requirements. You can integrate it with your app using the apis it provides. Source: 6 months ago
As an AI newbie, I used to find coding apps from scratch an absolute nightmare! The learning curve was steep as a ski slope, debugging took endless hours, and developing even a simple AI app nearly drove me insane! But since discovering Dify, it has totally revolutionized my life by enabling app development without any coding skills! Source: 9 months ago
Hugging-face 🤗 is a repository to host all the LLM models available in the world. https://huggingface.co/. - Source: dev.to / 5 days 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 / about 1 month ago
The only requirement for this tutorial is to have an Hugging Face account. In order to get it:. - Source: dev.to / about 1 month 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 2 months 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 2 months ago
Vectara Neural Search - Neural search as a service API with breakthrough relevance
Replika - Your Ai friend
Haystack NLP Framework - Haystack is an open source NLP framework to build applications with Transformer models and LLMs.
LangChain - Framework for building applications with LLMs through composability
Prompts - Build a better writing habit
Mitsuku - Browser-based, AI chat bot.