Hugging Face
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I use to do my one on ones manually and had a slew of questions I'd run through. Now I have my reports answer the questions and leave a response of the most important things we can discuss when in our one on one.
Now I have a historical record of everything that is important, we spend time talking about what is most important for them that week, and we save nearly 30-45min per one on one.
Based on our record, Hugging Face seems to be more popular. It has been mentiond 256 times since March 2021. 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.
These libraries are fundamental for building and training our GPT model. PyTorch is a deep learning framework that provides flexibility and speed, while the Transformers library by Hugging Face offers pre-trained models and tokenizers, including GPT-2. - Source: dev.to / 3 days ago
Hugging Face is a company and community platform making AI accessible through open-source tools, libraries, and models. It is most notable for its transformers Python library, built for natural language processing applications. This library provides developers a way to integrate ML models hosted on Hugging Face into their projects and build comprehensive ML pipelines. - Source: dev.to / 11 days ago
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 / 10 days ago
Hugging-face π€ is a repository to host all the LLM models available in the world. https://huggingface.co/. - Source: dev.to / 17 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 2 months ago
LangChain - Framework for building applications with LLMs through composability
Bonusly - Recognition and rewards that make work fun
Replika - Your Ai friend
Labourly - Your all-in-one HR solution to manage and hire work-ready candidates.
Mitsuku - Browser-based, AI chat bot.
Kudos - Kudos is the simple and easy to use employee recognition software that enhances employee engagement and team communication.