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Based on our record, Hugging Face seems to be a lot more popular than Rancher. While we know about 252 links to Hugging Face, we've tracked only 24 mentions of Rancher. 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.
I don't know in which extend you plan to use Kubernetes in the future, but if it is aimed to become several huge production clusters, you should looks into Apps like Rancher: https://rancher.com. Source: over 1 year ago
But I think once you have a good understanding of K8S internal (components, how thing work underlying, etc.), you can use some tool to help you provision / maintain k8s cluster easier (look for https://rancher.com/ and alternatives). Source: almost 2 years ago
A few years, I would have said no. Now, I'm cautiously optimistic about it. Personally, I think that you can use something like Rancher (https://rancher.com/) or Portainer (https://www.portainer.io/) for easier management and/or dashboard functionality, to make the learning curve a bit more approachable. For example, you can create a deployment through the UI by following a wizard that also offers you... - Source: Hacker News / almost 2 years ago
Alternatively, it is also possible to use a multi-cloud or hybrid-cloud approach, which combines several cloud providers or even public and private clouds. Special tools such as Rancher and OpenShift can be very useful to run this type of system. - Source: dev.to / almost 2 years ago
Rancher provides a Rancher authentication proxy that allows user authentication from a central location. With this proxy, you can set the credential for authenticating users that want to access your Kubernetes clusters. You can create, view, update, or delete users through Rancher’s UI and API. - Source: dev.to / almost 2 years 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 / 19 days ago
The only requirement for this tutorial is to have an Hugging Face account. In order to get it:. - Source: dev.to / 24 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 1 month ago
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