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Question, what do you do with it and how is the data that underlies the model harvested?
I use it for both fun throwaway stuff as well as productivity tasks for learning.
Example of fun throwaway:
Do you remember that episode of Seinfeld where the Kramer starts using Facebook marketplace and starts buying the most worthless items before being robbed when trying to get a too-good-to-be-true sale? No? Because it never happened, but you can plug that premise into an crafted LLM prompt and it will pop out a whole TV script with in-character dialog for each actor as well as use of popular existing sets.
Foreign language learning:
I'm studying a foreign language and want to interact with just the level of vocabulary and grammar I have knowledge of right now at my level for practice. I can prompt the LLM to limit itself to just what I know now and adjust the conversation level so I can practice. If I ever get stuck, I can ask the LLM to explain the grammar usage or vocabulary choice.
I'm not sure what you're asking here. Are you asking, for example, how the Deepseek model was trained? If so, I answered that above. If not, can you rephrase your question?
You run a program like llama.cpp that can use the weights to run the model, which you can then use for whatever you'd use a model for: figuring out tech stuff, coding, etc. It's a bit involved, but there are tools that make it easy to get started. LM Studio for instance.
In some cases, the lab that created the model publishes the dataset that it was trained on, and those are usually made of publicly available data. In most cases, though, the labs don't give details, but the answer likely involves siphoning every web page they could find.