ajrlewis on Nostr: Thanks for replying 🙏 Sure .. grifters gonna grift ... and if you're playing ...
Thanks for replying 🙏
Sure .. grifters gonna grift ... and if you're playing rug-pull games with over-hyped investments expect to get, unfortunately, rug ... pulled. Defo not a clown-world defender and agree that an aspect like that is a horrible hoax with undeserving victims.
But ... What are the other soultions before LLMs? ... If you program, what tools could I use in a method that takes (text, required_key_value_extracted_pairs) and returns a JSON {"key_1": "value_1", ...} object? Advanced LLMs, with guidance, really excel in that functionality. After all, they're just heavily trained neural networks ..
In particular, I have a "chat extract" method in my FOSS API, https://ajrlewis.com/api/docs, that uses a HuggingFace interference model (probs LLama, can't remember lol!) to do this task ... Would love to know a different approach if you can suggest one 😊 Defo save on the overheads + complexity if I didn't want to use a 3rd party (HuggingFace) in future ...
Sure .. grifters gonna grift ... and if you're playing rug-pull games with over-hyped investments expect to get, unfortunately, rug ... pulled. Defo not a clown-world defender and agree that an aspect like that is a horrible hoax with undeserving victims.
But ... What are the other soultions before LLMs? ... If you program, what tools could I use in a method that takes (text, required_key_value_extracted_pairs) and returns a JSON {"key_1": "value_1", ...} object? Advanced LLMs, with guidance, really excel in that functionality. After all, they're just heavily trained neural networks ..
In particular, I have a "chat extract" method in my FOSS API, https://ajrlewis.com/api/docs, that uses a HuggingFace interference model (probs LLama, can't remember lol!) to do this task ... Would love to know a different approach if you can suggest one 😊 Defo save on the overheads + complexity if I didn't want to use a 3rd party (HuggingFace) in future ...