231–240 of 255 posts
Query: set a timer for 1 hour Result: [{"name":"set_timer","arguments":{"time_human":"1 hour"}}] Query: in 1 hour set a timer for 1 hour Result: [{"name":"set_timer","arguments":{"time_human":"1 hour"}}] I'd expect either a chain load or just a 2 hour timer. Further attempts humorously give two separate 1-hour-timers.
Dumb questions, from someone not in the field... What is a distilled model? Why doesn't Google do this (to make their models smaller)? Seems like you could make a competitor to Gemini?
There are two answers already and neither is entirely adequate. In normal LLM training, you take a set of documents and have it learn to predict the future, then have some private RLHF/RLVR etc. data that it learns to produce good chat outputs from. In distillation, you take a set of prompts you are interested in, and record the big LLM's outputs, then train your small model to produce the same output as the big LLM.…
Why use the bigger LLM outputs for this and not human outputs? If we assume that human responses to prompts are better than sota models (in some cases they are) then why use the big model at all?
Are you worried about Google's response to this? Google reportedly reacts to distillation attempts " with real-time proactive defenses that can degrade student model performance ". So if they detected you, they could have intentionally fed you a dumber but plausible variant of Gemini: https://cloud.google.com/blog/topics/threat-intelligence/dis... But also, this model is small and just focusing on the tool use. In te…
>Experiments at Cactus showed that MLPs can be completely dropped from transformer networks, as long as the model relies on external knowledge source. Heh, what a coincidence, just today one of my students presented research results which also confirmed this. He removed MLP from Qwen and the model still could do transformation tasks on input but lost knowledge.
Can it summarize text it fetches? Come to think of it, this could be a nice model to have as the first pass in a more complex agent system where Needle hands of the results of a tool call to a larger model. I will defiantly play around with this!
any attempt to summarize parts of this discussion result in empty output: [].
26M for tool calling is wild. curious how it handles ambiguous function signatures where the correct tool depends on conversational context rather than just the last message.
I assume this would only be useful as the second stage after a model like Whisper, as it can't understand speech where you'd want it, like on a phone or small device?