I’m just in the beginning, but my plan is to use it to evaluate policy docs. There is so much context to keep up with, so any way to load more context into the analysis will be helpful. Learning how to add excel information in the analysis will also be a big step forward.

I will have to check out Mistral:) So far Qwen2.5 14B has been the best at providing analysis of my test scenario. But i guess an even higher parameter model will have its advantages.

And exactly why are they missing? Who stole what at Microsoft?

Thank you! Very useful. I am, again, surprised how a better way of asking questions affects the answers almost as much as using a better model.

I need to look into flash attention! And if i understand you correctly a larger model of llama3.1 would be better prepared to handle a larger context window than a smaller llama3.1 model?

Thanks! I actually picked up the concept of context window, and from there how to create a modelfile, through one of the links provided earlier and it has made a huge difference. In your experience, would a small model like llama3.2 with a bigger context window be able to provide the same output as a big modem L, like qwen2.5:14b, with a more limited window? The bigger window obviously allow more data to be taken into account, but how does the model size compare?

Thank you for your detailed answer:) it’s 20 years and 2 kids since I last tried my hand at reading code, but I’m doing my best to catch up😊 Context window is a concept I picked up from your links which has provided me much help!

The problem I keep running into with that approach is that only the last page is actually summarised and some of the texts are… Longer.

Do you know of any nifty resources on how to create RAGs using ollama/webui? (Or even fine-tuning?). I’ve tried to set it up, but the documents provided doesn’t seem to be analysed properly.

I’m trying to get the LLM into reading/summarising a certain type of (wordy) files, and it seems the query prompt is limited to about 6k characters.

For me it’s the opposite. My body may be on the premises, but mentally…

Well, that’s been the basis for some other products. AMD and Intel comes to mind😊 They both have IP the other need and historically Intel has been the dominant one, but now the tables have turned somewhat.

I feel that is the issue with this post to begin with. Is it a shitpost or not? Seems more like a load of hot air to me

slow clap

That’s not a straight line, although it is possible to follow without changing direction😊

Well… Its built on statistics and statistical inference will return to the mean eventually. If all it ever gets to train on is closer and closer to the mean, there will be nothing left to work with. It will all be the average…

So “it was only a joke” is also back on the menu? Wow, times are rolling back fast!

Even the term itself is a generalising stereotype. But it we are to have a somewhat serious discussion about it, I’d say It’s a human condition, not a gendered condition. For example, given what is (not) known about our respective genders, you felt the need to explain this.

So stereotyping gender is back on the menu? Are hair color jokes still taboo?

An LLM once explained to me that it didn’t know, it simulated an answer. I found that descriptive.

That’s optimization for you. Just a 27% increase in size would allow a whole lot of more shapes, but think of the economy!

I did not in any way mean to suggest sensitivity is not a factor, only to suggest that light sensitivity may be more of a spectrum and that there are persons living in a darker world than others. So, it may not be a person on the top of the bell curve that need more light, but someone on the other end of the spectrum entirely.

Since the top comment in this thread was about needing more light in an already bright room i meamt to say that there might be reasons why people around us prefer 1 or 100000 lumen…