This post by @maggie has some great ideas on how #LLM tech can help enable #LocalFirst applications for regular folks. I've been wanting to do something similar within @agregore some day with local LLMs helping people author p2p web apps.
@mauve
I've yet to get a better response from a local LLM to a code question than I get from a web search or going to StackExchange etc. Are you finding good uses yet?
I confess I haven't tried too hard, but then most people won't and that's the point really anyway. 🤷♂️
I expect they should be good for accessibility, such as speech in/out but an not seeing those apps. Why not?! 🤦♂️
Although I see Mozilla have put a local LLM in Firefox to generate alt text for images.
@maggie nice reading. I'm a bit skeptical about LLM but you might be right 🤔 future will tell. As a dev, I'm glad to read non-techie people getting the point of local first app: you did well to introduce the concept
Pünktlich zur #bibliocon24 starten wir im VÖBB einen neuen, experimentellen Dienst: den VÖBB-Chatbot. Als meines Wissens erste (?) deutsche Bibliothek kombinieren wir hier Sprachtalent und "Wissen" eines Large Language Models (#LLM) mit den vollständigen Metadaten unseres #VÖBB Kataloges (als sog. Embedding).
I have a newly graduated SW Eng (BS in CS) who is struggling to find a job and getting advice to go back and get a Master’s Degree in #LLM in order to be more marketable.
I’ve always heard that grad degrees aren’t strictly necessary in SWE to start but is this changing? Are there other time investments that make more sense (open source contributions, certifications, personal projects, etc?)?
With #LLM applications more abundant, have researchers been using them to assist their writing? We know they have when writing peer reviews [1], but how about doing so in writing their published papers?
Liang et al comes back to answer this question in [3]. They applied the same corpus-based methodology proposed in [2] on 950k papers published between 2020 to 2024, and the answer is a resounding YES, esp. in CS (up to 17.5%) (screenshot 1).
In welchem ich als Ergänzung zu meinem vorherigen Artikel einmal die Installation und den Gebrauch von Ollama demonstriere.
Wir installieren Ollama, laden mistral:instruct und verwenden den Ollama Prompt auf einem Mac mini oder einem Windows-Rechner mit Nvidia, um einen Text zusammenfassen zu lassen.
While you consider submitting to the Call for Problems for the #ALTA2024 Shared Task (see link below), we'd like to share with you the winner of the #ALTA2023 Shared Task, which involved distinguishing #LLM-generated from human-generated text.
Here, Rinaldo Gagiano and Lin Tian from #RMIT use a fine-tuned #Falcon7B model with label smoothing, yielding an accuracy of 99.91%. Well done!
@sebsauvage j'ai l'impression aussi qu'economiquement parlant, il y a que Nvidia qui fait sont beurre et que les autres boîtes sur les investissements des banques.
There was a paper shared recently about the exponential amount of training data to get incremental performance gains in #llm#ai, but I seem to have misplaced it. Do you know what I’m referring to? Mind sharing the link if you have it?
I think one of the biggest fears people have about AI is that it isn't perfect as assumed, but that, like us humans, it takes the given information, assumes the most likely outcome, and presents it plausibly.
@anmey yeah, there’s this paradox — we kinda want computers to think like humans, but when they get plausibly good at it, we complain that they don’t think like computers anymore
I’d like to trust this story, but it fails to link to its supposed source or provide enough info to find it elsewise. A few clicks around the site makes me think that it may well be nothing but a #LLM-composed content farm. https://cosocial.ca/@kgw/112498693958537559
There's an economic curse on Large Language Models — the crappiest ones will be the most widely used ones.
The highest-quality models are exponentially more expensive to run, and currently are too slow for instant answers or processing large amounts of data.
Only the older/smaller/cut-down models are cheap enough to run at scale, so the biggest deployments are also the sloppiest ones.
Llama.cpp now supports the distributed inference, meaning you can use multiple computers to speed up the response time! Network is the main bottleneck, so all machines need to be hard wired, not connected through wifi. ##LLm#AI#MLhttps://github.com/ggerganov/llama.cpp/tree/master/examples/rpc