Think of LLMs like a stupid office worker. You wouldn't rely on them to make critical decisions, but they're valuable for tedious stuff.
For example, my calendar changed the way to enter new events breaking my workflow. Now I just type out a skeletal schedule and have LLM convert that into a .csv that I import.
I'm thinking of Ripping my CD collection again. I'm researching a way to use a LLM to tidy up the metadata.
I had a folder full of random stuff I've saved for years. Had a LLM organize and categorize it for me. I had to tweak the prompt enough that this was a medium difficulty task, but still way easier than doing it manually.
Yeah even gpt4o couldn't keep track of encounters, run battles etc. in my case...
I think if you wanted to do it mechanically consistently you'd probably need to integrate it into a vtt where you give it context and potentially fine-tune it to give quest related summaries & gming rather than just "stuff"
the answer is very spesific to ur pc and amount of vram you have availşble to you. But anything lama 3 even 8b models finetuned to DM or write stories should theoritically work. The other reply that reccomends connecting to another program to make sure rules are consistent sounds like a great idea whşch I have not tried. I use silly tavern as the ui whşch has lots of options and shit to mske thşngs wkrk well. I would reccomend goşng şnto the "KoboldAI" discord and askşng şn the support sectşon folk there are very helpfull sorry for not beşng able to gşve a strsight answer Also boost the context size way up that shit makes dşfference I habe like 16k or sumthin. good luck!
I've used it to summarize long articles, news posts, or videos when the title/thumbnail looks interesting but I'm not sure if it's worth the 10+ minutes to read/watch.
There are other solutions, like a dedicated summarizer, but I've investigated into them and they only extract exact quotes from the original text, an LLM can also paraphrase making the summary a bit more informative IMO.
(For example, one article mentioned a quote from an expert talking about a company, the summarizer only extracted the quote and the flow of the summary made me believe the company said it, but the LLM properly stated the quote came from the expert)
This project https://github.com/goniszewski/grimoire has in it's road map a way to connect to an AI to summarize the bookmarks you make and generate at 3 tags.
I've seen the code, I don't remember what the exact status of the integration.
Also I have a few models dedicated for coding, so I've also asked a few pieces of code and configurations to just get started on a project, nothing too complicated.
Well, it's a bit of a pipeline, I use a custom project to have an API to be able to send files or urls to summarize videos.
With yt-dlp I can get the video and transcribe it with fast whisper (https://github.com/SYSTRAN/faster-whisper), then the transcription is sent to the LLM to actually make the summary.
I've been meaning to publish the code, but it's embedded in a personal project, so I need to take the time to isolate it '^_^
I use the Continue VS Code plugin with Ollama to use a couple of different models (deepseek-coder-v2 & starcoder2) to recreate a local only Github Copilot type experience for coding. This is on an M1 Apple Silicon though. For autocomplete the generation needs to be pretty brisk - I'm not sure how that would go in a VM without a GPU.
starcoder2:latest f67ae0f64584 1.7 GB 3 days ago
phi3:latest d184c916657e 2.2 GB 3 weeks ago
deepseek-coder-v2:latest 8577f96d693e 8.9 GB 3 weeks ago
llama3:8b-instruct-q8_0 1b8e49cece7f 8.5 GB 3 weeks ago
dolphin-mistral:latest 5dc8c5a2be65 4.1 GB 3 weeks ago
codeqwen:latest df352abf55b1 4.2 GB 3 weeks ago
llama3:latest 365c0bd3c000 4.7 GB 4 weeks ago
I mostly use starcoder2 with Continue for code autocomplete, the big deepseek coder is a bit slow (I can feel it thinking), but it and the regular llama3 are good for chatbot type programming questions.
I don't really have anything to compare the M1 performance to. I guess the 8GB models output text a little slower than the web versions of the same models, and the 4GB ones about the same. Using ollama in the terminal, there's sometimes a 0.5-2 second pause before it starts outputting. Not with phi3 though - it's surprisingly snappy for the quality of answers.
I have a 4070 sitting around collecting dust that I got from a trade, I've been thinking about setting it up with whispr and TTS and having a way to talk to my house.
I have a couple of smart home integrations, mostly air conditioning, light switches, security, and doors.
What I would like would be to have a few speakers on the walls that can talk to my server where I can say something like, hey computer, turn on the lights in the dining room and the lights in the dining room would turn on without transmitting that information to Google or Amazon.
I am really curious if you can get the traditional smart functionality along with a LLM. Maybe have some sort of keyword the prompts the AI. You also could write a custom generated system prompt that includes the weather, time and any other information
Roleplay (text adventures), a (stupid but occasionally funny) dungeon master, translation and help with creativity. These are the use cases I found. If you don't need that, you might get rid of it.
IMO LLMs are ok to get a head start of searching. Like got a vague idea of something but don't know the exact keywords. LLMs can help and use the output on whatever search engine you like. This saves a lots of time tinkering the right keywords.
I use local AI for coding (more recently) and ML Photo storage facial recognition and security camera object detection (been using the later 2 for years now actually, don't want that kind of info out on someone else's cloud training on my images)