I used to feel the same way until I found some very interesting performance results from 3B and 7B parameter models.

Granted, it wasn’t anything I’d deploy to production - but using the smaller models to prototype quick ideas is great before having to rent a gpu and spend time working with the bigger models.

Give a few models a try! You might be pleasantly surprised. There’s plenty to choose from too. You will get wildly different results depending on your use case and prompting approach.

Let us know if you end up finding one you like! I think it is only a matter of time before we’re running 40B+ parameters at home (casually).

I am actively testing this out. It’s hard to say at the moment. There’s a lot to figure out deploying a model into a live environment, but I think there’s real value in using them for technical tasks - especially as models mature and improve over time.

At the moment, though, performance is closer to GPT 3.5 than GPT 4, but I wouldn’t be surprised if this is no longer the case within the next year or so.

After finally having a chance to test some of the new Llama-2 models, I think you’re right. There’s still some work to be done to get them tuned up… I’m going to dust off some of my notes and get a new index of those other popular gen-1 models out there later this week.

I’m very curious to try out some of these docker images, too. Thanks for sharing those! I’ll check them when I can. I could also make a post about them if you feel like featuring some of your work. Just let me know!

Assuming everything from the papers translate into current platforms, yes! A rather significant one at that. Time will tell us the true results as people begin tinkering with this new approach in the near future.

Thanks for reading! I’m glad you enjoy the content. I find this tech beyond fascinating.

Who knows, over time you might even begin to pick up on some of the nuance you describe.

We’re all learning this together!

Thanks for sharing this!

Good bot, I will do that next time.

Come hangout with us at !fosai@lemmy.world

I run this show solo at the moment, but do my best to keep everyone informed. I have much more content on the horizon. Would love to have you if we have what you’re looking for.

FOSAI Posts:

• Free Open-Source AI Nexus of Resources
• Lemmy Crash Course to Free Open-Source AI

OpenAI has launched a new initiative, Superalignment, aimed at guiding and controlling ultra-intelligent AI systems. Recognizing the imminent arrival of AI that surpasses human intellect, the project will dedicate significant resources to ensure these advanced systems act in accordance with human intent. It’s a crucial step in managing the transformative and potentially dangerous impact of superintelligent AI.

I like to think this starts to explore interesting philosophical questions like human intent, consciousness, and the projection of will into systems that are far beyond our capabilities in raw processing power and input/output. What may happen from this intended alignment is yet to be seen, but I think we can all agree the last thing we want in these emerging intelligent machines is to do things we don’t want them to do.

‘Superalignment’ is OpenAI’s response in how to put up these safeguards. Whether or not this is the best method is to be determined.

For anyone unaware, this is probably one of the better short and sweet explanations in regards to what HuggingFace is.

It is a hub for many code repositories hosting AI specific files and configurations, which has become a core ecosystem of many artificial intelligence breakthroughs, platforms, and applications.

🤗

FWIW, it’s a new term I am trying to coin in FOSS communities (Free, Open-Source Software communities). It’s a spin off of ‘FOSS’, but for AI.

There’s literally nothing wrong with FOSS as an acronym, I just wanted to use one more focused in regards to AI tech to set the right expectations for everything shared in /c/FOSAI

I felt it was a term worth coining given the varied requirements and dependancies AI/LLMs tend to have compared to typical FOSS stacks. Making this differentiation is important in some of the semantics these conversations carry.

Big brain moment.

Ironically, I think using this technology to do exactly that is one of its greatest strengths…

GL, HF!

Great suggestions! I’ve actually never interfaced with that first channel (SECourses). Looks like some solid tutorials. Definitely going to check that out. Thanks for sharing!

Lol, you had me in the first half not gonna lie. Well done, you almost fooled me!

Glad you had some fun! gpt4all is by far the easiest to get going with imo.

I suggest trying any of the GGML models if you haven’t already! They outperform almost every other model format at the moment.

If you’re looking for more models, TheBloke and KoboldAI are doing a ton for the community in this regard. Eric Hartford, too. Although TheBloke is typically the one who converts these into more accessible formats for the masses.

Thank you! Consider subscribing to /c/FOSAI if you want to stay in the loop with the latest and greatest news for AI.

This stuff is developing at breakneck speeds. Very excited to see what the landscape will look like for AI by the end of this year.