Sometimes I get into arguments about AI and people comment weird stuff here on Lemmy. Like we have ethical AI models (meaning they’re not made up of unethically sourced training material). Or we got plenty “libre” or “free and open-source” AI models.
Am I missing something? Or is this just a bunch of people trying to bullshit me? Is anyone here aware of such large language models and would be so kind as to educate me? I mean we do have a ton of open-weights models. But proper open-source or ethical??? I’ve been following the news for quite a while and I’m aware of a very select few I’d call LLMs with training datasets available. I’m aware of:
- Pythia by EleutherAI (up to 12B, from 2023) and Dolly 2 by Databricks (12B, also from 2023)
- BLOOM by BigScience (176B, from 2022)
- GPT-2 by OpenAI (1.5B, from 2019)
- The recreation of LLaMA by the RedPajama-Project
- Apertus by the ETH Zürich
Edit: In the comments we got:
- LLM360 with K2-65B, Crystal-7b, Amber-7B
- the Soofi-Project
- OLMoE
That list needs a lot of footnotes, though. Some of them come with restrictions, terms and conditions… And none of them can do modern tasks like AI-assisted coding. The only one getting close is the Swiss model. Sadly the earlier version I tried didn’t perform well. Seems they released an updated v1.5 version this summer, I wonder if that’s better and anybody uses it for real-world applications.
So… Back to my initial question: Are there models out there with datasets available? I mean except the exactly one somewhat usable one I already found?
What would be an application for this? More transparency for court cases? Or enable people to search for an alternative source for the data, like a torrent?
It would allow users to audit the dataset for ethical concerns, to interrogate the set to understand what a model does and does not “know”, to identify weaknesses and biases, and to ensure that nothing has been inserted specifically to engineer prompt responses. Just examples off the top of my head.
Knowledge should be relatively impossible, though, if you just get the names and hashsums of the files? I mean I guess you could look up if a specific file you already have is in it. Might have some application. It needs to be very detailed, though. If they just call it GitHub-dump-timestamp or Wikipedia-dump-2026 with some overall hash, that doesn’t tell us anything.
Most resources have a URI, Universal Resource Identifier. Related to a URL, except that it doesn’t necessarily tell you how to locate the resource it identifies, URIs have dozens of schemas (eg isbn: for books) that can identify virtually any kind of media. Ideally they would tell us the URI, all metadata, source/provenance/licensing information, and the hash of the resource. If they truly are training on anonymous unattributed blobs that they just mysteriously found in their dataset, well that would be interesting to learn.