5 ms·

There was a lot of detail and data in here, but it's not very useful to me because all of the comparisons are to things I have no experience with. There's real

There was a lot of detail and data in here, but it's not very useful to me because all of the comparisons are to things I have no experience with. There's really only one thing I care about: How does this compare to GPT-4? I have no use for models that aren't at that level. Even though this almost definitely isn't at that level, it's hard to know how close or far it is from the data presented.

quickthrower2 3y ago
The bit I liked best was the response examples. Look at those. Clearly not as good as GPT-4 but good enough I feel that for say a scenario where you care about privacy or data provenance this would be a contender. For example a therapist, a search bot for you diary, a company intranet help bot. Anything where the prompt contains something you don’t want to send to a third party.
- rawrmaan 3y ago
  That's a great point, I definitely overlooked these. They look pretty good, too, and I agree with your use cases. Thanks!
acapybara 3y ago
[flagged]
- rawrmaan 3y ago
  Oh I definitely agree that there are multiple levels of AI research that are valuable. Huge supporter of open source, and not meaning to talk down to anyone working on AI projects. It's just that at the moment I'm finding the open source LLM community hard to contextualize from an outside perspective. Maybe it's because things are moving so fast (probably a good thing). I just know that personally, I'm not going to be exploring any projects until I know they're near or exceeding GPT-4 performance level. And it's hard to develop an interest in anything else other than GPT-4 when comparison is so tough to begin with.
  - joshvm 3y ago
    I'd suggest reading the recently leaked Google memo for some context about why open source LLMs are important (and are disruptive from the perspective of a large company). It gives a good insight into why closed source models like GPT-4 might be overtaken by open source even if they can't directly compete at the moment. Typical reasons are highly specialised models that are cheap and fast to train, lack of censorship, lack of API and usage restrictions, lightweight variants and so on. The reason there's a lot of excitement right now is indeed how fast the space is moving. https://www.semianalysis.com/p/google-we-have-no-moat-and-neither https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...
    - rawrmaan 3y ago
      This was great. The timeline was the perfect catch-up I needed. Thank you!
- unsupp0rted 3y ago
  Surely this is satire. Machine-generated satire? Insisting on comparing open source options to the state of the art leader is white supremacy? Why not sexism and transphobia too?
  - deleted 3y ago
    [deleted]
  - adriand 3y ago
    I’ll admit that this comment struck me as strange too (and the thought it might be generated also crossed my mind) but I try to keep an open mind and after considering it, I think the point about white supremacy is worth considering. Note that the writer didn’t say “you’re a racist” but rather framed it rather neutrally: “This can be seen as a form of white supremacy, as it can exclude voices and perspectives from people who may not have access”. The question this raises is, who has access to state-of-the-art AI tech, and who does not, and might these groups have a racial dimension? Objectively, surely they do? Don’t lightweight models therefore serve an important democratizing purpose, in that they make this tech accessible to (for instance) people in developing nations who are overwhelmingly black and brown?
    - deleted 3y ago
      [deleted]
blihp 3y ago
Then you probably don't care about this (yet) Assume a truly competitive model in the Open Source world is still a ways off. These teams and their infrastructure are still in their early days while OpenAI is more at the fine-tuning and polishing stage. The fact that these open teams are able to have something in the same universe in terms of functionality this fast is pretty amazing... but it will take time before there's an artifact that will be a strong competitor.
- deleted 3y ago
  [deleted]
- nullsense 3y ago
  The pace of the progress the open source models are making is pretty astonishing. The smaller model sizes are cheap to train so there is a lot of iteration by many different teams. People are also combining proven approaches together. Then they're going to nail it and scale it. Will be very interesting to see where we are in 3 months time.
noman-land 3y ago
There's a nice chart in the leaked Google memos that compares some of the open models against ChatGPT and Bard so you can get a sense where these models land by comparing them to these. https://twitter.com/jelleprins/status/1654197282311491592 https://twitter.com/jelleprins/status/1654197282311491592
- encryptluks2 3y ago
  [dead]
atleastoptimal 3y ago
> How does this compare to GPT-4? I'll give you the answer for every open source model over the next 2 years: It's far worse
- MacsHeadroom 3y ago
  If you'd said that about OpenAI's DALL-E 2 you'd have been wrong. I suspect Open Source LLMs will outpace the release version of GPT-4 before the end of this year. It's less likely they will outpace whatever version of GPT-4 is shipped later this year, but still very much possible.
  - Sugimot0 3y ago
    Relevant post: https://news.ycombinator.com/item?id=35813322 https://news.ycombinator.com/item?id=35813322
  - int_19h 3y ago
    Open source LLMs might do that, but I very much doubt that those models will be small enough to run even on high-end consumer hardware (like say RTX 3090 or 4090).
    - regularfry 3y ago
      The way they'll do it, if they do it at all, is to find a way to squeeze the capability into smaller models and get much faster at executing them. That's where the market forces are. That's exactly the core of the email that leaked out of Google: it's proving far better to be able to have lots of people iterating quickly (which necessarily means broad access to the necessary hardware) than to rely on massive models and bespoke hardware. I'd anticipate something along the lines of a breakthrough in guided model shrinking, or some trick in partial model application that lets you radically reduce the number of calculations needed. Otherwise whatever happens isn't as likely to come out of the open source LLM community.
      - visarga 3y ago
Joeri 3y ago
None of the 3B and 7B models are at ChatGPT’s level, let alone GPT-4. The 13B models start doing really interesting things, but you don’t get near ChatGPT results until you move up to the best 30B and 65B models, which require beefier hardware. Nothing out there right now approximates GPT-4. The big story here for me is that the difference in training set is what makes the difference in quality. There is no secret sauce, the open source architectures do well, provided you give them a large and diverse enough training set. That would mean it is just a matter of pooling resources to train really capable open source models. That makes what RedPajama is doing, compiling the best open dataset, very important for the future of high quality open source LLM’s. If you want to play around with this yourself you can install oobabooga and figure out what model fits your hardware from the locallama reddit wiki. The llama.cpp 7B and 13B models can be run on CPU if you have enough RAM. I’ve had lots of fun talking to 7B and 13B alpaca and vicuna models running locally. https://www.reddit.com/r/LocalLLaMA/wiki/models/ https://www.reddit.com/r/LocalLLaMA/wiki/models/
- Semaphor 3y ago
  > The llama.cpp 7B and 13B models can be run on CPU if you have enough RAM. Bigger ones as well, you just have to wait longer. Nothing for real time usage, but if you can wait 10-20 minutes, you can use them on CPU.
  - int_19h 3y ago
    It's not even that bad. Core i7-12700K with DDR5 gives me ~1 word per second on llama-30b - that is fast enough for real-time chat, with some patience. And things are even better on M1/M2 Macs.
    - Joeri 3y ago
      The critical factor seems to be the ability to fit the whole model in RAM (--mlock option in oobabooga). With Apple's RAM prices most M1/M2 owners probably don't have the 32 GB RAM required to fit a 4bit 30B model.

quickthrower2 3y ago

The bit I liked best was the response examples. Look at those. Clearly not as good as GPT-4 but good enough I feel that for say a scenario where you care about privacy or data provenance this would be a contender. For example a therapist, a search bot for you diary, a company intranet help bot. Anything where the prompt contains something you don’t want to send to a third party.

rawrmaan 3y ago
That's a great point, I definitely overlooked these. They look pretty good, too, and I agree with your use cases. Thanks!

acapybara 3y ago

[flagged]

rawrmaan 3y ago
Oh I definitely agree that there are multiple levels of AI research that are valuable. Huge supporter of open source, and not meaning to talk down to anyone working on AI projects. It's just that at the moment I'm finding the open source LLM community hard to contextualize from an outside perspective. Maybe it's because things are moving so fast (probably a good thing). I just know that personally, I'm not going to be exploring any projects until I know they're near or exceeding GPT-4 performance level. And it's hard to develop an interest in anything else other than GPT-4 when comparison is so tough to begin with.
- joshvm 3y ago
  I'd suggest reading the recently leaked Google memo for some context about why open source LLMs are important (and are disruptive from the perspective of a large company). It gives a good insight into why closed source models like GPT-4 might be overtaken by open source even if they can't directly compete at the moment. Typical reasons are highly specialised models that are cheap and fast to train, lack of censorship, lack of API and usage restrictions, lightweight variants and so on. The reason there's a lot of excitement right now is indeed how fast the space is moving. https://www.semianalysis.com/p/google-we-have-no-moat-and-neither https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...
  - rawrmaan 3y ago
    This was great. The timeline was the perfect catch-up I needed. Thank you!
unsupp0rted 3y ago
Surely this is satire. Machine-generated satire? Insisting on comparing open source options to the state of the art leader is white supremacy? Why not sexism and transphobia too?
- deleted 3y ago
  [deleted]
- adriand 3y ago
  I’ll admit that this comment struck me as strange too (and the thought it might be generated also crossed my mind) but I try to keep an open mind and after considering it, I think the point about white supremacy is worth considering. Note that the writer didn’t say “you’re a racist” but rather framed it rather neutrally: “This can be seen as a form of white supremacy, as it can exclude voices and perspectives from people who may not have access”. The question this raises is, who has access to state-of-the-art AI tech, and who does not, and might these groups have a racial dimension? Objectively, surely they do? Don’t lightweight models therefore serve an important democratizing purpose, in that they make this tech accessible to (for instance) people in developing nations who are overwhelmingly black and brown?
  - deleted 3y ago
    [deleted]

blihp 3y ago

Then you probably don't care about this (yet) Assume a truly competitive model in the Open Source world is still a ways off. These teams and their infrastructure are still in their early days while OpenAI is more at the fine-tuning and polishing stage. The fact that these open teams are able to have something in the same universe in terms of functionality this fast is pretty amazing... but it will take time before there's an artifact that will be a strong competitor.

deleted 3y ago
[deleted]
nullsense 3y ago
The pace of the progress the open source models are making is pretty astonishing. The smaller model sizes are cheap to train so there is a lot of iteration by many different teams. People are also combining proven approaches together. Then they're going to nail it and scale it. Will be very interesting to see where we are in 3 months time.

noman-land 3y ago

There's a nice chart in the leaked Google memos that compares some of the open models against ChatGPT and Bard so you can get a sense where these models land by comparing them to these. https://twitter.com/jelleprins/status/1654197282311491592 https://twitter.com/jelleprins/status/1654197282311491592

encryptluks2 3y ago
[dead]

atleastoptimal 3y ago

> How does this compare to GPT-4? I'll give you the answer for every open source model over the next 2 years: It's far worse

MacsHeadroom 3y ago
If you'd said that about OpenAI's DALL-E 2 you'd have been wrong. I suspect Open Source LLMs will outpace the release version of GPT-4 before the end of this year. It's less likely they will outpace whatever version of GPT-4 is shipped later this year, but still very much possible.
- Sugimot0 3y ago
  Relevant post: https://news.ycombinator.com/item?id=35813322 https://news.ycombinator.com/item?id=35813322
- int_19h 3y ago
  Open source LLMs might do that, but I very much doubt that those models will be small enough to run even on high-end consumer hardware (like say RTX 3090 or 4090).
  - regularfry 3y ago
    The way they'll do it, if they do it at all, is to find a way to squeeze the capability into smaller models and get much faster at executing them. That's where the market forces are. That's exactly the core of the email that leaked out of Google: it's proving far better to be able to have lots of people iterating quickly (which necessarily means broad access to the necessary hardware) than to rely on massive models and bespoke hardware. I'd anticipate something along the lines of a breakthrough in guided model shrinking, or some trick in partial model application that lets you radically reduce the number of calculations needed. Otherwise whatever happens isn't as likely to come out of the open source LLM community.
    - visarga 3y ago

Joeri 3y ago

None of the 3B and 7B models are at ChatGPT’s level, let alone GPT-4. The 13B models start doing really interesting things, but you don’t get near ChatGPT results until you move up to the best 30B and 65B models, which require beefier hardware. Nothing out there right now approximates GPT-4. The big story here for me is that the difference in training set is what makes the difference in quality. There is no secret sauce, the open source architectures do well, provided you give them a large and diverse enough training set. That would mean it is just a matter of pooling resources to train really capable open source models. That makes what RedPajama is doing, compiling the best open dataset, very important for the future of high quality open source LLM’s. If you want to play around with this yourself you can install oobabooga and figure out what model fits your hardware from the locallama reddit wiki. The llama.cpp 7B and 13B models can be run on CPU if you have enough RAM. I’ve had lots of fun talking to 7B and 13B alpaca and vicuna models running locally. https://www.reddit.com/r/LocalLLaMA/wiki/models/ https://www.reddit.com/r/LocalLLaMA/wiki/models/

Semaphor 3y ago
> The llama.cpp 7B and 13B models can be run on CPU if you have enough RAM. Bigger ones as well, you just have to wait longer. Nothing for real time usage, but if you can wait 10-20 minutes, you can use them on CPU.
- int_19h 3y ago
  It's not even that bad. Core i7-12700K with DDR5 gives me ~1 word per second on llama-30b - that is fast enough for real-time chat, with some patience. And things are even better on M1/M2 Macs.
  - Joeri 3y ago
    The critical factor seems to be the ability to fit the whole model in RAM (--mlock option in oobabooga). With Apple's RAM prices most M1/M2 owners probably don't have the 32 GB RAM required to fit a 4bit 30B model.