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This is really cool but the output is such garbage at that weight size that you might as well be running a markov chain.
by naillo 4y ago
This is really cool but the output is such garbage at that weight size that you might as well be running a markov chain.
- lostmsu 4y agoFrom the video output seems fine. But if it is a trimmed version, it is wong to call it LLaMa.
- refulgentis 4y agoIt's nonsensical, celeb announces they're going to rehab and notes it (?) is an issue affecting all women, at least, earlier today (??), they also noted it wasn't drugs or alcohol this time, but, a life (???)
- ddren 4y agoWhat are you comparing it to? Without instruction tuning and a two character prompt "He" I am not sure why you would expect it to perform any better.
- refulgentis 4y agoI was replying to a comment that said it “seems fine.” It does not seem fine. It is incomprehensible and doesn’t match the results I’ve seen from 7B through 65B. It is true that RLHF could improve it, and perhaps then this severe of optimization will seem fine.
- tbalsam 4y agoI've heard a number of people say (from earlier) that the quantization and default sampling parameters is way wacked. Honestly even running that model size alone is the big achievement here and getting the accuracy to actually reach the benchmark is the beeg next step nao, I believe. <3 :'))))
- lostmsu 4y agoIf you run a quantized 60G model and the output is worse than raw 7G model, you can throw your quantizer out.
- londons_explore 4y agoWithout instruction tuning, the perfect language model produces output which has the same level of intelligibility as random text from the training set. And the training set probably has a lot of spam and junk in.
- PreachSoup 4y agoCould call it Slim LLaMa
- eganist 4y agoSLLaMa?
- sottol 4y agoAfaik, the Llama sampler needs to be tuned to get more sensible outputs. https://twitter.com/theshawwn/status/1632569215348531201 https://twitter.com/theshawwn/status/1632569215348531201
- simonw 4y agoThat's why Alpaca is so exciting: it instruction-tunes LLaMA to the point that even the tiny 7B model (the one that fits on a phone) produces useful output: https://simonwillison.net/2023/Mar/13/alpaca/ https://simonwillison.net/2023/Mar/13/alpaca/
- MagicMoonlight 4y agoBut they won’t give us the model… so it’s ultimately meaningless because they’ll just sell out
- itake 4y agoMy understanding is they legally can't. It was trained used OpenAI, which doesn't allow using their output to train new models. Someone would need to find another data source to fine tune llama.
- sdrg822 4y agoIt’s only a matter of time
- hackernewds 4y agowaiting for the stable diffusion version of gpt still
- dwallin 4y agoYou don't need to find a new data source, you just need to find an unencumbered third party. You can use the that data publicly provided in the git repo as long as you haven't signed an agreement with OpenAI yourself.
- Zetice 4y agoWhat will OpenAI do, sue? Okay but now it's out there.
- 4y ago
- circuit10 4y agoIt’s quite a bit bigger than GPT-2 which was a really big deal not very long ago (remember the unicorn news article example and the slow release because it was apparently too powerful?)
- falcor84 4y agoIsn't any LLM mathematically a Markov chain, such that the current state includes the context of the last (finite) n tokens?
- seabass-labrax 4y agoThe distinctive aspect of the 'transformer' family of neural networks is that they incorporate 'attention', which is a model that identifies which parts of the input are critical to its meaning. You're correct in that modern transformer models are essentially Markov chains, but the function that derives the probabilities (of which the attention heads are part of) is imbued with 'understanding' of related concepts during the training process. In contrast, a traditional Markov chain text generator might have a probability function that only takes into account the frequency of the n-grams in the training data, and so produces superficially coherent (but mostly meaningless) output.
- skybrian 4y agoThis is like saying all computers are state machines because they have finite amounts of memory and disk space. It's sort of true, but it's not a useful mathematical model. With a Markov chain, you're assuming a state machine where each state has independent probabilities on outgoing edges. As the number of states gets larger, you have fewer training samples for each state. When n gets large enough, nearly all states have zero training samples; they've never been seen before. How do you estimate probabilities? Better to just say it's a stateless function of the input.
- nl 4y agoOur standards have gone up so much! If you are talking about the video that's perfectly fluent English. There are some unusual elements to the story which probably wouldn't be there in a larger model. I'd invite you to try that with a Markov model or even something like a LSTM based neural network and compare.