6 ms·

Stanford Alpaca, and the acceleration of on-device LLM development

222 points by Kye 4y ago

warning26 4y ago
Really neat! > Second, the instruction data is based OpenAI’s text-davinci-003, whose terms of use prohibit developing models that compete with OpenAI. Wow, that seems really sketchy on the part of OpenAI. Even considering their overall lack of openness, this clause feels particularly egregious.
- flangola7 4y ago
  That has to run afoul of competition/antitrust laws and be unforceable. Imagine if Ford tried to tell people you can't use their pickups to carry tools around on a new Honda plant construction site.
  - panarky 4y ago
    What if all the Honda plant does is remove the Ford logo and replace it with a Honda logo.
    - OkGoDoIt 4y ago
      Wouldn’t the first sale doctrine protect that? As long as the Honda dealership isn’t abusing the Ford trademark, my understanding is it’s perfectly legal to buy a product, modify it (even by removing branding), and then resell it. Of course with software you don’t really buy it, you license it, so there’s a lot more worms in that can of worms. Cars were simple by comparison
    - speed_spread 4y ago
      Workers would leave of their own Accord.
    - flangola7 4y ago
      That doesn't have anything to do with the tools used to build the plant.
- kir-gadjello 4y ago
  Charitably speaking the researchers had little time to execute this, so they just ended up using the well known OpenAI API. Still, it would be very useful if someone used LLaMA-65B instead of text-davinci-003 here. Someone should ask the researchers, either via email or via github pull request, it shouldn't even be that hard to do.
- alwayslikethis 4y ago
  I wonder if this is legally enforceable. If Microsoft is right with Copilot on the point that training models counts as "fair use", then the only thing stopping people from fine tuning their own models are these terms of use, which seems like it can be easily sidestepped by having an intermediate person/company to "process" the outputs.
macintux 4y ago
Active discussion on Alpaca: https://news.ycombinator.com/item?id=35136624 https://news.ycombinator.com/item?id=35136624 Also: https://news.ycombinator.com/item?id=35139450 https://news.ycombinator.com/item?id=35139450
- dang 4y ago
  Thanks! Macroexpanded: Alpaca: A strong open-source instruction-following model - https://news.ycombinator.com/item?id=35136624 https://news.ycombinator.com/item?id=35136624 Also recent and related: Large language models are having their Stable Diffusion moment - https://news.ycombinator.com/item?id=35111646 https://news.ycombinator.com/item?id=35111646 - March 2023 (355 comments)
swyx 4y ago
I feel like there has to be another shoe to drop here, this seems almost too good to be true. > Alpaca shows that you can apply fine-tuning with a feasible sized set of examples (52,000) and cost ($600) such that even the smallest of the LLaMA models—the 7B one, which can compress down to a 4GB file with 4-bit quantization—provides results that compare well to cutting edge text-davinci-003 in initial human evaluation. this is the most exciting thing. the cost of finetuning is rapidly coming down which means everyone will be able to train their own models for their usecases. Looking for the contrarians on HN: what is being left unsaid here that people like myself and Simon might be getting too optimistic about? what are the known downsides that people in academia already know about?
- Kye 4y ago
  The big failure mode is they can hallucinate nonsense that isn't obviously nonsense. You have to check any facts against expert sources. At that point, you could just email an expert who can use their own LLM to whip up an answer and check the facts themselves.
  - simonw 4y ago
    That's a big problem if you're using a language model as a search engine. The trick is to learn how to use them for the things that they're good for outside of that.
    - typest 4y ago
      ^^ this. For instance, LLMs are really good at turning natural language into SQL. And if you know SQL, you can read it and make sure it looks good. But, it's much faster and easier than writing SQL by hand.
      - flir 4y ago
        But that's still "You have to check any facts against expert sources"! You just have the advantage of being your own personal expert.
deleted 4y ago
[deleted]
bestest 4y ago
In their readme they mention `output: str, the answer to the instruction as generated by text-davinci-003`. And in the alpaca-data file there's this: { "instruction": "Perform the following arithmetic operation.", "input": "(5+7)\*3.", "output": "60." }, Do we need human QA on the training data? It's contaminating the set! I wonder how incorrect data like this will be filtered out in the future. Unless we're ok with that kind of new math.
- JimmyAustin 4y ago
  You can actually use AI to validate some of these answers! For questions involving math, it’s easy to get the LLM to write a problem to solve the problem, then use that to double check the result. For example, with the following prompt to ChatGPT: ‘’’ Can a program that solves the following problem accurately be written, and if so, write it in Python: {“Problem”: “Perform the following arithmetic operation." "input": "(5+7)3”} ‘’’ I got the answer: ‘’’ Yes, a program can be written to perform the arithmetic operation given in the input. Here is a Python implementation: input_str = "(5+7)3" # input string result = eval(input_str) # using eval function to evaluate the expression print(result) # output: 36 The eval() function in Python evaluates the expression given as a string and returns the result. In this case, the input string "(5+7)*3" is evaluated to 36. The program then prints the result using the print() function. ’’’
Havoc 4y ago
The fact that they’re using ai to train ai successfully gives me strong singularity-lite vibes
keskival 4y ago
The article confuses instruct tuning with RLHF. Those are two different things. DaVinci is also not instruct tuned, but it has an instruct tuned variant like the other OpenAI text completion models. The article is otherwise good.

222 points by Kye 4y ago

Really neat! > Second, the instruction data is based OpenAI’s text-davinci-003, whose terms of use prohibit developing models that compete with OpenAI. Wow, that seems really sketchy on the part of OpenAI. Even considering their overall lack of openness, this clause feels particularly egregious.

flangola7 4y ago
That has to run afoul of competition/antitrust laws and be unforceable. Imagine if Ford tried to tell people you can't use their pickups to carry tools around on a new Honda plant construction site.
- panarky 4y ago
  What if all the Honda plant does is remove the Ford logo and replace it with a Honda logo.
  - OkGoDoIt 4y ago
    Wouldn’t the first sale doctrine protect that? As long as the Honda dealership isn’t abusing the Ford trademark, my understanding is it’s perfectly legal to buy a product, modify it (even by removing branding), and then resell it. Of course with software you don’t really buy it, you license it, so there’s a lot more worms in that can of worms. Cars were simple by comparison
  - speed_spread 4y ago
    Workers would leave of their own Accord.
  - flangola7 4y ago
    That doesn't have anything to do with the tools used to build the plant.
kir-gadjello 4y ago
Charitably speaking the researchers had little time to execute this, so they just ended up using the well known OpenAI API. Still, it would be very useful if someone used LLaMA-65B instead of text-davinci-003 here. Someone should ask the researchers, either via email or via github pull request, it shouldn't even be that hard to do.
alwayslikethis 4y ago
I wonder if this is legally enforceable. If Microsoft is right with Copilot on the point that training models counts as "fair use", then the only thing stopping people from fine tuning their own models are these terms of use, which seems like it can be easily sidestepped by having an intermediate person/company to "process" the outputs.

macintux 4y ago

Active discussion on Alpaca: https://news.ycombinator.com/item?id=35136624 https://news.ycombinator.com/item?id=35136624 Also: https://news.ycombinator.com/item?id=35139450 https://news.ycombinator.com/item?id=35139450

dang 4y ago
Thanks! Macroexpanded: Alpaca: A strong open-source instruction-following model - https://news.ycombinator.com/item?id=35136624 https://news.ycombinator.com/item?id=35136624 Also recent and related: Large language models are having their Stable Diffusion moment - https://news.ycombinator.com/item?id=35111646 https://news.ycombinator.com/item?id=35111646 - March 2023 (355 comments)

swyx 4y ago

I feel like there has to be another shoe to drop here, this seems almost too good to be true. > Alpaca shows that you can apply fine-tuning with a feasible sized set of examples (52,000) and cost ($600) such that even the smallest of the LLaMA models—the 7B one, which can compress down to a 4GB file with 4-bit quantization—provides results that compare well to cutting edge text-davinci-003 in initial human evaluation. this is the most exciting thing. the cost of finetuning is rapidly coming down which means everyone will be able to train their own models for their usecases. Looking for the contrarians on HN: what is being left unsaid here that people like myself and Simon might be getting too optimistic about? what are the known downsides that people in academia already know about?

Kye 4y ago
The big failure mode is they can hallucinate nonsense that isn't obviously nonsense. You have to check any facts against expert sources. At that point, you could just email an expert who can use their own LLM to whip up an answer and check the facts themselves.
- simonw 4y ago
  That's a big problem if you're using a language model as a search engine. The trick is to learn how to use them for the things that they're good for outside of that.
  - typest 4y ago
    ^^ this. For instance, LLMs are really good at turning natural language into SQL. And if you know SQL, you can read it and make sure it looks good. But, it's much faster and easier than writing SQL by hand.
    - flir 4y ago
      But that's still "You have to check any facts against expert sources"! You just have the advantage of being your own personal expert.

deleted 4y ago

[deleted]

bestest 4y ago

In their readme they mention `output: str, the answer to the instruction as generated by text-davinci-003`. And in the alpaca-data file there's this: { "instruction": "Perform the following arithmetic operation.", "input": "(5+7)\*3.", "output": "60." }, Do we need human QA on the training data? It's contaminating the set! I wonder how incorrect data like this will be filtered out in the future. Unless we're ok with that kind of new math.

JimmyAustin 4y ago
You can actually use AI to validate some of these answers! For questions involving math, it’s easy to get the LLM to write a problem to solve the problem, then use that to double check the result. For example, with the following prompt to ChatGPT: ‘’’ Can a program that solves the following problem accurately be written, and if so, write it in Python: {“Problem”: “Perform the following arithmetic operation." "input": "(5+7)3”} ‘’’ I got the answer: ‘’’ Yes, a program can be written to perform the arithmetic operation given in the input. Here is a Python implementation: input_str = "(5+7)3" # input string result = eval(input_str) # using eval function to evaluate the expression print(result) # output: 36 The eval() function in Python evaluates the expression given as a string and returns the result. In this case, the input string "(5+7)*3" is evaluated to 36. The program then prints the result using the print() function. ’’’

Havoc 4y ago

The fact that they’re using ai to train ai successfully gives me strong singularity-lite vibes

keskival 4y ago

The article confuses instruct tuning with RLHF. Those are two different things. DaVinci is also not instruct tuned, but it has an instruct tuned variant like the other OpenAI text completion models. The article is otherwise good.