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Alpaca: A strong open-source instruction-following model

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Re: Alpaca: A strong open-source instruction-following model

#261
post #28

This is why I think we're seeing a Stable Diffusion moment for LLMs: https://simonwillison.net/2023/Mar/11/llama/ Look at the timeline: 24th February 2023: LLaMA is announced, starts being shared with academic partners: https://research.facebook.com/publications/llama-open-and-ef... 2nd March: Someone posts a PR with a BitTorrent link to the models: https://github.com/facebookresearch/llama/pull/73 10th March: First…

LLAMA.cpp with 65B parameters runs on a MacBook M1 Max with 64GB of RAM. See https://gist.github.com/zitterbewegung/4787e42617aa0be6019c3...

I have also seen it working on a Mac Studio with 64Gb of RAM. It is quite slow, not unbearably so, but slow.

Re: Alpaca: A strong open-source instruction-following model

#262

Earlier quoted context omitted.

[flagged]

You mean the AI content trained on human input?

Yes, of course I mean that, and we've seen how flawed it is when it is either gamed by the left (during training and release control) or right (post release trolling).

I'm taking it as a given that we will have reasonably robust solutions to that by the time it is able to compete with Wikipedia - in which case, for the reasons given it will offer a positive distinguishing input, though I suspect it will be complementary for a time.

Perhaps we'll see a neutrality-bot at some point, automatically flagging some of the problematic behaviour that undermines Wikipedia...

Re: Alpaca: A strong open-source instruction-following model

#263
post #28

This is why I think we're seeing a Stable Diffusion moment for LLMs: https://simonwillison.net/2023/Mar/11/llama/ Look at the timeline: 24th February 2023: LLaMA is announced, starts being shared with academic partners: https://research.facebook.com/publications/llama-open-and-ef... 2nd March: Someone posts a PR with a BitTorrent link to the models: https://github.com/facebookresearch/llama/pull/73 10th March: First…

I think the Stable Diffusion moment is very dependant on someone creating a commercially licensable version of this somehow. I think the prospect of never being able to put your creations in a product is too inhibitive for the hypergrowth stable diffusion saw.

Re: Alpaca: A strong open-source instruction-following model

#264
post #217

Earlier quoted context omitted.

[flagged]

I'm sorry, how will a LLM trained on human writing look less human? We've already seen that, without very restrictive lockdowns by researchers, LLMs will display all the worst kinds of human traits. Why would this be different?

Replied to a sibling comment.

Re: Alpaca: A strong open-source instruction-following model

#265
post #227
post #75

Earlier quoted context omitted.

Don’t even need human time. Just run it through davinci. Is this correct, yes or no? Or MTurk. Can cost less than $500.

Not sure about this specific example, but the prompts used for training were generated by davinci. The researchers only used 175 human-written prompts, and these were fed into davinci to generate the other ~52k used for fine tuning LLaMA to make it follow instructions. They had humans review the generated prompts to eliminate bad ones before fine tuning, but humans mess up. I wonder how davinci would respond if every…

A trick I've started to use with davinci is prompting it to use a calculator for all math

I preprompt it to believe it's math cannot be trusted (because it can't) and to instead output "!c " any time it would otherwise try to solve something

The next step I'm working on this weekend is piping that to Wolfram Alpha and seeing if I can put together a wrapper to make it "seamless" in terms of responses

Re: Alpaca: A strong open-source instruction-following model

#266
post #216
post #72

Earlier quoted context omitted.

For 10 billion+ parameter models, the effects of quantization are relatively small, for smaller models like Llama 7B the effect becomes more dramatic, but there is ongoing research on new quantization methods (like GPTQ) that preserve significant performance even on the lower end. Quantization isn't the only technique available for downsizing a model, Llama itself is already the result of sizing the model and input d…

Curious about the google traffic comment. Are you saying people are visiting sites less because they can stay on Bing/OpenAI?

It's a nice business model, scrape the web and be the ultimate knowledge middle man

Re: Alpaca: A strong open-source instruction-following model

#267
post #28

This is why I think we're seeing a Stable Diffusion moment for LLMs: https://simonwillison.net/2023/Mar/11/llama/ Look at the timeline: 24th February 2023: LLaMA is announced, starts being shared with academic partners: https://research.facebook.com/publications/llama-open-and-ef... 2nd March: Someone posts a PR with a BitTorrent link to the models: https://github.com/facebookresearch/llama/pull/73 10th March: First…

Here is one question I have not seen answered yet: All the magic of "7B LLaMA running on a potato" seems to involve lowering precision down to f16 and then further quantizing to int4. Clearly this quantized model still outputs something resembling human language, at the very least. But I haven't seen anyone show what effect this quantizing has on the quality of the output. If the quality of the output is bad, it's un…

See: https://arxiv.org/abs/2210.17323

Q: Doesn't 4bit have worsen output performance than 8bit or 16bit? A: GPTQ doesn't quantize linearly. While RTN 8bit does reduce output quality, GPTQ 4bit has effectively little output quality loss compared to baseline uncompressed fp16.

https://i.imgur.com/xmaNNDd.png https://i.imgur.com/xmaNNDd.png

Re: Alpaca: A strong open-source instruction-following model

#268

Earlier quoted context omitted.

So which is better, running 7B without quantization or running 13B with? They both require about the same amount of vRAM (10gb).

Empirically, 13B with quantization. In fact the person who said 4bit is worse is empirically incorrect. 13B with quantization even down to 3-bits has very near the same performance as uncompressed 16bit 13B with GPTQ quantization and binning. Source: https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-i...

I looked at the numbers you posted, and am feeling concerned with how aggressively you're commenting towards a number of people on this website.

For starters, I started in this field a few years after the 2012 wave started. I've been with it for a while and have seen a lot of trends come and go. One thing that stays the same is that things are always changing. Very few things are set in stone, and due to a few other things it takes years and years before anything even begins to be finalized.

The numbers you are quoting are from various research groups, and are days to weeks old. You've antagonized a number of users in this forum, from calling them wrong directly, or saying that another person is empirically incorrect based on numbers you haven't verified yourself, and that have not had time to settle in the field yet with respect to real-world usecases. I went to one of the methods you linked, GPTQ, and it indeed had a _good_ performance to size improvement, but was not 'no difference'. This also does not count that 4-bit GPU support is still not-well supported. On 13B, for 4-bit, a .1 perplexity difference is great, but I also believe that that is also at least a noticeable drop. The .42 perplexity drop for 3 bit is massive, but also still very information efficient.

This completely ignores the conversation about (back to the GPU side of things) kernel-level support for these operators, which is very underdeveloped. Technical and unvalidated lab numbers do not represent the real world, it's like battery technologies. They are two very different things, though there are impressive tech demos and numbers out there. Like many things, in my experience, at least, it comes down to a big 'it depends'. It'll all settle out in the wash and we'll see what methods end up reigning in the long run.

Again -- please stop attacking other HN users based on a partial -- if well-researched -- understanding of the subject matter. It seems you're very involved in this topic, and I agree that more people need to hear about it. I think you could do an excellent job in sharing that news to them. That is good, and I hope the evangelism efforts go well and wish you all the best on that front. However, it seems (and this may be an inappropriate judgement on my end) that you might have become personally entangled in what is generally a technical issue.

I am just a commenter on this website, though I have used hacker news for a very long time at this point. I requested previously that you tamp down flaming the other users a bit, and I'd like to ask you once more. A good litmus test to maybe ask yourself is "Am I including any information in this message that indicates that another person may be right or wrong, or that I might be right or wrong? How strongly do I feel that my perspective is reality vs their incorrect perspective?" If you trigger that line when writing out a comment -- even if there is a strong impulse to ignore it, it may be time to step back, breathe, and separate out what is a personal issue for you, and what is a technical issue that you are passionate about. You can have both at once.

Please just slow it down a bit. I want to see what you and everyone else can mutually bring to the table in this conversation. Thank you.

Re: Alpaca: A strong open-source instruction-following model

#269
post #221

Earlier quoted context omitted.

So which is better, running 7B without quantization or running 13B with? They both require about the same amount of vRAM (10gb).

Definitively, 13B with quantization will perform better. 4bits has been shown to be the optimal quantization for accuracy vs memory requirements.

Yeah, 7b vs 13b is basically no comparison in any situation, 16bit 7b is def worse than 4bit 13b. I'll be looking into 30B tomorrow. I may be able to do a full matrix of tests 4-16bit X 7-30b.

Re: Alpaca: A strong open-source instruction-following model

#270
post #28

This is why I think we're seeing a Stable Diffusion moment for LLMs: https://simonwillison.net/2023/Mar/11/llama/ Look at the timeline: 24th February 2023: LLaMA is announced, starts being shared with academic partners: https://research.facebook.com/publications/llama-open-and-ef... 2nd March: Someone posts a PR with a BitTorrent link to the models: https://github.com/facebookresearch/llama/pull/73 10th March: First…

LLAMA.cpp with 65B parameters runs on a MacBook M1 Max with 64GB of RAM. See https://gist.github.com/zitterbewegung/4787e42617aa0be6019c3...

That is still a 4000 usd computer. You can get 2 RTX3900 used for ~1000 usd and run 65B much faster.

I have a discord server up serving almost 500 users with 65B.

https://twitter.com/ortegaalfredo/status/1635402627327590400

For some things is better than GPT3, for other even Alpaca is better.

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