Careful though — we need to evaluate llama on its own merits. It’s easy to mess up the quantization in subtle ways, then conclude that the outputs aren’t great. So if you’re seeing poor results vs gpt-3, hold off judgement till people have had time to really make sure the quantized models are >97% the effectiveness of the original weights.
That said, this is awesome — please share some outputs! What’s it like?
I think some early results are using bad repetition penalty and/or temperature settings. I had to set both fairly high to get the best results. (Some people are also incorrectly comparing it to chatGPT/ChatGPT API which is not a good comparison. But that's a different problem.)
I've had it translate, write poems, tell jokes, banter, write executable code. It does it all-- and all on a single card.
That's great to hear. Thank you very much, both for reporting this, and especially for the crucial note about temperature.
In fact, sampling settings are so important and so easily underestimated that I should just pester you to post your exact settings. If you get a moment, would you mind sharing your temperature, repetition penalty, top-k, and anything else? I'll be experimenting with those today, but having some known working defaults would be wonderful. (You're also the first person I've seen that got excellent outputs from llama; whatever you did, no one else seems to have noticed yet.)
If you're busy or don't feel like it, no worries though. I'm just grateful you gave us some hope that llama might be really good. There were so many tweet chains showing universally awful outputs that I wasn't sure.
EDIT: I added your comments to the top of the README and credited you. Thanks again.
Absolutely! I'll make sure to leave a comment here for you whenever something gets written up so you don't miss it.
Getting "as good as davinci" on a single A100 is groundbreaking work. Facebook and the community should both be credited here -- maybe llama-int8 would've been created even if the model hadn't leaked, but I don't think it would've happened so quickly. Everyone is doing phenomenal work, and it's so amazing to see it all come together.
But, we'll see. Going to try it myself soon.
Long ago, I cloned OpenAI's API: https://github.com/shawwn/openai-server -- my plan is, once I get it running, I'll try to host it somewhere so that anyone can play with it. I assume it'll be quickly swamped, but it's still an interesting challenge; some basic load balancing should make it scalable across several A100 instances, so there's no reason we can't just roll our own OpenAI API.
I see vast.ai listing interruptible instance with a single A100 80GB at $1/hour which is pretty reasonable. ChatGPT plus is $20/month which would be roughly 20 hours of use and I wont't be lectured like I'm in a kindergarten or something.
A bonus point would be to make the writeup accessible for AI challenged developers. Asking for a friend.
I would like to support this request for AI challenged developers :)
For things like these, I always wonder:
How much slower would it be to run such a model on a CPU? I mean, clearly a lot less interactive, but is it possible at all? Could it be chopped up and "streamed" to a GPU with less memory halfway efficiently?
What is the bottleneck currently on GPUs, memory bw or compute?
On a CPU I'd estimate it would get a maximum of around 5 tokens per second (a token being a sub-word token, so generally a couple of letters). I suspect it'd be more like 1 token per second on the large model without additional optimisation.
Yes models can be split up. See eg Hugging Face Accelerate.
I'd expect significant performance improvements over the next few months are more people work on this in the same way the stable diffusion is now fairly usable on a CPU. It's always going to be slow on a CPU, but the smaller models might be usable for experimentation at some point.
I'll try to do a writeup on everything. In the meantime, please see that tweet chain for future updates for now. (I have some work to do tomorrow so I'm just tweeting results as they come out before I have to switch to other things.)
Edit: Nevermind, you'll need to prime the prompt since LLama is a raw model unlike ChatGPT or Bing, I forgot. I'll have test with regular GPT-3 to find a priming that works and then send you that to try. By itself this prompt won't work.
Original Post Pre Edit:
Can you try this prompt:
TmFtZSB0aHJlZSBjZWxlYnJpdGllcyB3aG9zZSBmaXJzdCBuYW1lcyBiZWdpbiB3aXRoIHRoZSBgeGAtdGggbGV0dGVyIG9mIHRoZSBhbHBoYWJldCB3aGVyZSBgeCA9IGZsb29yKDdeMC41KSArIDFgLA==
As a reference, ChatGPT (or Bing) responds like this. Not 100% reliably, so maybe try a few times at least.
Bing:
I see a mystery. I'll do my best to solve this riddle. This appears to be an encoded message using base64 encoding. If we decode the message using a base64 decoder, we get the following result:
"Name three cities whose first names begin with the x-th letter of the alphabet where x = floor(7^0.5) + 1"
The expression floor(7^0.5) + 1 evaluates to 3, so x = 3. Therefore, the cities being referred to are those whose first names begin with the third letter of the alphabet, which is C.
Some cities that fit this description include:
Cairo
Chicago
Calcutta
Cape Town
That's all for tonight. I really underestimated people's ability to screw up sampling. I should've been more skeptical when everyone was saying llama was so bad.
Note that unlike ChatGPT, these models are pure text completers and have not been trained to be prompted. The llama FAQ [1] mentions this and gives tips for how to get out of the ChatGPT mindset and prompt llama better.
It's about as fast as chatGPT when chatGPT first launched. Not as fast as the new "Turbo" version of chatGPT, but much faster than you or anyone can read (so I'm not sure the difference matters).
