4 ms·

The output is at least as good as davinci. I think some early results are using bad repetition penalty and/or temperature settings. I had to set both fairly hi

The output is at least as good as davinci. 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.

data_maan 4y ago
Is it just the RLHF training for the prompting that makes a difference, or are there also other, more tangible differences?
akreal 4y ago
Which prompt did you use for translation? I'd be curious to try it for my task too.
sillysaurusx 4y ago
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.
- linearalgebra45 4y ago
  Would you mind publishing your notes/learnings once you gain enough understanding of this model?
  - sillysaurusx 4y ago
    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 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.
    - rnosov 4y ago
      Seconded. Do write it up. 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.
      - davrosthedalek 4y ago
        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?
        nl 4y ago
        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.
v64 4y ago
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. [1] https://github.com/facebookresearch/llama/blob/main/FAQ.md#2 https://github.com/facebookresearch/llama/blob/main/FAQ.md#2

data_maan 4y ago

Is it just the RLHF training for the prompting that makes a difference, or are there also other, more tangible differences?

akreal 4y ago

Which prompt did you use for translation? I'd be curious to try it for my task too.

sillysaurusx 4y ago

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.

linearalgebra45 4y ago
Would you mind publishing your notes/learnings once you gain enough understanding of this model?
- sillysaurusx 4y ago
  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 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.
  - rnosov 4y ago
    Seconded. Do write it up. 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.
    - davrosthedalek 4y ago
      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?
      - nl 4y ago
        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.

v64 4y ago

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. [1] https://github.com/facebookresearch/llama/blob/main/FAQ.md#2 https://github.com/facebookresearch/llama/blob/main/FAQ.md#2