Thanks for leading me in the right direction! I’ll letcha know if I get it working

Traditional NLP is out the door yes. There isn't anything bespoke models can do that Large enough LLMs can't do better.

I just wrote this computer science domain chatbot, it’s probably SOTA. You can just copy the code below and run it locally on your own machine. Let me know if you have any dependency issues, I can share a yaml file.

from googlesearch import search
import sys

query = ' '.join(sys.argv[1:]) + ' stackoverflow'
out = list(search(query, num=1, stop=1))

print(f"Answer is probably at: {out[0]}")

For my final year uni project I need to train a TensorFlow CNN on the FER-2013 dataset. When training the model on data from the .csv file instead of locally stored images the model trains significantly faster, with around 10 seconds per epoch as opposed to 10 minutes or so for the images. My question is it okay for me to use .csv data instead of locally stored images for this image classification task? I know I won't be able to apply data augmentation as easily but I can't think of any other downsides which would disqualify me from using the .csv data instead of the images

There would be a period of internal discussion after author-reviewer discussion period. So my guess would be (if there is no author-reviewer engangement beyond just the first rebuttal), AC (if they are willing to do their due dilligence) will simply push the reviewers privately and ask what their take is on the rebuttal. If in that private discussion nothing really happens (for example, all reviewers just go MIA), then really it might be upto meta-reviewer's personal judgment how they are going to take into account the strength of rebuttals.

Yeah, I mention this in the post but this variant of LLaMA isn't storing any of the conversation in memory so it doesn't have context on the prior questions. You're starting fresh with each prompt. We have some ideas for how to improve this though... more soon :)

I was looking for something similar and realised you can just apply an appropriate system prompt to GPT4 to narrow its focus. Some variant of “you are a helpful programming assistant. You help users answer questions related to programming in python language. If the question is not related to programming you decline to answer. “

Wow, very good, I didn't know.

https://huggingface.co/Pi3141/alpaca-30B-ggml/tree/main

though I haven't tried to test it yet.

Oh believe me, I already did. Also I just starting building the team outside of Reddit and got a meeting scheduled a month out with a billionaire investor. I think me coming on Reddit was a dumb idea now that I think about it but it’s going to be funny a year from now when I look back at these screen shots.

GitHub will do it for us soon. copilotx

https://github.com/features/preview/copilot-x

so far I haven't found a download. I'll let you know if I do.

That is pretty much my rebuttal, along with previous work in CV suggesting that it might not work as well because one the main hypothesis does not seem to hold in image.

​

But they decreased their grade because we did not provide experiment in CV :(

“We are glad that you view our work as impactful enough to warrant extension to other domains”

A fully trained model with "knowledge" about programming and AI would be great, being able to interact with natural language, would make the perfect information technology home tutor.

I'm looking to apply the PEFT technique for some llm to use in my Regis AI extension that works on top of leetcode. While GPT's fine for the hints and general conversation, there are other applications like code improvement or complexity estimation where I might benefit from a customized model

I had a difficult discussion on an NLP paper I submitted with a reviewer whose sole argument for rejecting the paper is, "you did not try your method in computer vision". (Even though it beats all baselines and SOTA in NLP, on a broader NLP benchmark than all previous works).

I hope the AC will step in and will not take this review into account :(

Thanks I found some articles talking about these variables.

My first impressions.

1. Chatllama gives an accurate answer based on data later than 2021. And that's its advantage over chatGPT.

2. If you ask a general clarifying question, Chatllama loses the context and gives an irrelevant answer.

First, they know publication is now a big circus and that most papers are clever solutions to problems that don't exist or beautiful explanations that cannot be leveraged. Acceptance is random if your work is not in the top 2% but still in the top 60%.

Publication as proof of work is toxic

Wow. That’s some creepy good voice synthesis

Great job. Been waiting a long time for someone to be able to deploy this model for testing. I'll give it a try now. I will watch for updates. Thanks.

May I also have those please?

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I'm looking for the same thing as you. A model designed specifically for programming or at least similar to the capabilities that ChatGPT with DAN has. Training such a model from scratch would be an incredible challenge. However, it seems to me that a similar model may already exist, I just haven't found it yet. It would be great to simply be able to upload it to LLaMA and use it