This is code for running the LLaMa model, sort of like llama.cpp.

It's a reimplementation of Facebook's original GPL-licensed open source client under a more permissive Apache license. The GPL requires all your other code to also be GPL, so you can't use it in closed-source projects.

This doesn't affect the license for the model weights, which you will still have to download from somewhere else.

not a lawyer, but i dont think it is enough to change the license, as its still derived from the LLaMa weights and so you'd still have to follow the rules.

>Meta grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty free and limited license under Meta’s copyright interests to reproduce, distribute, and create derivative works of the Software solely for your non-commercial research purposes. The foregoing license is personal to you, and you may not assign or sublicense this License or any other rights or obligations under this License without Meta’s prior written consent; any such assignment or sublicense will be void and will automatically and immediately terminate this License.

https://huggingface.co/decapoda-research/llama-7b-hf/blob/main/LICENSE

I've had a lot more luck solving novel coding problems with the GPT-4 version of chatGPT then Google. If you stick to older tech and libraries like Java and Spring that have been around forever, it's really good at solving fairly difficult problems if you just keep providing context. With Google, it's basically has someone done this exact thing on SO and gotten an answer, if not oh well

Exactly. There are people around me who say “I don’t need Copilot, I’m a real software engineer” and I really don’t understand this statement. It’s like the spoon is invented but you’re still eating with your hands and claiming that “real people eat with hands”

I’ve been using Copilot for 6 months now and it’s nothing to be scared of, it’s a tool that helps you write code efficiently while also improving you as a developer by providing some code snippets that may be more efficient/clean than the way you were thinking of writing them in the first place.

I suggest every software developer to make use of these tools, not to be scared by them. I highly doubt that in near future companies will hire prompt engineers over machine learning/software engineers.

Are you using some vector knowledge database? If the library is not popular, how can the GPT "know" about it?

In my experience, gpt tends to hallucinate the same incorrect response and refuses to make the directed corrections to code.

Do we expect businesses to be able to fine-tune training chat gpt or other big models with their own data sets? Has this been discussed or rumoured at all? Or is it already happening? I may have missed something.

The code created by generative models will still need to be verified by a dev. The industry will change. I don't even question that anymore. There was a similar wave when we started to Google everything and were able to find a ton of useful content on stackoverflow. Some devs adapted, some became obsolete. I think developers will be able to upskill faster and be more productive. In turn, the projects will just get more ambitious.

To your question, I would not worry.

My informal definition of "human-level AGI," is a system whose only mistakes are indistinguishable from mistakes a human could make, given the same context.

Oh, you are using Tensorflow -- that basically means you are going to be doing mostly cookie-cutter ML. Tensorflow is extremely rigid regarding customizable workflows. I wouldn't worry too much about your math background -- it'll mostly be software engineering.

Idk, the procedure to check for contamination described in the release report sounded solid at first glance, and I don't see how this news changes that

So far?

• Draft logo creations
• Recreation of those late night geeking sessions
• a smarter search engine

Large scale automation has been happening for over 200 years (and beyond) and so far it hasn't translated to productivity gains being handed down to workers, so I'm not holding my breath.

>I think a lot of people have falsely bought the concept that their identity is their job, because there is such material incentive for that to be the case.

This is easily said and while true, this kind of sentiment seems to neglect the fact that we live in an economic system where you need a job to survive if you're not independently wealthy.

And for that question it does make a big difference whether you are a 200k/year ML engineer, or a $20/hr LLM prompter.

Four days after the end of the reviewer-author discussion period, I'm still wondering how many people (here at least) have received no reply to their rebuttals. Asking this because I hoped that there will be late replies (i.e. a couple of days past the end of the discussion period). But I only got one (6 hours past the end).

I ask Bing to read the pdf document I have opened and provide me with an abstract. I love that.

https://youtu.be/S3Lz1L-ee3k this is what 5 min of training can do :D

WandB is great tool. But I'm not a huge of the licensing for self-hosting it.

The free licence does not allow team collaboration, apparently you have to pay per GB of artifacts above 100GB (even if the data is on private cloud object storage like S3) and is only limited to docker deployments (Kubernetes only on Enterprise edition).

Wandb only logs what you tell it to log. The data hosted on their server is opt-in, meaning you explicitly log what you want when you use their API.

If you don't trust any third party with your process, the local setup seems pretty straightforward (docker container), info at https://docs.wandb.ai/guides/hosting/basic-setup.

I did use some other experiment tracking software and found wandb to be easier and full of useful features. Granted I didn't try MLFlow, but wandb had almost everything I needed so no point in trying all the options. (I wanted a mix of collaborative Tensorboard+Git+DVC+Pachyderm but on windows)

It absolutely has a concept of right or wrong. Ask it basic true or false questions and it will get them right most of the time.

In fact I asked it for grammar mistakes in your post and it noticed you used the incorrect for of "its" in your 3rd paragraph, and used "anyways" when it should be "anyway".

Seems like it knows right from wrong.

>It doesn't reason between sources.

It doesn't have access to source, it only has access to its own memory.

This is like if you asked me a question and I answered correctly, then you asked for sources and I tried to remember where I got it from. I could tell you sources that I think are right but are actually wrong due to my own memory degradation. Human memory is also very unreliable, but they're very good at making up things that "sound" like they could be right to them.

People "hallucinate" facts all the time.

Keep in mind that it takes a very, very enlightened and humble person to admit that another person could do their job, let alone an AI. Reddit isn’t known for playing host to this type of personality.

ChatGPT is great at learning the nuances of english, i.e. synonyms and metaphors. But if you feed it a reworded leetcode question and it finds the answer within its neural net, has it learned to conceptualize? No, it just learned that synonym ...

If you have an offer you’re qualified. It’s not like they’re going to dump you in a cube alone with no guidance and no teammates while expecting you to produce GPT-5 competitor by yourself. Take it and go.

Accept it. Countless people take on jobs they may not be qualified for and do a terrible job. Just take it and do your best to learn on the go and youll be ahead of the curve.

How about you offer to work for half of the payment they offered?