Earlier quoted context omitted.
If you read the Llama2 paper it is very clear that small amounts of data (thousands of records) make vast difference at the instruction turning stage. From the Llama2 paper: > Quality Is All You Need. > Third-party SFT data is available from many different sources, but we found that many of these have insufficient diversity and quality — in particular for aligning LLMs towards dialogue-style instructions. As a result…
This quote is quite funny taken out of context like this. Top AI researchers find that garbage in === garbage out.
Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
271–280 of 349 posts
Re: Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
#272Earlier quoted context omitted.
LLamA 2 at 70B is, let’s say pessimistically 70% as good as GPT3.5. This makes me think that OpenAI is lying about their parameter count, are vastly less efficient than LLaMA, or, the lager model sizes have diminishing returns. Either way, your point is a good one. Something doesn’t add up.
IMO Llama2 really isn’t close to 3.5. It still has regular mode collapse (or whatever you call getting repetitive and nonsensical responses after a while), it has very poor mathematical/logical reasoning and is not good at following multi-part instructions. It just sounds like 3.5/4 because it was trained on it.
Re: Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
#273Earlier quoted context omitted.
LLamA 2 at 70B is, let’s say pessimistically 70% as good as GPT3.5. This makes me think that OpenAI is lying about their parameter count, are vastly less efficient than LLaMA, or, the lager model sizes have diminishing returns. Either way, your point is a good one. Something doesn’t add up.
IMO Llama2 really isn’t close to 3.5. It still has regular mode collapse (or whatever you call getting repetitive and nonsensical responses after a while), it has very poor mathematical/logical reasoning and is not good at following multi-part instructions. It just sounds like 3.5/4 because it was trained on it.
Re: Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
#274Earlier quoted context omitted.
> The closed source players have the best researchers Is that definitely why? GPT 3.5 and GPT 4 are far larger than 70B, right? So if a 70B, local model like LLaMA can even remotely rival them, would that not suggest that LLaMA is fundamentally a better model? For example, would a LLaMA model with even half of GPT 4's parameters be projected to outperform it? Is that how it works? [I'm not super familiar with LLM tec…
LLamA 2 at 70B is, let’s say pessimistically 70% as good as GPT3.5. This makes me think that OpenAI is lying about their parameter count, are vastly less efficient than LLaMA, or, the lager model sizes have diminishing returns. Either way, your point is a good one. Something doesn’t add up.
There's a few public numbers from a handful of foundation models as to performance vs parameter count vs architecture generation. Not being able to compare in detail the architecture of the various closed models nor being more rigorous on training with progressively sized parameter sets, the conclusion at the moment is a general feeling or conjecture.
Re: Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
#275Apparently, many organizations have their own Azure OpenAI deployment and won’t let their employees use the public OpenAI service.
My understanding is that Azure makes sure all network traffic is isolated to their network so they have more controls over how their organization use ChatGPT.
I created a super simple step-by-step guide on how to obtain an Azure OpenAI endpoint & key here:
https://pdfpals.com/help/how-to-generate-azure-openai-api-ke...
Hope it would be useful to someone just getting started with Azure.
[0]: https://boltai.com
[1]: https://pdfpals.com
Re: Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
#276Earlier quoted context omitted.
The Azure SLAs state that neither the chats are stored nor used for training in any way. They are private and protected in the same way all the other sensitive data is stored on Azure. On top, you might argue that Microsoft and Azure are easier to trust than a still rather new AI startup.
I agree with your points. Having said that, Microsoft removed my Azure OpenAI GPT-4 access last week without warning. I was not breaking any TOS. Oh well, pointed back at OpenAi.
What kind of volume were you doing and did you use the API for anything other than your listed use case when applying?
Re: Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
#277Private and secure? I thought the main issue with privacy and security of (not at all)OpenAI models is that by using their products you agree for them to retain all the data you send and receive from the models forever for whatever they choose to use it for. Or is this just a thing for free use? If you pay, do you get a Ts&Cs that don't contain any wording like this? Still, even if there was no specific "we own every…
Re: Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
#278Earlier quoted context omitted.
This is one of the things that make me uncomfortable about proprietary llm. They get task performance by doing a lot more than just feeding a prompt straight to an llm, and then we performance compare them to raw local options. The problem is, as this secret sauce changes, your use case performance is also going to vary in ways that are impossible for you to fix. What if it can do math this month and next month the h…
I'm not sure you realize how proprietary LLMs are being built on. No one is doing secret math in the backend people are building on. The OpenAI API allows you to call functions now, but even that is just a formalized way of passing tokens into the "raw LLM". All the features in the comment you replied to only apply to the web interface , and here you're being given an open interface you can introspect.
How do you know that? With SaaS you are at the mercy of the vendor.
Re: Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
#279This appears to be a web frontend with authentication for Azure's OpenAI API, which is a great choice if you can't use Chat GPT or its API at work. If you're looking to try the "open" models like Llama 2 (or it's uncensored version Llama 2 Uncensored), check out https://github.com/jmorganca/ollama or some of the lower level runners like llama.cpp (which powers the aforementioned project I'm working on) or Candle, the…
Llama 2 might by some measures be close to GPT 3.5, but it’s nowhere near GPT 4, nor Anthropic Claude 2 or Cohere’s model. The closed source players have the best researchers - they are being paid millions a year with tons of upside - and it’s hard to keep pace with that. My sense is that the foundation model companies have an edge for now and will probably stay a few steps ahead of the open source realm simply for e…
It will be if openai keeps dumbing down GPT 4, no proof they're doing it but there is no way it's as good as it was at launch, or maybe I just got used to it and now notice the mistakes more.
Re: Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
#280Earlier quoted context omitted.
Llama 2 might by some measures be close to GPT 3.5, but it’s nowhere near GPT 4, nor Anthropic Claude 2 or Cohere’s model. The closed source players have the best researchers - they are being paid millions a year with tons of upside - and it’s hard to keep pace with that. My sense is that the foundation model companies have an edge for now and will probably stay a few steps ahead of the open source realm simply for e…
> The closed source players have the best researchers Is that definitely why? GPT 3.5 and GPT 4 are far larger than 70B, right? So if a 70B, local model like LLaMA can even remotely rival them, would that not suggest that LLaMA is fundamentally a better model? For example, would a LLaMA model with even half of GPT 4's parameters be projected to outperform it? Is that how it works? [I'm not super familiar with LLM tec…
Apparently there's a diminishing returns effect on ever enlarging the model.