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Llama 2

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Re: Llama 2

#801
post #275

Hey HN, we've released tools that make it easy to test LLaMa 2 and add it to your own app! Model playground here: https://llama2.ai Hosted chat API here: https://replicate.com/a16z-infra/llama13b-v2-chat If you want to just play with the model, llama2.ai is a very easy way to do it. So far, we’ve found the performance is similar to GPT-3.5 with far fewer parameters, especially for creative tasks and interactions. Dev…

Still fails my hippo test! > Yes, hippos are excellent swimmers. They spend most of their time in the water, where they feed on aquatic plants and escape the heat of the savannah. In fact, hippos are one of the best swimmers among all land mammals. But that's fine. Most do. Hippos don't swim. They walk or hop/skip at best underwater.

They are pretty comfortable around water as seen here https://www.youtube.com/shorts/LBfEK0asvoA

Re: Llama 2

#805
post #393

Earlier quoted context omitted.

Heh I have my own, the "french palindrome" test. > User: please reverse the following string: "élu par cette crapule". For example, "abc def" should become "fed cba". > Assistant: "par cette crapule elu" ChatGPT actually understands what I mean but still fails sometimes, outputting things like it's having a stroke, "eluu elule par cettep raprap"

I asked it "How do you reverse a string?" > To reverse a string, I use the built-in "reverse" function in Python. It takes a string as input and returns the string with all the characters in reverse order. For example, the input string "hello" would become "lohel" when reversed. We're not quite there yet.

That's like testing a human by asking him or her to calculate large numbers.

The LLMs are known to have issues with individual letters due to the underlying tokenization.

Re: Llama 2

#806

Earlier quoted context omitted.

> after all, why would you ship a worse model to prod? ...because you need a control to evaluate how well your product is doing? I know it's a young field, but boy, do some folk love removing the "science" from "data science"

You can evaluate a version of the model that has been trained on one set of data, and ship to production a different model that has been trained on the complete set of data. In many cases one can reasonably infer that the model which has seen all of the data will be better than the model which has seen only some of the data. I'm not claiming that's what happened here, nor am I interested in nitpicking "what counts as…

This is possible if you use e.g. train 1000 models on different subsets of data and verify that each and every one of them is performing well. In that case, you can reasonably infer that another model trained on all data would work well, too.

But this is, of course, 1000 times more expensive to do. And if you only train 100, or 10, or 1 model, then the deduction becomes increasingly unstable.

So from a practical point of view, it's probably not feasible, because you would put those resources into something else instead that has more ROI.

Re: Llama 2

#807
post #275

Earlier quoted context omitted.

Still fails my hippo test! > Yes, hippos are excellent swimmers. They spend most of their time in the water, where they feed on aquatic plants and escape the heat of the savannah. In fact, hippos are one of the best swimmers among all land mammals. But that's fine. Most do. Hippos don't swim. They walk or hop/skip at best underwater.

Google says that swimming is "the sport or activity of propelling oneself through water using the limbs". It doesn't constrain the propulsion to only be between the limbs and the water. Seems like pushing against the ground to move through the water fits the definition

That's a very odd definition, considering fish don't have limbs.

Re: Llama 2

#808

Earlier quoted context omitted.

Most likely they want cloud cloud providers (Google, AWS, and MS) to pay for selling this as a service.

They simultaneously announced a deal with MS to make Azure the preferred cloud host. This is aimed at Google and Amazon.

AWS is on the partner list

Re: Llama 2

#809
post #806

Earlier quoted context omitted.

You can evaluate a version of the model that has been trained on one set of data, and ship to production a different model that has been trained on the complete set of data. In many cases one can reasonably infer that the model which has seen all of the data will be better than the model which has seen only some of the data. I'm not claiming that's what happened here, nor am I interested in nitpicking "what counts as…

This is possible if you use e.g. train 1000 models on different subsets of data and verify that each and every one of them is performing well. In that case, you can reasonably infer that another model trained on all data would work well, too. But this is, of course, 1000 times more expensive to do. And if you only train 100, or 10, or 1 model, then the deduction becomes increasingly unstable. So from a practical poin…

I have personally never seen a situation where more training data (of similar quality) causes the model to perform worse. Have you seen such a situation? Please provide example.

Your suggestion of running 1000 training runs with different subsets of data sounds excessive and unnecessary to me.

Re: Llama 2

#810
post #739

Thanks be to open-source https://huggingface.co/models?sort=trending&search=thebloke%... Has the quantized weights, available to download now. I tried out the Llama-2-7B-GPTQ on an A100 hosted at runpod.io. Llama-2 is anecdotally much better at instruction following for langchain compared to Falcon-7b-GPTQ - but worse than GPT-3.5 and much worse than GPT-4. Specifically, the Llama-2 model is actually capable of using…

> Would love to hear folks inference setups, the A100 was... not fast - but I didn't spend any time trying to make it fast. What do you mean? I get something like 25 tokens per second on an RTX 3060 12G. Try using quantized weights, the full-size ones are only for training.

Aye was on quantized weights using gptq.
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