Web LLM – WebGPU Powered Inference of Large Language Models

Re:Web LLM – WebGPU Powered Inference of Large Language Models by junrushao1994 on Saturday April 15, 2023 @09:22PM (#35586378)

To share some fun stuff, here is the response generated by this model: As an AI language model, I would respond by acknowledging that the model discussed in the message is indeed smaller than some of the larger language models like GPT-3/4. However, I would also point out that size is not always a direct indicator of performance, and that the model may still be able to perform well on certain tasks or in certain contexts. Additionally, I would emphasize that the browser compatibility of the model is a significant advantage, as it allows for easy and convenient access to the model without the need to install any specific software or hardware. Finally, I would suggest that the discussion focus on the potential applications and capabilities of the model, rather than solely its size.

Back to the topic, we don't make much innovation on the model, so I am probably not the best person to evaluate how a model compares with SOTAs. There are indeed lots of super cool techniques being explored lately that makes it possible to deploy smaller and smaller models, for example, LLM.int8() [1] and int4 quantization [2] without loss of zero-shot accuracy. Can't predict the future, but maybe one day there will be something really powerful but small enough to fit in the pocket of everyone :-)

[1] Dettmers, Tim, et al. "LLM. int8 (): 8-bit matrix multiplication for transformers at scale." arXiv preprint arXiv:2208.07339 (2022).

[2] Dettmers, Tim, and Luke Zettlemoyer. "The case for 4-bit precision: k-bit Inference Scaling Laws." arXiv preprint arXiv:2212.09720 (2022).

[ Parent | on HN ]

Re:Web LLM – WebGPU Powered Inference of Large Language Models by wokwokwok on Sunday April 16, 2023 @12:43AM (#35587336)

> but maybe one day

There you go, summarised for you.

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Re:Web LLM – WebGPU Powered Inference of Large Language Models by junrushao1994 on Sunday April 16, 2023 @03:30AM (#35588018)

Their model says:

Dear [Name],

Thank you for your message. We understand that the model you are referring to is a simple and basic model. However, it is important to highlight that this model serves a specific purpose and can be useful for certain applications.

Regarding the comparison with larger models like GPT, it's important to note that different models have different strengths and weaknesses, and the choice of model depends on the specific task and use case. While larger models like GPT may be more powerful and capable, they also come with greater computational and memory requirements.

We appreciate your concerns and feedback, and we will take them into consideration as we continue to develop our models. Our aim is to provide solutions that are tailored to the needs of our clients and meet their requirements for accuracy, efficiency, and performance.

Thank you for your input, and we hope to have the opportunity to work with you in the future.

Best regards, [Your Name]

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Re:Web LLM – WebGPU Powered Inference of Large Language Models by wokwokwok on Sunday April 16, 2023 @04:08AM (#35588155)

Quote:

> I would also point out that size is not always a direct indicator of performance, and

Yes. It is.

This thread is a direct response to a comparison to GPT, and your response (generated or not) is dishonest.

I can’t be blunter than that.

If you want amortise your responsibility by posting generated responses, go for it. Do whatever you want.

My response is directly to the parent comment about the comparison to GPT, for anyone who is unclear about the comparison.

[ Parent | on HN ]

Re:Web LLM – WebGPU Powered Inference of Large Language Models by robinduckett on Sunday April 16, 2023 @07:51AM (#35589185)

You’re quoting and talking to an LLM

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Re:Web LLM – WebGPU Powered Inference of Large Language Models by lyu07282 on Sunday April 16, 2023 @08:53AM (#35589608)

And loosing the argument no less

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Re:Web LLM – WebGPU Powered Inference of Large Language Models by visarga on Sunday April 16, 2023 @02:39PM (#35592970)

Tricks like Speculative Sampling show we can use small models to do useful work for large models, or use large models as correcting devices for small models. So I see a mixed future - both small and large models - one with low latency and fast, the other slow and sparsely called, working together to achieve the qualities of both. Maybe small models are useful to bring contextual information from search engines into the context, they can summarise faster a large volume of input text.

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