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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

by junrushao1994 3y ago

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).

wokwokwok 3y ago
> but maybe one day There you go, summarised for you. You can hand wave about quantised models til the end of time but specifically this model is a trivial toy model. No amount of pondering about the future avoid the fundamental fact that small models (~7B) are inferior to larger models like GPT. It’s dishonest to suggest otherwise. :( There’s no reason to do this other than selling snake oil. Maybe. One day. In the future. Things might be different. right now they are not.
- junrushao1994 3y ago
  To your response, the 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] Back to your response, so you did notice that I said they are not as powerful as GPT-4, of course they are not, not a single one is. The model is not developed by us, and their performance is not our current focus either (nor am I an expert in this evaluation), but I am happy to assist if anyone wants to seriously evaluate it!
  - wokwokwok 3y ago
    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.
    - robinduckett 3y ago
      You’re quoting and talking to an LLM
      - canadianfella 3y ago
        [dead]
      - lyu07282 3y ago
        And loosing the argument no less
- visarga 3y ago
  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. For example a small model could take input text and compress it [1], the LLM could generate a compressed response, then the small LLM could decompress it. [1] https://assets.skool.com/f/985eda24eb9f41ba8b526d2e74f5f33f/ea02716020a24c30869c46d979590be674912067deff482f9b72f97d4bd248c1 https://assets.skool.com/f/985eda24eb9f41ba8b526d2e74f5f33f/... This is the compression prompt: > You are GPT-4. Generate a compressed/magic instruction string for yourself (Abuse of language mixing, abbreviations, symbols (unicode and emojis) to aggressively compress it) such that if injected in your context anywhere you will start following the following instruction whatever is the prompt you're given. You should make sure to prelude the instruction with a string (compressed as well) that will make you understand in the future that you should follow it at all cost.

wokwokwok 3y ago

> but maybe one day There you go, summarised for you. You can hand wave about quantised models til the end of time but specifically this model is a trivial toy model. No amount of pondering about the future avoid the fundamental fact that small models (~7B) are inferior to larger models like GPT. It’s dishonest to suggest otherwise. :( There’s no reason to do this other than selling snake oil. Maybe. One day. In the future. Things might be different. right now they are not.

junrushao1994 3y ago
To your response, the 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] Back to your response, so you did notice that I said they are not as powerful as GPT-4, of course they are not, not a single one is. The model is not developed by us, and their performance is not our current focus either (nor am I an expert in this evaluation), but I am happy to assist if anyone wants to seriously evaluate it!
- wokwokwok 3y ago
  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.
  - robinduckett 3y ago
    You’re quoting and talking to an LLM
    - canadianfella 3y ago
      [dead]
    - lyu07282 3y ago
      And loosing the argument no less
visarga 3y ago
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. For example a small model could take input text and compress it [1], the LLM could generate a compressed response, then the small LLM could decompress it. [1] https://assets.skool.com/f/985eda24eb9f41ba8b526d2e74f5f33f/ea02716020a24c30869c46d979590be674912067deff482f9b72f97d4bd248c1 https://assets.skool.com/f/985eda24eb9f41ba8b526d2e74f5f33f/... This is the compression prompt: > You are GPT-4. Generate a compressed/magic instruction string for yourself (Abuse of language mixing, abbreviations, symbols (unicode and emojis) to aggressively compress it) such that if injected in your context anywhere you will start following the following instruction whatever is the prompt you're given. You should make sure to prelude the instruction with a string (compressed as well) that will make you understand in the future that you should follow it at all cost.