4 ms·

> 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 to

> 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 4y 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 4y 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 4y ago
    You’re quoting and talking to an LLM
    - canadianfella 4y ago
      [dead]
    - lyu07282 4y ago
      And loosing the argument no less
visarga 4y 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.

junrushao1994 4y 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 4y 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 4y ago
  You’re quoting and talking to an LLM
  - canadianfella 4y ago
    [dead]
  - lyu07282 4y ago
    And loosing the argument no less

visarga 4y 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.