Well, I do not have anything like this but it is very clear that China is way above GPT-3 level. Even the open-source community is significantly above. Take a look at LLaMA/Alpaca, people run them on consumer PC and it’s around GPT-3.5 level, the largest 65B model is even better (it cannot be run on consumer PC but can be run on a small ~10k$ server or cheaply in the cloud). It can also be fine-tuned in 5 hours on RTX 4090 using LORA: https://github.com/tloen/alpaca-lora .
Chinese AI researchers contribute significantly to AI progress, although of course, they are behind the USA.
My best guess would be China is at most 1 year away from GPT-4. Maybe less.
Thanks for that. In my own exploration, I was able to hit a point where ChatGPT refused a request, but would gladly help me build LLaMA/Alpaca onto a Kubernetes cluster in the next request, even referencing my stated aim later:
“Note that fine-tuning a language model for specific tasks such as [redacted] would require a large and diverse dataset, as well as a significant amount of computing resources. Additionally, it is important to consider the ethical implications of creating such a model, as it could potentially be used to create harmful content.”
FWIW, I got down into nitty gritty of doing it, debugging the install, etc. I didn’t run it, but it would definitely help me bootstrap actual execution. As a side note, my primary use case has been helping me building my own task-specific Lisp and Forth libraries, and my experience tells me GPT-4 is “pretty good” at most coding problems, and if it screws up, it can usually help work through the debug process. So, first blush, there’s at least one universal jailbreak—GPT-4 walking you through building your own model. Given GPT-4′s long text buffers and such, I might even be able to feed it a paper to reference a specific method of fine-tuning or creating an effective model.
Well, I do not have anything like this but it is very clear that China is way above GPT-3 level. Even the open-source community is significantly above. Take a look at LLaMA/Alpaca, people run them on consumer PC and it’s around GPT-3.5 level, the largest 65B model is even better (it cannot be run on consumer PC but can be run on a small ~10k$ server or cheaply in the cloud). It can also be fine-tuned in 5 hours on RTX 4090 using LORA: https://github.com/tloen/alpaca-lora .
Chinese AI researchers contribute significantly to AI progress, although of course, they are behind the USA.
My best guess would be China is at most 1 year away from GPT-4. Maybe less.
Btw, an example of a recent model: ChatGLM-6b
Thanks for that. In my own exploration, I was able to hit a point where ChatGPT refused a request, but would gladly help me build LLaMA/Alpaca onto a Kubernetes cluster in the next request, even referencing my stated aim later:
“Note that fine-tuning a language model for specific tasks such as [redacted] would require a large and diverse dataset, as well as a significant amount of computing resources. Additionally, it is important to consider the ethical implications of creating such a model, as it could potentially be used to create harmful content.”
FWIW, I got down into nitty gritty of doing it, debugging the install, etc. I didn’t run it, but it would definitely help me bootstrap actual execution. As a side note, my primary use case has been helping me building my own task-specific Lisp and Forth libraries, and my experience tells me GPT-4 is “pretty good” at most coding problems, and if it screws up, it can usually help work through the debug process. So, first blush, there’s at least one universal jailbreak—GPT-4 walking you through building your own model. Given GPT-4′s long text buffers and such, I might even be able to feed it a paper to reference a specific method of fine-tuning or creating an effective model.