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a fun fact is Bing AI already gives pretty good answers on the tasks Bloomberg cites as motivation, like who is the CEO of various companies, "write a bloomberg
by RockyMcNuts 4y ago
a fun fact is Bing AI already gives pretty good answers on the tasks Bloomberg cites as motivation, like who is the CEO of various companies, "write a bloomberg excel blp formula to return industry subgroup of ibm apple microsoft google"
another fun fact from the paper, it trained for 1.3m GPU hours and didn't get through the whole corpus, did 80% of 1 epoch (p. 16).
another fun fact, according to Twitter rumors GPT-5 is already training on 25,000 A100s w/8 GPUs each, so 4.8m GPU/hours per day.
it's a good paper with a lot more detail than OpenAI is releasing. also confirms OpenAI is quite a bit ahead. They compare with benchmarks they ran with Bloom and open source GPT-NeoX which they were able to run on same data, and reported benchmarks for GPT-3 since you can't run it yourself (well I guess you could sample the API), and BloombergGPT was competitive with GPT-3 which is a couple of years old now.
my takeaway was, this stuff is hard and OpenAI is crushing it.
I wonder if they will just use it for terminal features, or make the API available, and at what cost, and if they will license the model and weights for people who want to run it internally and maybe train incrementally on their own data. (not sure how feasible that is)
- chasd00 4y ago> another fun fact, according to Twitter rumors GPT-5 is already training on 25,000 A100s w/8 GPUs each, so 4.8m GPU/hours per day. If true that is incredible. From what I understand there isn’t a function that relates the increase of training time/effort to performance. For example, twice the training doesn’t mean twice the performance. It could mean no increase or 1000x increase, no one really knows. Does anyone have any idea what to expect in terms of capabilities when this training is finished?
- RockyMcNuts 4y agohttps://twitter.com/abacaj/status/1635837820270002178 https://twitter.com/abacaj/status/1635837820270002178 the bloomberg paper does talk about how they sized the model based on their compute budget and research on time to train ... megatron has 1t parameters i'm intrigued by some of the knowledge graph performance benchmarks and wonder about training it alongside an explicit knowledge graph, instead of building its own implicit knowledge graph, despite the 'bitter lesson' about just letting simple models and massive computation do their thing.