5 ms·
OpenAI has done the reasonable thing of not exposing the probability distribution per generated token so it's very hard to use that to completely map to their m
by varunkmohan 4y ago
OpenAI has done the reasonable thing of not exposing the probability distribution per generated token so it's very hard to use that to completely map to their models. Ultimately, you still do need a very large base model to compete.
- meghan_rain 4y ago"not exposing the probability distribution per generated token" can you elaborate on what that means?
- throwaway1851 4y agoA language model takes in a sequence of tokens and outputs a probability (0-1) for each token in the vocabulary (the set of all tokens the model knows). Based on this probability distribution, there are various sampling strategies that can be employed to choose which token to actually show to the user.
- wskish 4y agoOpenAI's previous completion endpoint for the davinci-003 and older models included a "logprob" return option: https://platform.openai.com/docs/api-reference/completions/create#completions/create-logprobs https://platform.openai.com/docs/api-reference/completions/c... Their newer chat style endpoint for the GPT-3.5-turbo and GPT-4 models no longer supports this. https://platform.openai.com/docs/api-reference/chat https://platform.openai.com/docs/api-reference/chat
- leobg 4y agoI was wondering. They do give you an embeddings endpoint. Can’t that theoretically be used to reconstruct the model’s weights?