4 ms·
Not sure exactly what setup you are running, in theory yes, higher temperature for both model means higher chance of overlap and thus less rejections -> faster
by sailingparrot 1y ago
Not sure exactly what setup you are running, in theory yes, higher temperature for both model means higher chance of overlap and thus less rejections -> faster sampling (but worse quality overall).
However, if you have higher temperature but still are operating under a top-k sampling where k is small, not sure it's going to translate to any noticeable difference, since this will make your actual distributions very much non-uniform.
- wishawa 1y agoThis is with Together's API via OpenRouter, running DeepSeek V3 0324 and Kimi K2 0905. I didn't set a top-k. So it seems like Together must be doing something weird in their speculative decoding implementation.
- sailingparrot 1y agoOh in that case there is definitely a top-k or top-p behind the scene, it might just not be exposed to the user as a param they can change through their API. I haven’t heard of anyone running a LLM in prod with actual pure sampling
- wishawa 1y agoI see. That's slightly unfortunate. In principle, increasing temperature flattens out the distribution but the ordering between different tokens' probabilities remain the same, so setting a top-k shouldn't break my test. Can't say the same for top-p though. And all of this is probably too deep into the provider's implementation details for me to make assumptions on.