it to like make the model small without it shedding the bed. >> Uh I see. Okay. So if you're doing post- training quantization which is uh you take the model that uh the uh like the release model the BF16 model and do post- training quantization on it. It is fairly u like you know easy to do. Uh so we quantize to FP4 models like uh a uh large uh GLM or like you know those like trillion size models. uh you need uh like um a we have blackwell nodes so we put it on blackwell uh in in a couple of hours the condise checkpoint is ready but then our pain starts there because now we have to evaluate these models match the model card actually that is where we spend a lot of our time now coming to uh uh training based methods okay so for large models and mediumsiz models I would say like you know 20 billion parameters plus or 30 million parameter plus den size. Uh this PTQ usually works out of the box. Uh with uh some selective quantization like the heristics like Dan mentioned right like we use some heristics such as sparse MOS can be aggressively quandisized like yeah and we also have this auto quandis which uses this automatic sensitivity analysis and napsack solver. Yeah. Yeah. So all those right. So for medium and large models PDQ works out of the box very easy relatively super easy to do. Uh then if it is smaller model say like you know less than 20B size model we have to do some uh quantization aware distillation etc to recover accuracy that is yeah so training based methods are a little bit more painful. They're becoming more painful especially with this reasoning models. So you need to have the original data set and it's not just about the original data set. Internally we have the original neotend data set but even then it is a pain because these are multi-rain uh multi-stage trained RL models like for done with now models are being trained with uh multiple teacher distillation where each teacher is like an expert in coding or reasoning or something. So it becomes really difficult to uh get uh good uh training data to uh train the model with with QAD uh yeah to recover accuracy. If we if we train if we do QA with wrong data it uh it most commonly breaks the model rather than helping it. Yeah.