__lawless

__lawless t1_j9617qd wrote

Reply to [D] Is Google a language transformer like ChatGPT except without the G (Generative) part? by Lets_Gooo_123

You are not making any sense. Language transformer is not a thing. Google is a search engine, ChatGPT is a LLM. There is no quantum computing involved in chatGPT. It has nothing to do with it at all. I’m gonna leave you with a quote from Billy Madison.

what you've just said is one of the most insanely idiotic things I have ever heard. At no point in your rambling, incoherent response were you even close to anything that could be considered a rational thought. Everyone in this room is now dumber for having listened to it. I award you no points, and may God have mercy on your soul.

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__lawless t1_j88fo2u wrote

Reply to [D] Looking for an open source Downloadable model to run on my local device. by [deleted]

I don’t think you have a sound idea what you are trying to do. So you want chatGPT + extra!!! What you are asking does not exist, at least currently. Making a model size of chatGPT will cost at the very least $5M and absolutely not possible locally. You need a distributed setup. Not to mention all the technical difficulties of making such a setup.

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__lawless t1_j76xpgk wrote

Reply to comment by jaqws in [R] Multimodal Chain-of-Thought Reasoning in Language Models - Amazon Web Services Zhuosheng Zhang et al - Outperforms GPT-3.5 by 16% (75%->91%) and surpasses human performance on ScienceQA while having less than 1B params! by Singularian2501

Just 2 points a) They fine tuned this model to death. Where as GPT3.5 has a handful of examples to fine tune b) This is a multi modal model which consumes the image directly. Where as GPT can only consume text, so they fed it caption of the image

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__lawless t1_j4r9ebs wrote

Reply to comment by monkeysingmonkeynew in [D] Is it possible to update random forest parameters with new data instead of retraining on all data? by monkeysingmonkeynew

Ok let me elaborate a bit. Imagine the old model is called m_0. Your newly obtained training data is X, y, features and labels, respectively. Now calculate the residual error which is the difference between y and prediction of m_0: dy = y - m_0(X). Now train a new model m_1. The labels and features are X, dy. Finally at inference time the prediction is the sum of the two models: y_pred = m_0(X_new) + m_1(X_new).

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__lawless t1_j4pzotl wrote

Reply to [D] Is it possible to update random forest parameters with new data instead of retraining on all data? by monkeysingmonkeynew

A lot of folks here already mentioned online learning and the resources for it. However I am going to offer a very hacky solution inspired by idea of boosting. Suppose you had a regression model already trained. Make prediction for the new training batch and calculate the errors. Now train a new random forest model for the residual errors. For inference pass the features into the first model. For inference just pass the features to both models and sum the results.

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__lawless t1_j0yuzij wrote

Reply to comment by Awekonti in [D] Deep Learning based Recommendation Systems by Awekonti

It really depends on the problem/situation. Of course MF is very powerful and interpretable despite being very simple. But for my job we did not have success with it, on the other hand we had great success with transformer based sequential recommendation models. Another method you want to look into is GNN. We did not invest it in because it is harder (not impossible) to scale. For example look into pinsage

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