I’m running transformer models from Hugging Face on my laptop with python/jupyter notebooks. These notebooks take hours to run and I’d like to learn how to accelerate them. I believe my next steps are working with a cloud computing service to offload my code onto stronger computers, as well as editing my code to make use of parallelization. Does anyone have recommendations on services and/or resources for how to get started? I’m probably going to start looking into some sort of aws service (sagemaker?) and my friend mentioned dask to me, so I’ll look into that as well. Right now my code is calling these models using the standard pipeline method within a list comprehension. I believe I’ll need to change that, as I’m expecting that to not be parallelizable. Any advice? I’ve got a data frame with many rows and I’m trying to run a model on text within these rows for things like sentiment analysis and zero shot classification. Thank you! submitted by /u/MonBabbie [link] [comments]

Can someone explain the validation set? In a professional way like market-wise, how can it be a game changer? My whole knowledge about it is to plot it to check the model's performance and for early stopping, but that's it. submitted by /u/Top-Blueberry-6128 [link] [comments]

Hello Coders I would like to share an experience and know your opinions. I embedded about 12K+ order lists from a takeaway order system. I used Cohere english v3 and openai text embeding v3 for the embed. I prepared questions for the embed I would like large pizza, green pepper and corn questions with semantic parser. The output answers of these questions vegan pizza, vegan burger added pepperoni topping coke side topping did not satisfy me. Complementary and suggestion answers gave one quality and one poor quality output. Of course, these embed algorithms are usually based on conise similar. I suddenly had the suspicion that I should use embed for this type of rule based, match based, recommended. I believe that I can do the attached data with my own nlp libraries with more enrichment metadata tags without embedding. I would be glad if you share your ideas, especially if I can use llm in Out of vocabulary (OOV) detection contexts. Thank you. submitted by /u/serdarkaracay [link] [comments]

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Its hard as an international to get internships in this market but thankfully I had the fortune to interview for a few F250 companies. I seem to be missing out for fine margins. One company team technical lead said that i would be a good fit but since there was just 1 opening, I got referred to another team to apply . This happened quite a few times with others except i wasnt referred to other teams. I prepared for wrong things in that interview. I was able to answer all but it was thinking on spot and beating around the bush which definitely didn't help . Someone who knew it would sound more sure and knowledgeable and will get the edge .I know where i could have improved 🙁 This maybe my last opportunity to bag summer internship this year. I want to give my best and try to leave no stone unturned. It would be great of someone with experience in predictive Modelling in sales and marketing can tell me about some work done and commonly used questions / techniques. I did google and chatgpt but some real world / production level insights and some commonly used models and methods MLOps of this domain would help me a lot. Appreciate your support in the above matter submitted by /u/Mayukhsen1301 [link] [comments]

I had been training some Swin transformers using SimMIM for a paper and noticed that the linear probing accuracy on ImageNet1k was horrendous. While I was using the smallest Swin model, Swin-T, the performance after the 25th epoch was barely 2.5% top1 (ViT-T attains ~5% top1 while ViT-B does 7% after a similar number of epochs). I wanted to know if someone had done similar experimentation with Swin transformers and if using them in linear evaluation is a lost cause. Any help is appreciated. Thanks in advance. submitted by /u/Mad_Scientist2027 [link] [comments]

Does anyone know where to find information about what workshops will be held at ICML 2024? The workshop acceptance notifications were released March 27, but the workshops don't appear to be published on the ICML website yet. Thanks! submitted by /u/Stinein [link] [comments]

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I want to create a chatbot that can fetch data from database and answer questions. For example, I have a database with details of employees. Now If i ask chatbot how many people join after January 2024 that chatbot will return answer based on data stored in database. How to achieve this and what approch to use? submitted by /u/ssiddharth408 [link] [comments]

Paper: https://arxiv.org/abs/2402.02592 Code: https://github.com/SalesforceAIResearch/uni2ts Models: https://huggingface.co/collections/Salesforce/moirai-10-r-models-65c8d3a94c51428c300e0742 Dataset: https://huggingface.co/datasets/Salesforce/lotsa_data Blog post: https://blog.salesforceairesearch.com/moirai/ Abstract: Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models. The concept of universal forecasting, emerging from pre-training on a vast collection of time series datasets, envisions a single Large Time Series Model capable of addressing diverse downstream forecasting tasks. However, constructing such a model poses unique challenges specific to time series data: i) cross-frequency learning, ii) accommodating an arbitrary number of variates for multivariate time series, and iii) addressing the varying distributional properties inherent in large-scale data. To address these challenges, we present novel enhancements to the conventional time series Transformer architecture, resulting in our proposed Masked Encoder-based Universal Time Series Forecasting Transformer (Moirai). Trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains, Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models. Code, model weights, and data will be released. submitted by /u/SeawaterFlows [link] [comments]

arXiv: https://arxiv.org/abs/2402.14811 OpenReview: https://openreview.net/forum?id=8sKcAWOf2D Code: https://github.com/Nix07/finetuning Models: https://huggingface.co/nikhil07prakash/float-7b Project page: https://finetuning.baulab.info/ Abstract: Fine-tuning on generalized tasks such as instruction following, code generation, and mathematics has been shown to enhance language models' performance on a range of tasks. Nevertheless, explanations of how such fine-tuning influences the internal computations in these models remain elusive. We study how fine-tuning affects the internal mechanisms implemented in language models. As a case study, we explore the property of entity tracking, a crucial facet of language comprehension, where models fine-tuned on mathematics have substantial performance gains. We identify the mechanism that enables entity tracking and show that (i) in both the original model and its fine-tuned versions primarily the same circuit implements entity tracking. In fact, the entity tracking circuit of the original model on the fine-tuned versions performs better than the full original model. (ii) The circuits of all the models implement roughly the same functionality: Entity tracking is performed by tracking the position of the correct entity in both the original model and its fine-tuned versions. (iii) Performance boost in the fine-tuned models is primarily attributed to its improved ability to handle the augmented positional information. To uncover these findings, we employ: Patch Patching, DCM, which automatically detects model components responsible for specific semantics, and CMAP, a new approach for patching activations across models to reveal improved mechanisms. Our findings suggest that fine-tuning enhances, rather than fundamentally alters, the mechanistic operation of the model. submitted by /u/SeawaterFlows [link] [comments]

What type of architecture are you using for ML application? Are you using microservices or something else? submitted by /u/Bolehlaf [link] [comments]

I am using models to make classifications. Is it good practice to train a model to make a probability prediction on a training and test set and then to add the resulting probabilities back into a new model with the probability as a feature? Flow: Train/Test Model predict_proba to get probability Train another model with the same features as the first + the probability The second model is significantly more accurate, but I am skeptical. submitted by /u/Fuzzy_Lock_5557 [link] [comments]

So, I am working on this project where I am assigned with the task to detect pedestrians that are using mobile phones on road and fine them. So how this system works is exactly like the one with cars where the car number plate is read, then it is looked up on the database and a fine is deducted from the owner's account. Now, I've worked on face recognition / attendance systems before as there are thousands of models out there and I have looked at plenty of those codes before, but this is my first time working with yolo and I don't even do image processing but could not turn down my professor, which brings me here to ask for help from ya'll here. I have got all the other parts down, and a teammate is working on the fine deduction process but am stuck on how to implement the step where the face is to be recognized only when the person is holding the phone and only of the person(s) holding the phone. submitted by /u/gh0st_taskforce_141 [link] [comments]

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For a client I need to train an object detection model that is able to run on a coral board for edge TPU inference, there are some examples that simplify the process using TensorFlow Lite Model Maker or some other examples using kerasCV(not for edge TPU) but for this client we are looking to use pure keras because there are some requirements that cannot be achieved with the model maker(as far as I know, correct me if wrong), especifically: Need to calculate some additional metrics(ex: some obtained with coco evaluator) Need to track the metrics in tensorboard during training to compare different runs, different datasets, perform early stopping, detect under/over fitting, etc. Has anyone performed something similar? Or has any examples or ideas? submitted by /u/Sad-Anywhere-2204 [link] [comments]

I see build guides for nvidia RTX 2000, 3000 series, but none for 4000-series. Unfortunately the knowledge shared has aged rather quickly as GPUs have gotten fatter, longer, taller. Has anyone managed to fit multiple rtx4090 in a single ML server? If so, any suggestions? Thank you submitted by /u/freshairproject [link] [comments]

One-Size-Fits-Most RAG: Your RAG, Ready to Go w/ Cognita Full Article ​ https://preview.redd.it/v8vaad4vfruc1.png?width=1044&format=png&auto=webp&s=a8f6353ecc9cf2b911733bd1780ef12d17d7d826 Part 3 of the 'Cognita' Exploration Series This article is the third part of a series exploring the Cognita RAG framework. It builds upon Part 2, which provides a technical perspective, and Part 1, which explains the underlying concepts. What's This Article About? Explains how organizations can leverage Cognita to implement a comprehensive RAG framework quickly, without starting from scratch. Highlights Cognita's modular architecture, enabling organizations to scale individual components independently, ensuring seamless growth and adaptability. Why Read This Article? Highlights the challenges organizations face in extracting relevant insights from vast amounts of data, where traditional methods often fall short. Introduces the concept of "Retrieval Augmented Generation" (RAG) as a solution, and Cognita as a pre-built, open-source RAG product. Emphasizes how Cognita can revolutionize the way organizations handle and leverage data, without the need for extensive technical expertise or resources. Let's Get Cooking! Walks through the various tasks users can perform using Cognita's user-friendly interface, without technical expertise. Tasks include creating data sources, collections, data ingestion, selecting collections, configuring language models (LLMs), selecting document retrievers, writing prompts, and asking queries. Let's Set Up Provides a comprehensive step-by-step guide for setting up the Ready2DeployRAG4OrgAtScale environment. Covers prerequisites, cloning repositories, creating and activating a virtual environment, starting the backend server, installing Yarn, and setting up the frontend. ​ submitted by /u/AssistanceOk2217 [link] [comments]

Hello all, I am new to this field of image generation using transformer models. I am curious about the above two mentioned approaches. Particularly in light of this paper "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction" (results). It looks like these AR (auto-regressive) models seem to be better especially when scaled up compared to DiTs (Diffusion Transformers). Their main inference benefits seem to come from the low sampling efficiency of DiT. However, I have my doubts regarding this. Apart from the fact that major AI powerhouses have adopted diffusion models, there is a solid theory backing diffusion models being capable of generating any image distribution. Apart from that, there are no results of the above VAR paper for small models (something I am also interested in). So here are my questions: Are there any DiT distillation papers which improve the sampling efficiency? I could not find any transformer ones, all were U-Net based. Is there any theory backing AR transformer models for image generation (or for any generative task for that matter)? Is this VAR paper better purely because people haven't yet explored DiT well enough? Or AR model supremacy is what is expected? If you were to pick between the two based on potential performance (from a research point of view) and success, what would you pick and why? (I'm biased, so reasons pro-diffusion is appreciated) If you can answer any question or any opinion related to the matter please comment.. submitted by /u/InstinctsInFlow [link] [comments]

I've worked in a company in which there were different teams for DS and MLE with very distinct responsibilities. DS were responsible for talking with business stakeholders, understanding the project goals, talking with data engineers and analysts to assess data availability, do the whole data science project cycle and then deploying the model as a flask API using infrastructure built by the MLE team. However, I have seen more and more MLE jobs in which their responsibilities are much broader than that. Some of them expect MLEs to actually build the model themselves. I might be wrong here but it seems like the average ML Engineer could do everything that is expected from an average data science position, but an average data Scientist would really struggle at an average ML Engineer position. submitted by /u/CadeOCarimbo [link] [comments]