Long story short, a company has a budget for buying GPUs expected to fine-tune LLMs(probably 70B ones), and I have to the research to find which GPU setup is the best with respect to their budget. The budget can buy three H100 GPUs or five A100 GPUs. I tried my best but until now is not clear to me which of these setups is better. While five A100s have more VRAM, they say H100 are 2-8 times faster than A100s! I'm seeking help. Any valuable insights will be appreciated. submitted by /u/nlpbaz [link] [comments]

I have some papers in top venus myself, but whenever I sit down and be brutually honest with myself. I feel my work is good but it is just not that impactful, like one more brick in the wall. I wonder how often we can see something as impactful as "Attention is all you need" for example. submitted by /u/oddhvdfscuyg [link] [comments]

Evidentially there’s an automated component to every level of understanding… Analgoue training: Read a little about fpgas and Fourier analysis generalization of computation… leading to real-time learning signal on chip… then the concept of better than real time time coded resynthesis… further after learning scratch is pretty fast and remembering max msp and pure data… there’s must be a general purpose visual stringing patch programming (with patch design automation) that eliminates the need for me to have gruelingly entered tensorflow boilerplate for stale models. As well the original tensorflow scripting is not front and center. I’m assuming the architectures are more intuitively designed/devised than their papers convey. Does everyone use flow control visual methods? Secondly I saw some cool videos on more realistic visualization of really exotic processes for instance a totally fluid neural network (nodes by constructive interference) and am learning there are fundamental algorithms for intuitive procedures in life like a clock vs a timer has a best expression to define its relative optimality… where are all the other really cool learning machines. Does anyone use computers… does anyone use hardware at all? And if you can program with brain flow is there a community of decks who make brain aids what’s their name how do I master ml in this environment? Should we only think in a thought safe manner (hard coded values only). What’s the coolest machine I’ve never heard of and can we teach an algorithm to exploit rhyme scheme in corpus and from an language schema? Cant wait to find out more how do I pivot from old typewriter banging to state of the art methodology? HOW DO WE SAVE THE CODERS FROM WASTING VALUABLE LIFE TIME EXPERIENCE? submitted by /u/arcco96 [link] [comments]

Please share your journey and lessons. Thanks! submitted by /u/20231027 [link] [comments]

There's been a lot of discussion about benchmark contamination, where models are trained on the data they are ultimately evaluated on. For example, a recent paper showed that models performed substantially better on the public GSM8K vs GSM1K, which was a benchmark recently created by Scale AI to match GSM8K on difficulty and other measures. Because of these concerns about benchmark contamination, it is often hard to take a research lab's claims about model performance at face value. It's difficult to know whether a model gets good benchmark performance because it is generally capable or because its pre-training data was contaminated and it overfit on the benchmarks. One solution to this problem is for benchmark creators to release their datasets in stages. For example, a benchmark creator could release 50% of their dataset upon release, and then release the remaining 50% in two stages, 25% one year later and 25% two years later. This would enable model evaluators to check for benchmark contamination by comparing performance on the subset of data released prior to the training cutoff vs. the subset released after the training cutoff. It would also give us a better understanding of how well models are actually performing. One last point - this staged release process wouldn't be anywhere near as helpful for benchmarks created by scraping the web, as even the later-released data subsets could be found in the training data. But it should be useful for other kinds of benchmarks. submitted by /u/kei147 [link] [comments]

HI all I have a lot of pandas dataframes representing features that will be used to train my ML models. To provide more context: Each pandas dataframe is a collection of timeseries (1 column, 1 timeseries) created based on the combination of 5 parameters. Each of these parameters can have up to 5 different values, and one combination of parameters defines one dataframe. This means that I have approximately 2,000 dataframes with a shape of (3000, 1000). The only requirement I have is to be able to access them efficiently. I don't need to access all of them every time. I've considered using a SQL dataframe where the name of each table is the parameter combination, but perhaps there are better ways to do this. Any advice from someone who has already dealt with a similar problem? submitted by /u/Logical_Ad8570 [link] [comments]

Hey everyone, I’m Zach from Superpowered AI (YC S22). We’ve been working in the RAG space for a little over a year now, and we’ve recently decided to open-source all of our core retrieval tech. [spRAG](https://github.com/SuperpoweredAI/spRAG) is a retrieval system that’s designed to handle complex real-world queries over dense text, like legal documents and financial reports. As far as we know, it produces the most accurate and reliable results of any RAG system for these kinds of tasks. For example, on FinanceBench, which is an especially challenging open-book financial question answering benchmark, spRAG gets 83% of questions correct, compared to 19% for the vanilla RAG baseline (which uses Chroma + OpenAI Ada embeddings + LangChain). You can find more info about how it works and how to use it in the project’s README. We’re also very open to contributions. We especially need contributions around integrations (i.e. adding support for more vector DBs, embedding models, etc.) and around evaluation. Happy to answer any questions! [GitHub repo](https://github.com/SuperpoweredAI/spRAG) submitted by /u/zmccormick7 [link] [comments]

As more powerful large language models (LLMs) are used to perform a variety of tasks with greater accuracy, the number of applications and services that are being built with generative artificial intelligence (AI) is also growing. With great power comes responsibility, and organizations want to make sure that these LLMs produce responses that align with

We are excited to announce the general availability of two advanced recipes in Amazon Personalize, User-Personalization-v2 and Personalized-Ranking-v2 (v2 recipes), which are built on the cutting-edge Transformers architecture to support larger item catalogs with lower latency. In this post, we summarize the new enhancements, and guide you through the process of training a model and providing recommendations for your users.

Embeddings are integral to various natural language processing (NLP) applications, and their quality is crucial for optimal performance. They are commonly used in knowledge bases to represent textual data as dense vectors, enabling efficient similarity search and retrieval. In Retrieval Augmented Generation (RAG), embeddings are used to retrieve relevant passages from a corpus to provide

Paper just got accepted to ICML. Tbh it was a happy surprise. Unfortunately for both authors we either do not have a return visa to the US, or with high probability will not have a non-expired passport in July for the conference. I wonder if it is acceptable to pay for the conference registration fee $475, but not attending, and still have our paper published in the proceedings. I notice that conference registration does include virtual access to all the sessions and tutorials. But I am unsure about the publication part. submitted by /u/Normal-Comparison-60 [link] [comments]

I was wondering how best to solve or tackle the following problem. I want to predict the match tree of the following Euro 2024 based on past results of the national teams (from the last two years). Which methods are best suited for this? My guess would be something like RandomForest but i am really lost on how to tackle this project submitted by /u/LabSignificant6271 [link] [comments]

I'm trying to understand how tiktoken deals with smaller LLMs, but I can't find the implementation in its documentation. Let’s say we have a large model with over 16k tokens. If we have a large text with, let's say, 32k tokens, how is tiktoken cutting the document? Does it just disregard everything after the 16000th token? submitted by /u/Spaskich [link] [comments]

I work in a place where we analyze TELECOM audio. The method we use is to work with stereo audio where the attendant is played on the left side of the headphone and the client on the right side. Currently, we are receiving mono audio where the client and attendant are on both channels. I need a method to process this mono audio to make it work the way we do. I thought about using pre-trained AIs or some ready-made service, what do you suggest? Considering that we can identify the attendant by the amount of speech, in most cases, the attendant speaks more than the client. submitted by /u/TartNo9047 [link] [comments]

Hello everyone, today the ICML results are out, congratulations to all those who have papers accepted here. I'm not an academic myself, but sometimes I read papers at these conferences for work, and it's really interesting. I just have a question: why do juniors have so many papers at these conferences? I thought this was something you would have to learn throughout your 5 years of PhD and almost only achieve in the final years of your PhD. Furthermore, I've heard that to get into top PhD programs in the US, you need to have some papers beforehand. So, if a junior can publish papers early like that, why do they have to spend 5 long years pursuing a PhD? submitted by /u/ShiftStrange1701 [link] [comments]

I am using seq2seq model for predicting or correcting spellings for the product names, I do have dataset of product names with their misspelled and corrected versions(they do contain some special chars too). I have trained that data on few epochs and see some output. But i am giving the user input, it isn't predicting as expected. Then, after training the model and using this code: for seq_index in range(1, 50): input_seq = encoder_input_data[seq_index : seq_index + 1] decoded_sentence = decode_sequence(input_seq) print("-") print("Input sentence:", input_texts[seq_index]) #Print input sequence with char! print("Decoded sentence:", decoded_sentence) I have got good outputs like: Input sentence: Fluidic WorkCation Decoded sentence: Fluidic Worksation Input sentence: Li@uid Handler, Biomek FXp DuaO Decoded sentence: Liquid Handler, Biomek NXp Mult Then if i try to give a user input and let the model predict, i do get some text like this Input sentence: system Decoded sentence: 'Gamma Counter/Rotor - Water Machine System, Automated Parallell\n' which is way far on what it has learnt, but i have used the same code of encoder and decoder model. I want to know whether this seq2seq model will work for these scenarios or not first. submitted by /u/No-Purchase6293 [link] [comments]

My training images have texts that appear in various orientations on the image. As a result, I don't know what's their original orientation since for example DBNetPP does not return the bbox angles in the corners in a natural orientation order. How can I solve this issue? I have tried other pretrained detection models, but they also does not do that, maybe because they were not trained on rotated images. How can I solve this issue? https://preview.redd.it/tvq6fp9k3zxc1.png?width=1000&format=png&auto=webp&s=ecf3f3e757e6450e34c1257f9eb8e0fec4ce7bba https://preview.redd.it/yea66hdl3zxc1.png?width=1000&format=png&auto=webp&s=4eafb6d4354c6a0d851d6b5fad456f99441d9bc2 submitted by /u/tmargary [link] [comments]

Hi, when I plot a histogram of binary classifier test scores, they cluster too much in the last bar this making thresholding difficult as it becomes too discrete. Does anybody knows any methods to make sure classifier scores histogram are more evenly spreaded? The ideal would be a reliability diagram fully monotonic and as close to the identity line as possible. Tried Platt Scaling and Isotonic regression without success. Also wonder what determines the amount of possible different classifier score values? Any help would be more than welcome! submitted by /u/Loose-Event-7196 [link] [comments]

My icml submission got rejected with score 6655 , As heartbroken as I feel , what are some of the high acceptance rate conferences that I can resubmit it to ? I just wanna get it in and move on . submitted by /u/One-Blueberry4699 [link] [comments]

Hi everyone, I'm currently tasked with researching pipelines to build local custom Chatbot for my university. I have been reading about approaches like RAG, Rasa, Dialogflow and specific pipelines such as LangChain, Ragflow, KRAGEN and had some results from testing. However I want to capture the current state of which pipelines and approaches are most effective for building a Chatbot, especially in commercial settings. I'd be really thankful for your all information! submitted by /u/ghosthunterk [link] [comments]

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Hi all, I've been using Pytorch since 2019, and it has changed a lot in that time (especially since huggingface). Are there any modern guides/style-docs/example-repos you would recommend? For example, are namedtensors a good/common practice? Is Pytorch Lightning recommended? What are the best config management tools these days? How often do you use torch.script or torch.compile? submitted by /u/SirBlobfish [link] [comments]

submitted by /u/RSchaeffer [link] [comments]

Large language models (LLMs) are making a significant impact in the realm of artificial intelligence (AI). Their impressive generative abilities have led to widespread adoption across various sectors and use cases, including content generation, sentiment analysis, chatbot development, and virtual assistant technology. Llama2 by Meta is an example of an LLM offered by AWS. Llama

Not that many people are paying attention to LLM interpretability research when capabilities research is moving as fast as it currently is, but interpretability is really important and in my opinion, really interesting and exciting! Anthropic has made a lot of breakthroughs in recent months, the biggest one being "Towards Monosemanticity". The basic idea is that they found a way to train a sparse autoencoder to generate interpretable features based on transformer activations. This allows us to look at the activations of a language model during inference, and understand which parts of the model are most responsible for predicting each next token. Something that really stood out to me was that the autoencoders they train to do this are actually very small, and would not require a lot of compute to get working. This gave me the idea to try to replicate the research by training models on my M3 Macbook. After a lot of reading and experimentation, I was able to get pretty strong results! I wrote a more in-depth post about it on my blog here: https://jakeward.substack.com/p/monosemanticity-at-home-my-attempt I'm now working on a few follow-up projects using this tech, as well as a minimal implementation that can run in a Colab notebook to make it more accessible. If you read my blog, I'd love to hear any feedback! submitted by /u/neverboosh [link] [comments]

Paper: https://arxiv.org/abs/2404.19756 Code: https://github.com/KindXiaoming/pykan Quick intro: https://kindxiaoming.github.io/pykan/intro.html Documentation: https://kindxiaoming.github.io/pykan/ Abstract: Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs. https://preview.redd.it/r7vjmp31juxc1.png?width=2326&format=png&auto=webp&s=a2c722cf733510194659b9aaec24269a7f9e5d47 submitted by /u/SeawaterFlows [link] [comments]

Title basically, this was a conversation I read just recently and am now looking for the source. A specific paper was mentioned in there as well. The conclusion drawn was that we might be at the limit of what we can do with statistical models and that there's nothing special about the models themselves - only the data that's fed matters. Any pointers would be appreciated, thanks! submitted by /u/SunraysInTheStorm [link] [comments]

Imagine harnessing the power of advanced language models to understand and respond to your customers’ inquiries. Amazon Bedrock, a fully managed service providing access to such models, makes this possible. Fine-tuning large language models (LLMs) on domain-specific data supercharges tasks like answering product questions or generating relevant content. In this post, we show how Amazon

Organizations often offer support in multiple languages, saying “contact us for translations.” However, customers who don’t speak the predominant language often don’t know that translations are available or how to request them. This can lead to poor customer experience and lost business. A better approach is proactively providing information in multiple languages so customers can

Numerous customers face challenges in managing diverse data sources and seek a chatbot solution capable of orchestrating these sources to offer comprehensive answers. This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment. Amazon Bedrock is a fully managed service that offers a choice

Hey folks! I’m Jonathan from TensorDock, and we’re building a cloud GPU marketplace. We want to make GPUs truly affordable and accessible. I once started a web hosting service on self-hosted servers in middle school. But building servers isn’t the same as selling cloud. There’s a lot of open source software to manage your homelab for side projects, but there isn’t anything to commercialize that. Large cloud providers charge obscene prices — so much so that they can often pay back their hardware in under 6 months with 24x7 utilization. We are building the software that allows anyone to become the cloud. We want to get to a point where any [insert company, data center, cloud provider with excess capacity] can install our software on our nodes and make money. They might not pay back their hardware in 6 months, but they don’t need to do the grunt work — we handle support, software, payments etc. In turn, you get to access a truly independent cloud: GPUs from around the world from suppliers who compete against each other on pricing and demonstrated reliability. So far, we’ve onboarded quite a few GPUs, including 200 NVIDIA H100 SXMs available from just $2.49/hr. But we also have A100 80Gs from $1.63/hr, A6000s from $0.47/hr, A4000s from $0.13/hr, etc etc. Because we are a true marketplace, prices fluctuate with supply and demand. All are available in plain Ubuntu 22.04 or with popular ML packages preinstalled — CUDA, PyTorch, TensorFlow, etc., and all are hosted by a network of mining farms, data centers, or businesses that we’ve closely vetted. If you’re looking for hosting for your next project, give us a try! Happy to provide testing credits, just email me at [jonathan@tensordock.com](mailto:jonathan@tensordock.com). And if you do end up trying us, please provide feedback below [or directly!] 🙂 ​ Deploy a GPU VM: https://dashboard.tensordock.com/deploy CPU-only VMs: https://dashboard.tensordock.com/deploy_cpu Apply to become a host: https://tensordock.com/host submitted by /u/jonathan-lei [link] [comments]

ICML 2024 paper acceptance results are supposed to be released in 24 hours or so. I thought I might create this thread for us to discuss anything related to it. There is some noise in the reviews every year. Don’t forget that even though your paper might get rejected, this does not mean that it is not valuable work. Good luck everyone ! submitted by /u/hugotothechillz [link] [comments]

As of April 30, 2024 Amazon Q Business is generally available. Amazon Q Business is a conversational assistant powered by generative artificial intelligence (AI) that enhances workforce productivity by answering questions and completing tasks based on information in your enterprise systems. Your employees can access enterprise content securely and privately using web applications built with

In the fast-paced world of customer service, efficiency and accuracy are paramount. After each call, contact center agents often spend up to a third of the total call time summarizing the customer conversation. Additionally, manual summarization can lead to inconsistencies in the style and level of detail due to varying interpretations of note-taking guidelines. This

We believe generative artificial intelligence (AI) has the potential to transform virtually every customer experience. To make this possible, we’re rapidly innovating to provide the most comprehensive set of capabilities across the three layers of the generative AI stack. This includes the bottom layer with infrastructure to train Large Language Models (LLMs) and other Foundation

Today, we announced the General Availability of Amazon Q, the most capable generative AI powered assistant for accelerating software development and leveraging companies’ internal data. “During the preview, early indications signaled Amazon Q could help our customers’ employees become more than 80% more productive at their jobs; and with the new features we’re planning on

This is a guest post co-authored with Ville Tuulos (Co-founder and CEO) and Eddie Mattia (Data Scientist) of Outerbounds. To build a production-grade AI system today (for example, to do multilingual sentiment analysis of customer support conversations), what are the primary technical challenges? Historically, natural language processing (NLP) would be a primary research and development

This blog post is co-written with Pradeep Prabhakaran from Cohere.  Today, we are excited to announce that Cohere Command R and R+ foundation models are available through Amazon SageMaker JumpStart to deploy and run inference. Command R/R+ are the state-of-the-art retrieval augmented generation (RAG)-optimized models designed to tackle enterprise-grade workloads. In this post, we walk through how

This is a guest post by Mark McQuade, Malikeh Ehghaghi, and Shamane Siri from Arcee. In recent years, large language models (LLMs) have gained attention for their effectiveness, leading various industries to adapt general LLMs to their data for improved results, making efficient training and hardware availability crucial. At Arcee, we focus primarily on enhancing

Today, we are excited to announce that the DBRX model, an open, general-purpose large language model (LLM) developed by Databricks, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. The DBRX LLM employs a fine-grained mixture-of-experts (MoE) architecture, pre-trained on 12 trillion tokens of carefully curated data and

At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG). In previous posts, we covered new capabilities like hybrid search support, metadata filtering

Speaker diarization, an essential process in audio analysis, segments an audio file based on speaker identity. This post delves into integrating Hugging Face’s PyAnnote for speaker diarization with Amazon SageMaker asynchronous endpoints. We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud.

Organizations across industries are using automatic text summarization to more efficiently handle vast amounts of information and make better decisions. In the financial sector, investment banks condense earnings reports down to key takeaways to rapidly analyze quarterly performance. Media companies use summarization to monitor news and social media so journalists can quickly write stories on

Conversational artificial intelligence (AI) assistants are engineered to provide precise, real-time responses through intelligent routing of queries to the most suitable AI functions. With AWS generative AI services like Amazon Bedrock, developers can create systems that expertly manage and respond to user requests. Amazon Bedrock is a fully managed service that offers a choice of

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The team navigates a large volume of documents and locates the right information to make sure the warehouse design meets the highest standards. In the post A generative AI-powered solution on Amazon SageMaker to help Amazon EU

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]