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

I recently came across this project on Mr. Bellard's website, (QEMU's creator) which is the backend behind TextSynth: "TextSynth provides access to large language or text-to-image models such as GPT-J, GPT-Neo, M2M100, CodeGen, Stable Diffusion thru a REST API and a playground. [...] TextSynth employs custom inference code to get faster inference (hence lower costs) on standard GPUs and CPUs. [...]" Here are TextSynth Server's features: "Supports many Transformer variants (GPT-J, GPT-NeoX, GPT-Neo, OPT, Fairseq GPT, M2M100, CodeGen, GPT2, T5, RWKV, LLAMA) and Stable Diffusion. Integrated REST JSON API for text completion, translation and image generation. It is used by textsynth.com. Very high performance for small and large batches on CPU and GPU. Support of dynamic batching to handle a large number of simultaneous requests. Efficient custom 8 bit and 4 bit quantization. Our quantized models are thoroughly evaluated on several standard tasks to ensure good performance. Larger models work optimally on lower cost GPUs (e.g. RTX 3090, RTX A6000) thanks to efficient quantization. All is included in a single binary. Very few external dependencies (Python is not needed) so installation is easy. Uses the LibNC library for simple tensor manipulation using the C language. Simple command line tools (ts_test, ts_sd) are provided to test the various models. The CPU version is released as binary code under the MIT license. The GPU version is commercial software. Please contact fabrice at bellard dot org for the exact terms." Now a benchmark table. I hope the formatting isn't messed up. CPU: the speed is measured on an AMD Epyc 7313 CPU using 8 threads (ts_test -T 8). 100 tokens are generated. GPU: the speed is measured on a RTX A6000 GPU. 100 tokens are generated. Model CPU Speed (tokens/s) GPU Speed (tokens/s) gptj_6B_q8 12.6 84.2 gptneox_20B_q4 4.3 40.8 gptneox_20B_q8 3.5 27.4 llama_65B_q4 1.4 13.8 Sounds like a great, useful project. I am not associated with Fabrice Bellard. submitted by /u/Art10001 [link] [comments]

Hi all, Stable Diffusion has been shown to be capable of amazing text -> image generations. If the image generates an object or character in the scene, is it possible to create a new scene that contains the same object? For example, creating an OC character in one image, and then generating a series of images where the same character is doing a series of different tasks. Alternatively, this could be a playground, and then pictures of the same playground but destroyed, or filled with kids. This is different from variations since I would want the same object in the same style but in different contexts. To some extent I believe this idea of generation consistency could be applied to making consistent UIs or graphic content. I was wondering if there were any papers and/or repos that tackle this issue. Thanks all in advance! submitted by /u/Macetodaface [link] [comments]

I apologize if what I'm about to say sounds trivial, but I recently trained the 7b version of llama on my json dataset containing 122k questions and answers. The results were quite good, but I noticed that about 30% of the answers could be improved. I've heard that the 65b model is significantly better, so I'm interested in training it to see how it performs. I already tried Google Colab (high-ram), Paperspace, Deepnote, and Jetbrains, and all crashed. I'm wondering how I can realistically train the 65b model with my $1k budget and complete the training process without any major issues? Any advice is appreciated. submitted by /u/Business-Lead2679 [link] [comments]

I am surprisingly having trouble finding comparisons between beam sizes. Large papers seem to only declare what beam size they use, but I would love to see some sort of graph/data that shows the trend of accuracy as beam size increases. My intuition would be that large beam sizes would be extremely beneficial for difficult problems, but obviously also extremely computationally expensive. submitted by /u/BadassGhost [link] [comments]

imaginary.dev is a project I built to allow web developers to use GPT to easily add AI features to their existing web user interfaces. All a developer does is declares a function prototype in TypeScript with a good comment saying what the function should do, and then they can call the function from other TypeScript and JavaScript code, even though they've never implemented the function in question. It looks something like: /** * This function takes in a blog post text and returns at least 5 good titles for the blog post. * The titles should be snappy and interesting and entice people to click on the blog post. * * @param blogPostText - string with the blog post text * @returns an array of at least 5 good, enticing titles for the blog post. * * @imaginary */ declare function titleForBlogPost(blogPostText: string): Promise<Array<string>>; Under the covers, we've written a TypeScript and Babel plugin that replaces these "imaginary function" declarations with runtime calls to GPT asking GPT what the theoretical function would return for a particular set of inputs. So it's not using GPT to write code (like CoPilot or Ghostwriter); it's using GPT to act as the runtime. This gives you freedom to implement things that you could never do in traditional programming: classification, extraction of structured information out of human language, translation, spell checking, creative generation, etc. Here's a screencast where I show off adding intelligent features to a simple blog post web app: https://www.loom.com/share/b367f4863fe843998270121131ae04d9 Let me know what you think. Is this useful? Is this something you think you'd enjoy using? Is this a good direction to take web development? Happy to hear any and all feedback! submitted by /u/xander76 [link] [comments]

Hello, I am working on a project in which I'm detecting cavities in X-rays. The dataset I have is pretty limited (~100 images). Each X-ray has a black and white mask that shows where in the image are the cavities. I'm trying to improve my results. What I've tried so far: different loss functions: BCE, dice loss, bce+dice, tversky loss, focal tversky loss modifying the images' gamma to make the cavities more visible trying out different U-Nets: U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET None of the new U-nets that I've tried improved the results. Probably because they are more suited for a larger dataset. I'm now looking for other things to try to improve my results. Currently my network is detecting cavities, but it has trouble with the smaller ones. submitted by /u/viertys [link] [comments]

Hi, I have a bunch of videos (about 1000) that contain various people, about 300 people. I want to build an app that allows me to build a catalogue of the people that appear in every video, based on their faces, so that i can just ask, "In which videos does x person appear?" or "Which people appear in this video?" Are there any projects that already do this? If not, what would be the best libraries to achieve this? submitted by /u/Chance-Specialist132 [link] [comments]

This is a guest post by Neslihan Erdogan, Global Industrial IT Manager at HAYAT HOLDING. With the ongoing digitization of the manufacturing processes and Industry 4.0, there is enormous potential to use machine learning (ML) for quality prediction. Process manufacturing is a production method that uses formulas or recipes to produce goods by combining ingredients

This post is related to the casual/conversational use of LLMs. It mostly applies to developers of novelty LLM apps but the conversation about user immersion could apply to serious machine learning circles as well. DISCUSSION IDEA Overlaying an LLM response onto a relevant GIF can be a low-cost method to increase user immersion It could be thought of as similar to attaching a TTS voice to the response. It makes it a little more human, like someone sending you memes. The proposed workflow for this is below: WORKFLOW Instruct the LLM to follow a strict response template of Relevant GIF name, Reply Extract GIF name and Reply separate Feed GIF name into a search (Giphy in this case) and return the first result Split GIF into frames Overlay Reply into each frame and combine into the final GIF Users can reply using text but the LLM retains the context of the conversation and returns appropriate GIFs overlain with corresponding text. Once text-to-video and VR develops further, user immersion would go to a whole new level - GIFs could be a useful intermediary. submitted by /u/Microsofte [link] [comments]

On November 30, 2021, we announced the general availability of Amazon SageMaker Canvas, a visual point-and-click interface that enables business analysts to generate highly accurate machine learning (ML) predictions without having to write a single line of code. With Canvas, you can take ML mainstream throughout your organization so business analysts without data science or

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The United Nations (UN) was founded in 1945 by 51 original Member States committed to maintaining international peace and security, developing friendly relations among nations, and promoting social progress, better living standards, and human rights. The UN is currently made up of 193 Member States and has evolved over the years to keep pace with

Hey guys, I want to train any LLM on my company’s data we have stored in Azure and Snowflake It’s all in tabular form, and I was wondering how can I train an LLM on the data, and be able to ask it questions about it. No computations required from the model, but at least be able to tell answer questions such as: What was Apple’s return compared to it’s sector last month ( we have financial data) - is it possible to train an LLM to understand tabluar data - is it possible to train it on Snowflake/Azure Any help or links would be appreciated! submitted by /u/jaxolingo [link] [comments]

Hello what are we talking in term of diminishing returns between the 2 models ? do the 65b really improve a lot ? bonus question: how to train the 7b model to learn specific field on my computer ? (makin it tailored to my needs) submitted by /u/deck4242 [link] [comments]

I have been trying to find a nice tech stack I like for designing and running machine learning models, and currently I'm trying out mlflow, hydra, and optuna. However, hydra seems to have several limitations that are really annoying and are making me reconsider my choice. Most problematic is the inability to group parameters together in a multirun. Hydra only supports trying all combinations of parameters, as described in https://github.com/facebookresearch/hydra/issues/1258, which does not seem to be a priority for hydra. Furthermore, hydras optuna optimizer implementation does not allow for early pruning of bad runs, which while not a deal breaker is definitely a nice to have feature. What I do like about hydra is their ability to combine config yaml, using defaults. So does anyone have any good alternatives or suggestions for how to fix this or what to switch to? submitted by /u/alyflex [link] [comments]

I've seen posts claiming this. There is a paper saying that self-scrutiny feedback loops can improve the performance of GPT-4 by 30%. I've experimented with feedback loops using the API and don't doubt that this can, or in future may be able to, produce emergent behaviour. I'm no expert but my surface-level understanding of transformers is that they would not create feedback loops just from prompting and would merely just respond as if they were. If it were true, it would have significant economical implications since creating the feedback loop separately multiplies the price each loop. submitted by /u/RedditPolluter [link] [comments]

Systems vision science combines computational, behavioral, and neuroscience methods to discover functions and algorithms for vision in various brain regions and their implementations in neural circuits. Should be interesting for computer vision/machine learning researchers interested in learning about biological vision submitted by /u/retinex [link] [comments]

Hi All, We are a team of data analyst. We are doing a survey on the problems data scientists face while doing their work. Please comment if you have anything to share from your experience. In our personal work, we have found that sharing our work across our team and saving a history of our work progress is challenging. Thanks submitted by /u/lightversetech [link] [comments]

Happy to introduce our latest work on Panoptic Segmentation: YOSO. And to the best of our knowledge, YOSO is the first read-time panoptic segmentation framework that delivers competitive performance compared to the state-of-the-art models. The code is available here: https://github.com/hujiecpp/YOSO Specifically, YOSO achieves 46.4 PQ, 45.5 FPS on COCO; 52.5 PQ, 22.6 FPS on Cityscapes; 38.0 PQ, 35.4 FPS on ADE20k and 34.1 PQ 7.1 FPS on Mapillary Vistas. ​ https://preview.redd.it/5bmcl1n6bmqa1.png?width=1362&format=png&auto=webp&s=7394a0abdfa31da1bebe7c2dc9cf18abde73db47 https://preview.redd.it/o4pkvri7bmqa1.png?width=674&format=png&auto=webp&s=118af84ba363c0ebcbf0ff21bf8c528d446688e6 https://preview.redd.it/ufdaa028bmqa1.png?width=681&format=png&auto=webp&s=f22f5d667ca91e4499a936c87aabb2fa023fff4e https://preview.redd.it/y2t2upj8bmqa1.png?width=603&format=png&auto=webp&s=d169d49c81fa8a00e6cddc12a36d453764362d5c https://preview.redd.it/3wiyep09bmqa1.png?width=597&format=png&auto=webp&s=b4beb8e8983ec5d750b78c79f0c775db3b49e427 https://preview.redd.it/g1lhwce9bmqa1.png?width=595&format=png&auto=webp&s=470a519abe02ab7022699a6d68580b252072b388 ​ submitted by /u/Technical-Vast1314 [link] [comments]

Hello everyone, I am currently working on a research paper that explores the integration of AI-aided immune tweening with permafrost immunity. Permafrost melting has released ancient and novel pathogens that we have no cures for, posing a significant threat to public health. As a way of identifying these viruses, we have discovered that AI can perform fractal analysis called fractalomics, which helps us understand their shapes. However, I am seeking more specific AI-related topics to look into and any related papers or researchers. Additionally, any related ideas of integrating AI with microscopic biology and predictions of patterns would be helpful in understanding the impact of permafrost melting on public health. If anyone has any insights or knowledge on the use of AI in identifying and characterizing immuno-regulatory pathway sequences or related topics, please feel free to share. Any information related to permafrost immunity and its impact on immune surveillance, oral pathology, and public health would also be appreciated. Thank you for your help! submitted by /u/souper-nerd [link] [comments]

Lightning AI released Lit-LLaMa: an architecture based on Meta’s LLaMa but with a more permissive license. However, they still rely on the weights trained by Meta, which have a license restricting commercial usage. Is developing the architecture enough to change the license associated with the model’s weights? submitted by /u/murphwalker [link] [comments]

Project Link: https://github.com/comet-ml/kangas 5 months ago, I shared the initial release of Kangas, a new open source EDA tool that my colleagues and I were working on, here in r/MachineLearning. After collecting feedback from some community members here, and from various other Kangas users, we've finally released version 2.0! Kangas is a tool for viewing and exploring large tables of multimedia data. It allows you to ingest large tables of data—from dataframes, csv's, or other sources—via Kangas' Python library, and construct a data structure we call a DataGrid. From the DataGrid object, you can perform a variety of queries and operations, including rendering the Kangas UI by running DataGrid.show() ​ https://i.redd.it/fj3gvlbo5jqa1.gif We've focused on a handful of features with release 2.0, including: Built-in CV support. Kangas has out-of-the-box support for a variety of image metadata, including bounding boxes, masks, and annotations. In the above GIF, you can see some of the built-in visualizations that the Kangas UI provides without any extra setup. Scalability. DataGrids are actually SQLite databases under the hood, served by a Flask server and rendered via React Server Components using Next.js. As a result, Kangas isn't bound by the same memory constraints as other libraries, and can render large quantities of data quickly regardless of environment. Interoperability. Kangas can run as a standalone application, within a Jupyter notebook, or can be deployed on a server (as we've done at https://kangas.comet.com). Additionally, Kangas has built-in integrations with HuggingFace and Comet to allow you to easily import and export datasets. In the future, we are planning on a more robust set of default integrations. Customizability. You can create custom filters using a simple Python syntax, resize and reorder columns in whatever order you want, and perform complex querying logic directly from the Kangas UI. In the near future, we also plan to support completely custom visualizations for user-defined datatypes. We're incredibly grateful to the community here for all the feedback and support you've offered so far. It's been very helpful to us in setting our roadmap, and motivating our continued work. If you're curious about Kangas, please take it for a spin by running any of the Colab notebooks linked in the project README, or by visiting https://kangas.comet.com, where we've deployed a demo app. If you have any questions or feedback, I'd love to hear it! And thank you again for all your help so far. submitted by /u/calebkaiser [link] [comments]

I am not comfortable with idea that the codes I write will be logged into their server. Is there any alternate to wandb which can be hosted locally in my machine or in a common server where a team of people can collaborate ? submitted by /u/frodo_mavinchotil [link] [comments]

Predictive maintenance is a data-driven maintenance strategy for monitoring industrial assets in order to detect anomalies in equipment operations and health that could lead to equipment failures. Through proactive monitoring of an asset’s condition, maintenance personnel can be alerted before issues occur, thereby avoiding costly unplanned downtime, which in turn leads to an increase in

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Hey all! Recently, researchers from OpenAI proposed consistency models, a new family of generative models. It allows us to generate high quality images in a single forward pass, just like good-old GANs and VAEs. ​ training progress on cifar10 ​ I have been working on it and found it definetly works! You can try it with diffusers. import diffusers from diffusers import DiffusionPipeline pipeline = DiffusionPipeline.from_pretrained( "consistency/cifar10-32-demo", custom_pipeline="consistency/pipeline", ) pipeline().images[0] # Super Fast Generation! 🤯 pipeline(steps=5).images[0] # More steps for sample quality It would be fascinating if we could train these models on different datasets and share our results and ideas! 🤗 So, I've made a simple library called consistency that makes it easy to train your own consistency models and publish them. You can check it out here: https://github.com/junhsss/consistency-models I would appreciate any feedback you could provide! submitted by /u/Beautiful-Gur-9456 [link] [comments]

GPT-4 and professional benchmarks: the wrong answer to the wrong question OpenAI may have tested on the training data. Besides, human benchmarks are meaningless for bots. Problem 1: training data contamination To benchmark GPT-4’s coding ability, OpenAI evaluated it on problems from Codeforces, a website that hosts coding competitions. Surprisingly, Horace He pointed out that GPT-4 solved 10/10 pre-2021 problems and 0/10 recent problems in the easy category. The training data cutoff for GPT-4 is September 2021. This strongly suggests that the model is able to memorize solutions from its training set — or at least partly memorize them, enough that it can fill in what it can’t recall. As further evidence for this hypothesis, we tested it on Codeforces problems from different times in 2021. We found that it could regularly solve problems in the easy category before September 5, but none of the problems after September 12. In fact, we can definitively show that it has memorized problems in its training set: when prompted with the title of a Codeforces problem, GPT-4 includes a link to the exact contest where the problem appears (and the round number is almost correct: it is off by one). Note that GPT-4 cannot access the Internet, so memorization is the only explanation. submitted by /u/Balance- [link] [comments]

Hi all, I recently read this reddit post about a 2D modeler experiencing an existential crisis about their job being disrupted by midjourney (HN discussion here). I can't help but feel the same as someone who has been working in the applied ML space for the past few years. Despite my background in "classical" ML, I'm feeling some anxiety about the rapid pace of LLM development and face a fear of missing out / being left behind. I'd love to get involved again in ML research apart from my day job, but one of the biggest obstacles is the fact that training most of foundational LLM research requires huge compute more than anything else [1]. I understand that there are some directions in distributing compute (https://petals.ml), or distilling existing models (https://arxiv.org/abs/2106.09685). I thought I might not be the only one being humbled by the recent advances in ChatGPT, etc. and wanted to hear how other people feel / are getting involved. -- [1] I can't help but be reminded of Sutton's description of the "bitter lesson" of modern AI research: "breakthrough progress eventually arrives by an opposing approach based on scaling computation... eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach." submitted by /u/00001746 [link] [comments]

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]

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This is joint post co-written by Leidos and AWS. Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. Leidos has partnered with AWS to develop an approach to privacy-preserving, confidential machine learning (ML) modeling where

With Amazon Rekognition Custom Labels, you can have Amazon Rekognition train a custom model for object detection or image classification specific to your business needs. For example, Rekognition Custom Labels can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected

There has been a paradigm change in the mindshare of education customers who are now willing to explore new technologies and analytics. Universities and other higher learning institutions have collected massive amounts of data over the years, and now they are exploring options to use that data for deeper insights and better educational outcomes. You

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and

Deforestation is a major concern in many tropical geographies where local rainforests are at severe risk of destruction. About 17% of the Amazon rainforest has been destroyed over the past 50 years, and some tropical ecosystems are approaching a tipping point beyond which recovery is unlikely. A key driver for deforestation is raw material extraction

Amazon SageMaker customers can view and manage their quota limits through Service Quotas. In addition, they can view near real-time utilization metrics and create Amazon CloudWatch metrics to view and programmatically query SageMaker quotas. SageMaker helps you build, train, and deploy machine learning (ML) models with ease. To learn more, refer to Getting started with

As organizations grow in size and scale, the complexities of running workloads increase, and the need to develop and operationalize processes and workflows becomes critical. Therefore, organizations have adopted technology best practices, including microservice architecture, MLOps, DevOps, and more, to improve delivery time, reduce defects, and increase employee productivity. This post introduces a best practice

This is a guest post co-written with Antony Vance from Intel. Customers are always looking for ways to improve the performance and response times of their machine learning (ML) inference workloads without increasing the cost per transaction and without sacrificing the accuracy of the results. Running ML workloads on Amazon SageMaker running Amazon Elastic Compute

Organizations use messaging platforms like Microsoft Teams to bring the right people together to securely communicate with each other and collaborate to get work done. Microsoft Teams captures invaluable organizational knowledge in the form of the information that flows through it as users collaborate. However, making this knowledge easily and securely available to users can

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. For customers who have been developing ML models on premises, such as their local desktop, they want to migrate their legacy ML models to the AWS Cloud to fully take advantage of

This post is co-written with Mahima Agarwal, Machine Learning Engineer, and Deepak Mettem, Senior Engineering Manager, at VMware Carbon Black VMware Carbon Black is a renowned security solution offering protection against the full spectrum of modern cyberattacks. With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML)

Amazon SageMaker Ground Truth Plus is a managed data labeling service that makes it easy to label data for machine learning (ML) applications. One common use case is semantic segmentation, which is a computer vision ML technique that involves assigning class labels to individual pixels in an image. For example, in video frames captured by

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Data Wrangler enables you to access data from a wide variety of popular sources (Amazon S3, Amazon Athena, Amazon Redshift, Amazon EMR and Snowflake) and over 40 other third-party sources.

In this two-part series, we demonstrate how to label and train models for 3D object detection tasks. In part 1, we discuss the dataset we’re using, as well as any preprocessing steps, to understand and label data. In part 2, we walk through how to train a model on your dataset and deploy it to

Online fraud has a widespread impact on businesses and requires an effective end-to-end strategy to detect and prevent new account fraud and account takeovers, and stop suspicious payment transactions. In this post, we show a serverless approach to detect online transaction fraud in near-real time. We show how you can apply this approach to various data streaming and event-driven architectures, depending on the desired outcome and actions to take to prevent fraud (such as alert the user about the fraud or flag the transaction for additional review).

The AI landscape is being reshaped by the rise of generative models capable of synthesizing high-quality data, such as text, images, music, and videos. The course toward democratization of AI helped to further popularize generative AI following the open-source releases for such foundation model families as BERT, T5, GPT, CLIP and, most recently, Stable Diffusion.