From the Llama3.1 paper, the vocabulary consists of 128k tokens, with 100k dedicated to English and 28k allocated for non-English languages. Given that languages like Korean, Chinese, and Japanese do not use the 26-letter Latin alphabet and can have millions of unique characters, how is it possible to cover these languages with just 28k tokens? Is the tokenization based on Unicode? submitted by /u/Financial_Air5256 [link] [comments]

Hi. I'm a high school student and is interested in writing a research paper (for IB DP) on LLM but I can't decide on what topic precisely. So far my progress is I have familliarise myself somewhat with PyTorch, Weights and Biases, Accelerate and Huggingface, etc. I had followed and finished the book from O'reilly on HF transformers. And as seen below, using 4x Nvidia A100 80GB PCIe. I had managed to train a 111 million parameter LLM from scratch which generates python code (based on codeparrot) using a decoder architecture. https://huggingface.co/shng2025/gptesla-small The issue stands that I can't decide on what to research on now, as in what independent variables to vary for my research paper that is very interesting and has some level of depth for me to analyse on. I am now following Andrej Karparthy's Zero to Hero course on GPT and is close to finishing it as well. Preferably I want to research on something that involes training a language model (decoder architecture) from scratch, as so I could showcase the build up of my skill till now to the examiners, etc. if possible. Current directions: I looked into researching about attention which would be interesting, but would this be too cliche? Idk. (This one is very much implementable already since Andrej directly covered it for example. But I feel like maybe it won't play that much heavy of a role in training a large language model? I mean it will but it's not really something new to research on) I also contemplated on researching on layer normalisation, and is wondering (need help for this one) whether is there even any papers or exemplars out there for me to follow through to implement on? I am currently thinking to look into investigating on effects of different placements of the normalisation layer in a decoder architecture. Such as pre-normalisation vs post-normalisation. Can you guys help me out on this? I also have an alternative potential idea which is to (don't even think this is implementable but...) maybe try and implement layer normalisation to be introduced at different times during training and see maybe this would make the training session converge faster? Not sure if this is possible. I hope you guys can also try and suggest me alternatives directions that's interesting to explore. And any resources, course, or code of implementation also help. Advice also help too! Thx. Sorry if this is a long post. submitted by /u/EVM_E110 [link] [comments]

It seems like RAG is the de-facto standard of question answering in the industry. What's the alternative? submitted by /u/clocker2004 [link] [comments]

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For example comparing LLaMA-3 to GPT-4. To me it doesn't make sense because it feels like comparing a model to an actual product. A product being a model with a bunch of other things that have been applied (e.g., pre-/post-processing techniques). submitted by /u/Seankala [link] [comments]

I have a dataframe with 30 rows and 10 columns. 5 of the columns are input features and the other 5 are output/target columns. The target columns contain classes represented as 0, 1, 2. I want to split the dataset into train and test such that, in the train set, for each output column, the proportion of class 1 is between 0.15 and 0.3. (I am not bothered about the distribution of classes in the test set). ADDITIONAL CONTEXT: I am trying to balance the output classes in a multi-class and multi-output dataset. My understanding is that this would be an optimization problem with 25 (?) degrees of freedom. So if I have any input dataset, I would be able to create a subset of that input dataset which is my training data and which has the desired class balance (i.e class 1 between 0.15 and 0.3 for each output column). I make the dataframe using this import pandas as pd import numpy as np from sklearn.model_selection import train_test_split np.random.seed(42) data = pd.DataFrame({ 'A': np.random.rand(30), 'B': np.random.rand(30), 'C': np.random.rand(30), 'D': np.random.rand(30), 'E': np.random.rand(30), 'F': np.random.choice([0, 1, 2], 30), 'G': np.random.choice([0, 1, 2], 30), 'H': np.random.choice([0, 1, 2], 30), 'I': np.random.choice([0, 1, 2], 30), 'J': np.random.choice([0, 1, 2], 30) }) My current silly/harebrained solution for this problem involves using two separate functions. I have a helper function that checks if the proportions of class 1 in each column is within my desired range def check_proportions(df, cols, min_prop = 0.15, max_prop = 0.3, class_category = 1): for col in cols: prop = (df[col] == class_category).mean() if not (min_prop <= prop <= max_prop): return False return True def proportionately_split_data(data, target_cols, min_prop = 0.15, max_prop = 0.3): while True: random_state = np.random.randint(100_000) train_df, test_df = train_test_split(data, test_size = 0.3, random_state = random_state) if check_proportions(train_df, target_cols, min_prop, max_prop): return train_df, test_df Finally, I run the code using target_cols = ["F", "G", "H", "I", "J"] train, test = proportionately_split_data(data, target_cols) My worry with this current "solution" is that it is probabilistic and not deterministic. I can see the proportionately_split_data getting stuck in an infinite loop if none of the random state I set in train_test_split can randomly generate data with the desired proportion. Any help would be much appreciated! I apologize for not providing this earlier, for a Minimal working example, the input (data) could be A B C D E OUTPUT_1 OUTPUT_2 OUTPUT_3 OUTPUT_4 OUTPUT_5 5.65 3.56 0.94 9.23 6.43 0 1 1 0 1 7.43 3.95 1.24 7.22 2.66 0 0 0 1 2 9.31 2.42 2.91 2.64 6.28 2 1 2 2 0 8.19 5.12 1.32 3.12 8.41 1 2 0 1 2 9.35 1.92 3.12 4.13 3.14 0 1 1 0 1 8.43 9.72 7.23 8.29 9.18 1 0 0 2 2 4.32 2.12 3.84 9.42 8.19 0 0 0 0 0 3.92 3.91 2.90 8.19 8.41 2 2 2 2 1 7.89 1.92 4.12 8.19 7.28 1 1 2 0 2 5.21 2.42 3.10 0.31 1.31 2 0 1 1 0 which has 10 rows and 10 columns, and an expected output (train set) could be A B C D E OUTPUT_1 OUTPUT_2 OUTPUT_3 OUTPUT_4 OUTPUT_5 5.65 3.56 0.94 9.23 6.43 0 1 1 0 1 7.43 3.95 1.24 7.22 2.66 0 0 0 1 2 9.31 2.42 2.91 2.64 6.28 2 1 2 2 0 8.19 5.12 1.32 3.12 8.41 1 2 0 1 2 8.43 9.72 7.23 8.29 9.18 1 0 0 2 2 3.92 3.91 2.90 8.19 8.41 2 2 2 2 1 5.21 2.42 3.10 0.31 1.31 2 0 1 1 0 Whereby each output column in the train set has at least 2 (>= 0.15 * number of rows in input data) instances of Class 1 and at most 3 (<= 0.3 * number of rows in input data). I guess I also didn't clarify that the proportion is in relation to the number of examples (or rows) in the input dataset. My test set would be the remaining rows in the input dataset. submitted by /u/Individual_Ad_1214 [link] [comments]

Hi, I'm interested in leveraging the Stable Diffusion v1 model as a feature extractor for a downstream task. Specifically, I want to do this without involving the text prompt mechanism. To achieve this, I'm considering: Eliminating the text encoder module Removing the cross-attention layers in the UNet Is this a feasible approach? Has anyone experimented with using Stable Diffusion in this way? What would be the challenges or limitations of such a modification? I can find papers that use SDv1 for downstream tasks along with a basic text prompt but none try to do it without involving the text prompt mechanism. Otherwise, I am considering to use DINOv2. submitted by /u/rustyelectron [link] [comments]

If I can get a good deal on a Siena Epyc CPU 32 or 64 core with say 256GB RAM is that a decent starting point to get into a local setup to run models? I need the lanes for the GPUs. submitted by /u/Kltpzyxmm [link] [comments]

I submitted to the ACL rolling review in June and found the reviewers' evaluation scores very subjective. Although the ACL committee has an instruction on some basic reviewing guidelines, there lacks of a preliminary test for the reviewers to explicitly show the evaluation standards. Maybe we should provide some paper-score examples to prompt the reviewers for more objective reviews? Or build a test before they make reviews to make sure they fully understand the meaning of soundness and overall assessment, rather than giving some random scores based on their personal interests. submitted by /u/Spico197 [link] [comments]

I assume that all the training data is in JSON format, but higher temperature or other randomness during generation doesn’t guarantee that the outputs will always be in JSON. What other methods do you think could ensure that the outputs are consistently in JSON? Perhaps some rule-based methods during decoding could help? submitted by /u/Financial_Air5256 [link] [comments]

I tried quantizing my finetuned mT5 model using the optimum’s openvino wrapper to int8 and int4. There was very little difference in the inference time, close to 5%. This makes me wonder if it’s an issue with hardware. I’m using intel sapphire rapids and it has an avx512_vnni instruction set. How did I figure out if it supports int4? And why and why not? submitted by /u/Abs0lute_Jeer0 [link] [comments]

After the first theoretical issue with my transformer, I now see another. The original paper uses normalization after residual addition (Post-LN), which led to training difficulties and later got replaced by normalization at the beginning of each attention or mlp block/branch (Pre-LN). This is known to work better in practice (trainable without warmup, restores highway effect), but it still doesn't seem completely ok theoretically. First consider things without normalization. Assuming attention and mlp blocks are properly set up and mostly keep norms, each residual addition would sum two similar norm signals, potentially scaling up by something like 1.4 (depending on correlation, but it starts at sqrt(2) after random init). So the norms after the blocks could look like this: [1(main)+1(residual)=1.4] -> [1.4+1.4=2] -> [2+2=2.8] etc. This would cause various problems (like changing the softmax temp in later attention blocks), so adjustment is needed. Pre-LN ensures each block works on normalized values (thus with constant - if slightly arbitrary - softmax temperature). But since it doesn't affect the norm of the main signal (as forwarded by the skip connection) but only the residual, the norms can still grow, albeit slower. The expectation is now roughly: [1+1=1.4] -> [1.4+1=1.7] -> [1.7+1=2] -> [2+1=2.2] etc - with a final normalization correcting the signal near output (Pre-LN paper). One possible issue with this is that later attention blocks may have reduced effect, as they add unit norm residuals to a potentially larger and larger main signal. What is the usual take on this problem? Can it be ignored in practice? Does Pre-LN work acceptably despite it, even for deep models (where the main norm discrepancy can grow larger)? There are lots of alternative normalization papers, but what is the practical consensus? Btw attention is extremely norm-sensitive (or, equivalently, the hidden temperature of softmax is critical). This is a sharp contrast to fc or convolution which are mostly scale-oblivious. For anybody interested: consider what happens when most raw attention dot products come out 0 (= query and key is orthogonal, no info from this context slot) with only one slot giving 1 (= positive affinity, after downscaled by sqrt(qk_siz) ). I for one got surprised by this during debug. submitted by /u/lostn4d [link] [comments]

Hi, I am a 2nd year PhD student in CS. My supervisor just got this idea about MoEs and fairness and asked me to implement it ( work on a toy classification problem on tabular data and NOT language data). However as it is not their area of expertise, they did not give any guidelines on how to approach it. My main question is: How do I search for or proceed with implementing a mixture of expert models? The ones that I find are for chatting and such but I mainly work with tabular EHR data. This is my first foray into this area (LLMs and MoEs) and I am kind of lost with all these Mixtral, openMoE, etc. As we do not have access to Google Collab or have powerful GPUs I have to rely on local training (My lab PC has 2080ti and my laptop has 4070). Any guideline or starting point on how to proceed would be greatly appreciated. submitted by /u/Furiousguy79 [link] [comments]

Today, we are excited to announce a new capability in Amazon SageMaker inference that can help you reduce the time it takes for your generative artificial intelligence (AI) models to scale automatically. You can now use sub-minute metrics and significantly reduce overall scaling latency for generative AI models. With this enhancement, you can improve the

My data is a bit complicated to describe so I'm going try to describe something analogous. Each example is randomly generated, but you can group them based on a specific but latent (by latent I mean this isn't added into the features used to develop a model, but I have access to it) feature (in this example we'll call this number of bedrooms). Feature x1 Feature x2 Feature x3 ... Output (Rent) Row 1 Row 2 Row 3 Row 4 Row 5 Row 6 Row 7 2 Row 8 1 Row 9 0 So I can group Row 1, Row 2, and Row 3 based on a latent feature called number of bedrooms (which in this case is 0 bedroom). Similarly, Row 4, Row 5, & Row 6 have 2 Bedrooms, and Row 7, Row 8, & Row 9 have 4 Bedrooms. Furthermore, these groups also have an optimum price which is used to create output classes (output here is Rent; increase, keep constant, or decrease). So say the optimum price for the 4 bedrooms group is $3mil, and row 7 has a price of $4mil (=> 3 - 4 = -1 mil, i.e a -ve value so convert this to class 2, or above optimum or increase rent), row 8 has a price of $3mil (=> 3 - 3 = 0, convert this to class 1, or at optimum), and row 9 has a price of $2mil (3 - 2 = 1, i.e +ve value, so convert this to class 0, or below optimum, or decrease rent). I use this method to create an output class for each example in the dataset (essentially, if example x has y number of bedrooms, I get the known optimum price for that number of bedrooms and I subtract the example's price from the optimum price). Say I have 10 features (e.g. square footage, number of bathrooms, parking spaces etc.) in the dataset, these 10 features provide the model with enough information to figure out the "number of bedrooms". So when I am evaluating the model, feature x1 feature x2 feature x3 ... Row 10 e.g. I pass into the model a test example (Row 10) which I know has 4 bedrooms and is priced at $6mil, the model can accurately predict class 2 (i.e increase rent) for this example. Because the model was developed using data with a representative number of bedrooms in my dataset. Features.... Output (Rent) Row 1 0 Row 2 0 Row 3 0 However, my problem arises at examples with a low number of bedrooms (i.e. 0 bedrooms). The input features doesn't have enough information to determine the number of bedrooms for examples with a low number of bedrooms (which is fine because we assume that within this group, we will always decrease the rent, so we set the optimum price to say $2000. So row 1 price could be $8000, (8000 - 2000 = 6000, +ve value thus convert to class 0 or below optimum/decrease rent). And within this group we rely on the class balance to help the model learn to make predictions because the proportion is heavily skewed towards class 0 (say 95% = class 0 or decrease rent, and 5 % = class 1 or class 2). We do this based the domain knowledge of the data (so in this case, we would always decrease the rent because no one wants to live in a house with 0 bedrooms). MAIN QUESTION: We now want to predict (or undertake inference) for examples with number of bedrooms in between 0 bedrooms and 2 bedrooms (e.g 1 bedroom NOTE: our training data has no example with 1 bedroom). What I notice is that the model's predictions on examples with 1 bedroom act as if these examples had 0 bedrooms and it mostly predicts class 0. My question is, apart from specifically including examples with 1 bedroom in my input data, is there any other way (more statistics or ML related way) for me to improve the ability of my model to generalise on unseen data? submitted by /u/Individual_Ad_1214 [link] [comments]

EMNLP paper review scores Overall assessment for my paper is 2, 2.5 and 3. Is there any chance that it may still be selected? The confidence is 2, 2.5 and 3. The soundness is 2, 2.5, 3.5. I am not sure how soundness and confidence may affect my paper's selection. Pls explain how this works. Which metrics should I consider important. Thank you! submitted by /u/Immediate-Hour-8466 [link] [comments]

https://openai.com/index/searchgpt-prototype/ We’re testing SearchGPT, a temporary prototype of new AI search features that give you fast and timely answers with clear and relevant sources. submitted by /u/we_are_mammals [link] [comments]

Amazon Q Business is a fully managed, generative artificial intelligence (AI)-powered assistant that helps enterprises unlock the value of their data and knowledge. With Amazon Q, you can quickly find answers to questions, generate summaries and content, and complete tasks by using the information and expertise stored across your company’s various data sources and enterprise

As conversational artificial intelligence (AI) agents gain traction across industries, providing reliability and consistency is crucial for delivering seamless and trustworthy user experiences. However, the dynamic and conversational nature of these interactions makes traditional testing and evaluation methods challenging. Conversational AI agents also encompass multiple layers, from Retrieval Augmented Generation (RAG) to function-calling mechanisms that

In the post, we introduce the AWS Neuron node problem detector and recovery DaemonSet for AWS Trainium and AWS Inferentia on Amazon Elastic Kubernetes Service (Amazon EKS). This component can quickly detect rare occurrences of issues when Neuron devices fail by tailing monitoring logs. It marks the worker nodes in a defective Neuron device as unhealthy, and promptly replaces them with new worker nodes. By accelerating the speed of issue detection and remediation, it increases the reliability of your ML training and reduces the wasted time and cost due to hardware failure.

I built a simple starter demo of a Knowledge Question and Answering System using Llama 3.1 (8B GGUF) and Marqo. Feel free to experiment and build on top of this yourselves! GitHub: https://github.com/ellie-sleightholm/marqo-llama3_1 submitted by /u/elliesleight [link] [comments]

https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/ They solved 4 of the 6 IMO problems (although it took days to solve some of them). This would have gotten them a score of 28/42, just one point below the gold-medal level. submitted by /u/we_are_mammals [link] [comments]

Hi community, I am exploring the Responsible AI domain where I have started reading about methods and tools to make Deep Learning Models explainable. I have already used SHAP and LIMe for ML model explainability. However, I am unsure about their use in explaining LLMs. I know that these methods are model agnostic but can we use these methods for Text Generation or Summarization tasks? I got reference docs from Shap explaining GPT2 for text generation tasks, but I am unsure about using it for other newer LLMs. Additionally, I would like to know, are there any better ways for Explainable AI for LLMs? submitted by /u/PhoenixHeadshot25 [link] [comments]

What is the standard way to model high-dimensional stochastic processes today? I have some process defined over images x, and I would like to compute P(x' | x, z) for all x'. I know there are Normalizing Flows, Gaussian Processes, etc, but I do not know which to get started with. I specifically want to compute the probabilities, not just sample some x' ~ P(x, z). submitted by /u/smorad [link] [comments]

Happy to share our recent paper, where we demonstrate that LLMs exhibit surprising agreement on purely imaginary and hallucinated contents -- what we call a "shared imagination space". To arrive at this conclusion, we ask LLMs to generate questions on hypothetical contents (e.g., a made-up concept in physics) and then find that they can answer each other's (unanswerable and nonsensical) questions with much higher accuracy than random chance. From this, we investigate in multiple directions on its emergence, generality and possible reasons, and given such consistent hallucination and imagination behavior across modern LLMs, discuss implications to hallucination detection and computational creativity. Link to the paper: https://arxiv.org/abs/2407.16604 Link to the tweet with result summary and highlight: https://x.com/YilunZhou/status/1816371178501476473 Please feel free to ask any questions! The main experiment setup and finding. submitted by /u/zyl1024 [link] [comments]

For people working on information verification in general, for instance, working on fact checking, fake news detection or even using RAG from news articles this paper may be useful. Authors use different reinforcement learning techniques to estimate reliability values of news media outlets based on how they interact on the web. The method is easy to scale since the source code is available to build larger hyperlink-based interaction graphs from Common Crawl News. Authors also released the computed values and dataset with news media reliability annotation: Github repo: https://github.com/idiap/News-Media-Reliability Paper: https://aclanthology.org/2024.naacl-long.383/ Live Demo Example: https://lab.idiap.ch/criteria/ In the demo, the retrieved news articles will be order not only by the match to the query but also by the estimated reliability for each sources (URL domains are color coded from green to red, for instance, scrolling down will show results coming from less reliable sources marked with red-ish colors). Alternatively, if a news URL or a news outlet domain (e.g. apnews.com) is given as a query, information about the estimated values are detailed (e.g. showing the neighboring sources interacting with the media, etc.) Have a nice day, everyone! 🙂 submitted by /u/sergbur [link] [comments]

Too anxious about reviews as they didn’t arrive yet! Wanted to share with the community and see the reactions to the reviews! Rant and stuff! Be polite in comments. submitted by /u/always_been_a_toy [link] [comments]

Mistral AI’s Mistral Large 2 (24.07) foundation model (FM) is now generally available in Amazon Bedrock. Mistral Large 2 is the newest version of Mistral Large, and according to Mistral AI offers significant improvements across multilingual capabilities, math, reasoning, coding, and much more. In this post, we discuss the benefits and capabilities of this new

https://mistral.ai/news/mistral-large-2407/ 123B parameters On par with GPT-4o and Llama 3.1 405B, according to their benchmarks Mistral Research License allows usage and modification for research and non-commercial purposes submitted by /u/we_are_mammals [link] [comments]

Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You may need to customize an LLM to adapt to your unique use case, improving its performance on your specific dataset or task. You can customize the model

Amazon Q is a fully managed, generative artificial intelligence (AI) powered assistant that you can configure to answer questions, provide summaries, generate content, gain insights, and complete tasks based on data in your enterprise. The enterprise data required for these generative-AI powered assistants can reside in varied repositories across your organization. One common repository to

This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. Salesforce, Inc. is an American cloud-based software company headquartered in San Francisco, California. It provides customer relationship management (CRM) software and applications focused on sales, customer service, marketing automation,

In today’s digital landscape, the protection of personally identifiable information (PII) is not just a regulatory requirement, but a cornerstone of consumer trust and business integrity. Organizations use advanced natural language detection services like Amazon Lex for building conversational interfaces and Amazon CloudWatch for monitoring and analyzing operational data. One risk many organizations face is

Today, we are excited to announce AWS Trainium and AWS Inferentia support for fine-tuning and inference of the Llama 3.1 models. The Llama 3.1 family of multilingual large language models (LLMs) is a collection of pre-trained and instruction tuned generative models in 8B, 70B, and 405B sizes. In a previous post, we covered how to deploy Llama 3 models on AWS Trainium and Inferentia based instances in Amazon SageMaker JumpStart. In this post, we outline how to get started with fine-tuning and deploying the Llama 3.1 family of models on AWS AI chips, to realize their price-performance benefits.

Today, we are excited to announce the availability of the Llama 3.1 405B model on Amazon SageMaker JumpStart, and Amazon Bedrock in preview. The Llama 3.1 models are a collection of state-of-the-art pre-trained and instruct fine-tuned generative artificial intelligence (AI) models in 8B, 70B, and 405B sizes. Amazon SageMaker JumpStart is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. Amazon Bedrock offers a straightforward way to build and scale generative AI applications with Meta Llama models, using a single API.

Today, we are excited to announce that the state-of-the-art Llama 3.1 collection of multilingual large language models (LLMs), which includes pre-trained and instruction tuned generative AI models in 8B, 70B, and 405B sizes, is available through Amazon SageMaker JumpStart to deploy for inference. Llama is a publicly accessible LLM designed for developers, researchers, and businesses to build, experiment, and responsibly scale their generative artificial intelligence (AI) ideas. In this post, we walk through how to discover and deploy Llama 3.1 models using SageMaker JumpStart.

Please post your personal projects, startups, product placements, collaboration needs, blogs etc. Please mention the payment and pricing requirements for products and services. Please do not post link shorteners, link aggregator websites , or auto-subscribe links. Any abuse of trust will lead to bans. 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. Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads. submitted by /u/AutoModerator [link] [comments]

In this post, we show how to develop an IDP solution using Anthropic Claude 3 Sonnet on Amazon Bedrock. We demonstrate how to extract data from a scanned document and insert it into a database.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. To equip FMs with up-to-date and proprietary information, organizations use Retrieval Augmented Generation (RAG), a technique that

This post was written in collaboration with Liran Zelkha and Eyal Solnik from Lili. Small business proprietors tend to prioritize the operational aspects of their enterprises over administrative tasks, such as maintaining financial records and accounting. While hiring a professional accountant can provide valuable guidance and expertise, it can be cost-prohibitive for many small businesses.

This post is co-written with Maciej Mensfeld from Mend.io. In the ever-evolving landscape of cybersecurity, the ability to effectively analyze and categorize Common Vulnerabilities and Exposures (CVEs) is crucial. This post explores how Mend.io, a cybersecurity firm, used Anthropic Claude on Amazon Bedrock to classify and identify CVEs containing specific attack requirements details. By using

As digital commerce expands, fraud detection has become critical in protecting businesses and consumers engaging in online transactions. Implementing machine learning (ML) algorithms enables real-time analysis of high-volume transactional data to rapidly identify fraudulent activity. This advanced capability helps mitigate financial risks and safeguard customer privacy within expanding digital markets. Deloitte is a strategic global

AWS announced the availability of the Cohere Command R fine-tuning model on Amazon SageMaker. This latest addition to the SageMaker suite of machine learning (ML) capabilities empowers enterprises to harness the power of large language models (LLMs) and unlock their full potential for a wide range of applications. Cohere Command R is a scalable, frontier

As a customer, you rely on Amazon Web Services (AWS) expertise to be available and understand your specific environment and operations. Today, you might implement manual processes to summarize lessons learned, obtain recommendations, or expedite the resolution of an incident. This can be time consuming, inconsistent, and not readily accessible. This post shows how to

In today’s rapidly evolving landscape of artificial intelligence (AI), training large language models (LLMs) poses significant challenges. These models often require enormous computational resources and sophisticated infrastructure to handle the vast amounts of data and complex algorithms involved. Without a structured framework, the process can become prohibitively time-consuming, costly, and complex. Enterprises struggle with managing