#LargeLanguageModels

[mysocial8onetech]

Learn how Qwen2.5, a large language model developed by Alibaba Cloud, revolutionizes AI with its ability to process long contexts up to 128K tokens and support over 29 languages. Pretrained on a large-scale dataset of 18 trillion tokens, it enhances high-quality code, mathematics, and multilingual data. Discover how it matches Llama-3-405B’s accuracy with only one-fifth of the parameters.

[craigbrownphd]

Expel adds active defense for combating email threats

https://www.kmworld.com/Articles/ReadArticle.aspx?ArticleID=169206&utm_source=dlvr.it&utm_medium=tumblr

[govindhtech]

IBM SSM Transformer Speed Performance With Bamba Model

IBM built “Bamba” by crossing a transformer and SSM. In collaboration with CMU, Princeton, and the University of Illinois, IBM Research created an open-source LLM that combines state-space model runtime performance with transformer expressiveness. Important enhancements are coming to IBM Granite 4.0.

IBM SSM

The transformer architecture of today's huge language models can create human-like writing. Self-attention, which allows the model to examine every word in an input sequence before responding, boosts its effectiveness.

The issue grows with prolonged talk. The model's memory retention of the ongoing sequence during response quadratically increases generation cost. Double the context window size and the cost of processing and responding quadruples. This “quadratic bottleneck” often delays model responses to queries. Duplicated computing is also produced. Before ChatGPT popularised the transformer in 2022, scholars were exploring for alternate architectures.

There are two possible answers.

IBM SSM layers with state-space models (SSMs) and transformers. IBM's first hybrid project, Bamba, can parse long sequences like a transformer and run as quickly as an SSM. It was just made public. A few months from now, IBM's Granite 4.0 machines will have Bamba's upgrades. Bamba-9B can run at least twice as fast as equivalent transformers while preserving accuracy by dramatically reducing KV (key value) cache memory demands. Everything depends on the IBM researcher leading the KV cache reduction initiative. Greater context duration, lower latency, and higher throughput. State-space models, the most important model you've never heard of, have been used to represent dynamic systems for decades but aren't as well known as transformers. They are crucial to robotics, control theory, signal processing, and electrical engineering. IBM researchers helped convert SSMs to deep learning. State-space models analyse time-series data in any discipline. SSMs can explain weather, stock markets, and brain electrical activity using mathematical calculations. An SSM uses observations to find a “hidden state” of a given size that captures the system's important properties. Consider the state a historical summary. The hidden state updates with future forecasts without increasing when fresh information arrives. SSMs evolved into neural networks in 2021 when Stanford academics Albert Gu and colleagues released S4, which used state variables to language. The transformer and RNNs before it and the SSM processed word sequences well. However, it handled long sequences faster and better than transformers and RNNs. SSMs compress historical data in a hidden state, whereas transformers process every word in the context window. Selective retention speeds up inference and reduces memory overhead. S4, which was difficult to build, surprised participants in Long Range Arena, a test for language models' ability to handle long sequences. Gupta, an IBM AI resident, helped Gu and his colleagues reduce the model with diagonal state spaces. The “diagonal” IBM SSM lowered S4's 1,000 lines of code to 10. After introducing a gating mechanism that filtered out irrelevant information, Gupta helped SSMs equal transformers' "expressivity," or sequence-modeling capacity, for the first time. That team revealed a possible first hybrid transformer. He investigated hybrids since he works on IBM's Granite Vision models. Text with local dependencies can be handled using standard attention blocks while leveraging SSMs for longer-range contextualisation. Tri Dao at Princeton and Gu, then a CMU professor, released the gated SSM variant Mamba2 in 2023, sparking a wave of hybrids like Samba and Mamba Former. Last year, Nvidia announced Nemotron-H after proving that these hybrids could speed up inferencing and surpass either architecture.

Overcoming KV cache bottleneck

IBM Research's Granite LLMs for enterprise have always prioritised efficiency. As IBM Granite expanded, researchers studied the quadratic bottleneck. IBM researchers built their own hybrid Bamba-9B after internal confirmation of Nvidia's findings. They used Nvidia's Mamba2 architecture and released practically all of Bamba's components open-source, including data, training recipes, IBM's data loader for large-scale distributed training, and a quantisation framework to decrease storage and inference costs. First, Bamba was trained with 2 trillion tokens—words and fragments. Motivated by the results, they decreased the model's bit width from Mamba2's 16-bit floating-point precision to 8-bits, added trillion tokens, and quantised it to reduce its size from 18 GB to 9 GB. Bamba matches Meta's Llama-3.1 8B model, which was trained on seven times more data, on crucial benchmarks because to its design and training data. SSM execution optimisation was their next problem with vLLM. The Bamba team worked together with Red Hat to integrate the model into the “virtual” LLM, the most popular open-source inference server for Large Language Models. SSMs require customised state management, making support difficult. Ganti requested the audience to enhance Bamba when it was published late last year. Bamba's Hugging Face introduction said, “Let's work together to overcome the KV-cache bottleneck.” After training on 4,000 token sequences, Bamba can handle 32,000 token conversations. Ganti said vLLM can reach one million tokens and function five times quicker than a transformer if it supports SSM.

[tsqc]

AI Leadership & Ethical Innovation: Shaping the Future of Business

[elenajhonson]

Unlocking business potential with LLM & Generative AI d

In today’s fast-paced digital environment, businesses are increasingly turning to artificial intelligence solutions to streamline operations, improve customer experiences, and gain a competitive edge. Among the most impactful technologies are Large Language Models (LLMs) and Generative AI tools, which are being adopted across industries to drive business automation, enhance creativity, and support smarter decision-making.

A leading technology service provider is playing a key role in delivering tailored AI development services by combining both proprietary and open-source models. Their expertise lies in building and integrating LLMs that align with specific business goals—whether it’s improving customer support, enhancing content creation, or simplifying data analysis through Natural Language Processing (NLP) solutions. These AI systems are designed not only to automate repetitive tasks but also to provide valuable insights and personalised experiences.

The development process begins with a detailed understanding of the client’s objectives and challenges. Through workshops and data analysis, they identify the areas where AI can add the most value. Based on this discovery phase, a custom AI strategy and roadmap is created, complete with ethical guidelines, system design, and clear success metrics.

Once the strategy is in place, a prototype is developed and tested in real-world conditions. Feedback from users and stakeholders is used to refine the system for better accuracy, usability, and fairness. Following successful testing, the solution is scaled up and fully integrated into existing business workflows. Training, documentation, and continuous support are provided to ensure smooth adoption and long-term performance.

Their services cover a wide range of AI-driven capabilities, including intelligent virtual assistants, AI-powered data analytics, predictive modelling, content generation, and seamless IoT and AI integration. These solutions are not only built for current needs but are also designed to evolve alongside the business, with regular model updates and performance monitoring.

What sets this approach apart is the emphasis on ethical AI development, user testing, and scalable architecture. By focusing on measurable results and long-term impact, this provider helps organisations confidently embrace digital transformation with AI and unlock new growth opportunities through advanced AI technologies.

Businesses exploring AI for workflow automation, customer engagement, or data analysis can benefit greatly from such a structured, human-centered approach to LLM & Generative AI Development. It’s a forward-thinking investment that prepares companies to thrive in an AI-powered future.

[totoshappylife]

Speculative Decoding for Verilog:

Excerpt from PDF: Speculative Decoding for Verilog: Speed and Quality, All in One Changran Xu1,3,†, Yi Liu1,3,†, Yunhao Zhou1,3, Shan Huang2,3, Ningyi Xu2, and Qiang Xu1,3 1The Chinese University of Hong Kong, Shatin, Hong Kong S.A.R. 2Shanghai Jiao Tong University, Shanghai, China 3National Technology Innovation Center for EDA, Nanjing, Jiangsu, China Abstract—The rapid advancement of large…

[meiiaiinc]

Mei AI is a global leader in AI solutions, offering industry-trained Large Language Models that can be tuned accordingly with company-specific data and hosted privately or in your cloud.

Our RAG ( Retrieval Augmented Generation ) based AI approach uses Embedded Model and Retrieval context ( Semantic Search ) while processing a conversational query to curate Insightful response that is specific for an Enterprise. Blended with our unique skills and decade long experience we had gained in Data Analytics solutions, we combine LLMs and ML Algorithms that offer great solutions for Mid level Enterprises.

We are engineering a future that allows people, businesses, and governments to seamlessly leverage technology. With a vision to make AI accessible for everyone on the planet, our team is constantly breaking the barriers between machines and humans.

[emergysllc]

How can verticalization of LLM pivot industries to increase accuracy, efficiency, and relevance in practical applications? Get insights here: https://lnkd.in/dW7B4iq5

Learn how tailored LLMs can empower professionals to leverage AI in meaningful, context-aware ways across sectors.

[yourtechdietblog]

Understanding the Power of Large Language Models (LLMs)

Large Language Models (LLMs) are advanced AI systems that can understand and generate text with remarkable accuracy. Trained on massive datasets, these models use transformer-based architectures to perform tasks like answering questions, summarizing content, and facilitating conversational AI.

✨ What Makes LLMs Special?

Everyday Applications: Tools like ChatGPT and Google Bard transform industries like education, healthcare, and customer service.

Advantages: LLMs enhance automation, boost productivity, and enable seamless human-AI interactions.

Limitations: Challenges include bias, high computational costs, and lack of true reasoning.

LLMs are shaping the future of AI by bridging the gap between human creativity and machine efficiency.

[ai-network]

Writer Unveils Self-Evolving Language Models

Writer, a $2 billion enterprise AI startup, has announced the development of self-evolving large language models (LLMs), potentially addressing one of the most significant limitations in current AI technology: the inability to update knowledge post-deployment.

Breaking the Static Model Barrier

Traditional LLMs operate like time capsules, with knowledge frozen at their training cutoff date. Writer's innovation introduces a "memory pool" within each layer of the transformer architecture, enabling the model to store and learn from new interactions after deployment.

Technical Implementation

The system works by incorporating memory pools throughout the model's layers, allowing it to update its parameters based on new information. This architectural change increases initial training costs by 10-20% but eliminates the need for expensive retraining or fine-tuning once deployed. This development is particularly significant given the projected costs of AI training. Industry analyses suggest that by 2027, the largest training runs could exceed $1 billion, making traditional retraining approaches increasingly unsustainable for most organizations.

Performance and Learning Capabilities

Early testing has shown intriguing results. In one mathematics benchmark, the model's accuracy improved dramatically through repeated testing - from 25% to nearly 75% accuracy. However, this raises questions about whether the improvement reflects genuine learning or simple memorization of test cases.

Current Limitations and Challenges

Writer reports a significant challenge: as the model learns new information, it becomes less reliable at maintaining original safety parameters. This "safety drift" presents particular concerns for customer-facing applications. To address this, Writer has implemented limitations on learning capacity. For enterprise applications, the company suggests a memory pool of 100-200 billion words provides sufficient learning capacity for 5-6 years of operation. This controlled approach helps maintain model stability while allowing for necessary updates with private enterprise data.

Industry Context and Future Implications

This development emerges as major tech companies like Microsoft explore similar memory-related innovations. Microsoft's upcoming MA1 model, with 500 billion parameters, and their work following the Inflection acquisition, suggests growing industry focus on dynamic, updateable AI systems.

Practical Applications

Writer is currently beta testing the technology with two enterprise customers. The focus remains on controlled enterprise environments where the model can learn from specific, verified information rather than unrestricted web data. The technology represents a potential solution to the challenge of keeping AI systems current without incurring the massive costs of regular retraining. However, the balance between continuous learning and maintaining safety parameters remains a critical consideration for widespread deployment. Read the full article

[thedevmaster-tdm]

Unlocking the Secrets of LLM Fine Tuning! 🚀✨

[feathersoft-info]

LLM Developers & Development Company | Why Choose Feathersoft Info Solutions for Your AI Needs

In the ever-evolving landscape of artificial intelligence, Large Language Models (LLMs) are at the forefront of technological advancement. These sophisticated models, designed to understand and generate human-like text, are revolutionizing industries from healthcare to finance. As businesses strive to leverage LLMs to gain a competitive edge, partnering with expert LLM developers and development companies becomes crucial. Feathersoft Info Solutions stands out as a leader in this transformative field, offering unparalleled expertise in LLM development.

What Are Large Language Models?

Large Language Models are a type of AI designed to process and generate natural language with remarkable accuracy. Unlike traditional models, LLMs are trained on vast amounts of text data, enabling them to understand context, nuances, and even generate coherent and contextually relevant responses. This capability makes them invaluable for a range of applications, including chatbots, content creation, and advanced data analysis.

The Role of LLM Developers

Developing an effective LLM requires a deep understanding of both the technology and its applications. LLM developers are specialists in creating and fine-tuning these models to meet specific business needs. Their expertise encompasses:

Model Training and Fine-Tuning: Developers train LLMs on diverse datasets, adjusting parameters to improve performance and relevance.

Integration with Existing Systems: They ensure seamless integration of LLMs into existing business systems, optimizing functionality and user experience.

Customization for Specific Needs: Developers tailor LLMs to address unique industry requirements, enhancing their utility and effectiveness.

Why Choose Feathersoft Info Solutions Company for LLM Development?

Feathersoft Info Solutions excels in providing comprehensive LLM development services, bringing a wealth of experience and a proven track record to the table. Here’s why Feathersoft Info Solutions is the go-to choice for businesses looking to harness the power of LLMs:

Expertise and Experience: Feathersoft Info Solutions team comprises seasoned experts in AI and machine learning, ensuring top-notch development and implementation of LLM solutions.

Customized Solutions: Understanding that each business has unique needs, Feathersoft Info Solutionsoffers customized LLM solutions tailored to specific industry requirements.

Cutting-Edge Technology: Utilizing the latest advancements in AI, Feathersoft Info Solutions ensures that their LLMs are at the forefront of innovation and performance.

End-to-End Support: From initial consultation and development to deployment and ongoing support, Feathersoft Info Solutions provides comprehensive services to ensure the success of your LLM projects.

Applications of LLMs in Various Industries

The versatility of LLMs allows them to be applied across a multitude of industries:

Healthcare: Enhancing patient interactions, aiding in diagnostic processes, and streamlining medical documentation.

Finance: Automating customer support, generating financial reports, and analyzing market trends.

Retail: Personalizing customer experiences, managing inventory, and optimizing supply chain logistics.

Education: Creating intelligent tutoring systems, generating educational content, and analyzing student performance.

Conclusion

As LLM technology continues to advance, partnering with a skilled LLM development company like Feathersoft Info Solutions can provide your business with a significant advantage. Their expertise in developing and implementing cutting-edge LLM solutions ensures that you can fully leverage this technology to drive innovation and achieve your business goals.

For businesses ready to explore the potential of Large Language Models, Feathersoft Info Solutions offers the expertise and support needed to turn cutting-edge technology into actionable results. Contact Feathersoft Info Solutions today to start your journey toward AI-powered success.

[govindhtech]

HalluMeasure Tracks LLM Hallucinations & Hybrid Framework

Find HalluMeasure, a hybrid framework that assesses AI hallucinations using logic and language.

This novel method combines claim-level evaluations, chain-of-thought reasoning, and hallucinatory error classification.

Large language models (LLMs) do not search a medically validated list of drug interactions for queries like “Which medications are likely to interact with St. John’s wort?” unless instructed to do so. Instead, it lists St. John's wort-related terms by distribution.

How to spot llm hallucinations

A mix of real and maybe made-up medications with varying interaction risks is likely. These LLM hallucinations or misleading claims still hinder corporate utilisation of LLMs. While healthcare has ways to reduce hallucinations, recognising and measuring them is still necessary for safe generative AI use.

The latest Conference on Empirical Methods in Natural Language Processing (EMNLP) paper describes HalluMeasure, a novel method for measuring hallucinations using claim-level evaluations, chain-of-thought reasoning, and linguistic classification into error types.

HalluMeasure extracts assertions from the LLM answer. The classification model splits the claims into five primary groups (supported, absent, contradicted, partially supported, and unevaluatable) using a distinct claim classification model by comparing them to the context-retrieved material relevant to the request.

HalluMeasure also categorises hallucination claims into eleven language faults, including entity, temporal, and overgeneralisation. Finally, compute the distribution of fine-grained error categories and assess the rate of unsupported claims (classes other than supported) to calculate an aggregate hallucination score. This distribution helps LLM builders improve by revealing their LLMs' flaws.

Deconstructing replies into assertions

Breaking an LLM answer into statements is the first step. A “claim”—a single predicate with a subject and (optionally) an object—is the simplest unit of context-related information.

The developers evaluated at the claim level because categorisation improves hallucination detection and atomicity improves measurement and localisation. People differ from existing techniques by directly extracting assertions from the whole answer language.

The few-shot prompting claim extraction methodology presents task required rules following an initial instruction. Sample replies and manually extracted assertions are supplied. This extensive prompt teaches the LLM to accurately extract claims from each answer without modifying model weights. After extracting claims, classify them by hallucination kind.

Advanced claim classification logic

The traditional technique of requesting an LLM to categorise extracted claims directly did not meet performance standards. Also used chain-of-thought (CoT) reasoning, where an LLM must defend each step in addition to accomplishing a goal. Shown to improve model explainability and LLM performance.

Five-step CoT prompts use few-shot claim categorisation examples and tell the claims classification LLM to carefully examine each claim's faithfulness to the reference context and record the reason for each analysis.

The team compared HalluMeasure against other solutions using the standard SummEval benchmark dataset after implementation. With few-shot CoT prompting, performance improves by 2 percentage points (from 0.78 to 0.8), bringing us closer to large-scale automated LLM hallucination detection.

Area under the SummEval dataset receiver-operating-characteristic curve for hallucination identification solutions.

Fine-grained error classification

HalluMeasure helps design more targeted LLM dependability therapies by providing more exact information regarding hallucinations. From verbal patterns in frequent LLM hallucinations, individuals propose a new set of error types beyond binary classifications or the widely used natural-language-inference (NLI) categories of support, reject, and not enough information. Temporal reasoning might be applied to an answer that states a new invention is being utilised when the context suggests it will be used later.

Knowing the prevalence of mistake types across LLM replies helps concentrate hallucination mitigation. Allowing more than 10 turns in a discussion might be examined if a majority of wrong statements contradict a context-specific argument. Limiting rounds or summarising previous turns may lessen hallucination if fewer turns reduce this mistake kind.

HalluMeasure can help researchers spot a model's hallucinations, but generative AI's risk is shifting. Therefore, studying reference-free detection, employing dynamic few-shot prompting tactics for specific use cases, and integrating frameworks will continue to advance responsible AI.

[tsqc]

AI and Leadership: Insights from Novartis and Claruna

[womaneng]

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I tried to remove the stickers from my 11-year-old MacBook... Anyone got a miracle solution to save my MacBook? Help me out, please! 🙃👩🏻‍💻 . . .