Ashish Patel 🇮🇳’s Post

Optimisation is biggest problem in Large Language Model when we want to use that for real-world condition So I found one interesting book that help us to understand how we can do mathematical optimization with python. Learning Resource: https://lnkd.in/dFji3W5D P.S Learning this will help you to understand the concept like how quantization, PEFT, LORA all are contributing.

🔍 See Beyond the Next Word: Multi-Token Vision for LLM Supremacy Instead of predicting just the next word, this novel training approach has models simultaneously predict multiple future tokens at once. By looking further ahead into sequences, models develop better contextual understanding. The key benefits and use cases include: ✅ Up to 3x faster inference speeds for models trained on 4-token prediction ⬆️ Significantly improved accuracy on code generation tasks like HumanEval (+12%) and MBPP (+17%) 📈 Larger gains as model sizes increase, ideal for scaling up LLMs ⚡ No extra training time or memory overhead This simple tweak to the standard next-token objective could supercharge performance across AI applications requiring deeper language comprehension and reasoning. For language model practitioners, this multi-token prediction method offers an exciting opportunity to enhance your models' capabilities on coding, analysis, and other complex AI tasks! Researchers should also explore extending this approach across different model sizes, domains, and architectures to unlock its full potential. Paper: https://lnkd.in/dDZSg8Zi P.S. The future of more human-like AI understanding could involve training models to plan further ahead. Very promising work!

🔥Top 4 Large Language Model Inference Optimization Framework everybody should know in 2024 ✔️✔️✔️ Are you tired of slow inference performance and limited GPU utilization when working with large language models (LLMs)? 🤯Look no further 🚀 We've got you covered with the top LLM GPU optimization frameworks that can supercharge your workflow. 💻TensorRT-LLM: https://lnkd.in/gKkCRh5X 🔹 Use Case: Optimizing LLM inference performance on NVIDIA GPUs 🔹 Features: ‣ Static batching for improved GPU utilization ‣ Continuous batching for parallel processing ‣ Paged attention for reduced memory footprint ‣ LoRA for low-precision quantization ✨ LLMTools : https://lnkd.in/gNgbBqHz 🔹 Use Case: Training and finetuning LLMs on consumer GPUs 🔹 Features: ‣ Mixed precision training for reduced memory/compute ‣ Data parallelism across multiple GPUs for faster training ‣ Knowledge distillation for efficient student models 🌴 LiteLLM: https://lnkd.in/gy3aHT5m 🔹 Use Case: Unified interface for calling different LLM providers 🔹 Features: ‣ Prompt caching for faster inference ‣ Model fusion for combined LLMs into one optimized model 🚀 Optimum: https://lnkd.in/gQxzcCvw 🔹 Use Case: Defining and building new LLM models from scratch 🔹 Features: ‣ Automatic mixed precision selection per layer ‣ Tensor fusion for combined operations These frameworks enable deep learning practitioners to unlock the full potential of large language models on GPU hardware through techniques like batching, quantization, parallelism, caching, and operation fusion. Choose the right tool based on your specific LLM use case and optimization goals. 🚀 P.S. Era of GPU Optimization is because of new generation of LLMs is arrived.

Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models As large language models (LLMs) like GPT-4 continue to advance, accurately evaluating their outputs has become increasingly challenging and costly. Traditionally, we've relied on using an expensive, single large model as the judge. However, a groundbreaking new approach called PoLL (Panel of LLm evaluators) flips this paradigm! The key innovation? PoLL employs a diverse panel of smaller LLMs from families like GPT-3.5, Command R, and Haiku to collectively evaluate model outputs. This unique combination achieves: 1️⃣ Higher correlation with human judgments across datasets like QA and dialogue compared to GPT-4 alone 2️⃣ Over 7x lower evaluation costs, making it highly scalable 3️⃣ Reduced intra-model bias by pooling evaluations from varied model families By democratizing access to advanced LLM evaluation capabilities, PoLL empowers organizations of all sizes to rigorously test and improve their models. It's a game-changer for more inclusive, unbiased, and cost-effective model development! The implications are profound - PoLL could reshape how we approach AI evaluation and accelerate innovation across industries. I'm excited to see PoLL and similar methods further evolve AI accessibility. Paper : https://lnkd.in/dgFDMhNq Dataset : https://lnkd.in/d5N6xueM What are your thoughts on collective model evaluation? How could PoLL transform your work or field? Let me know in the comments!

Revolutionizing Medicine with Med-Gemini: What unprecedented opportunities can 1M+ context open up in medicine?🩺💡 In the rapidly evolving field of AI-assisted medical diagnosis and treatment, a groundbreaking innovation called Med-Gemini is poised to transform the healthcare landscape. Built upon the powerful Gemini architecture, this cutting-edge model family introduces game-changing capabilities for medical reasoning and multimodal data integration. 📊 Unparalleled Performance Across Medical Benchmarks: ✦ New state-of-the-art 91.1% accuracy on the challenging MedQA (USMLE) benchmark ✦ Top scores on multimodal tasks like Path-VQA and ECG-QA   ✦ Superior results on medical text summarization, surpassing human experts 🔍 Core Innovations Driving Excellence: 💫 Self-training mechanisms tailored for medical data 💫 Uncertainty-guided web search for enhanced reasoning 💫 Specialized multimodal fine-tuning for medical applications 💫 Efficient customization with novel encoders for new data modalities 🧪 Real-World Capabilities:  🏋 Seamless navigation of extensive electronic health records (EHRs) 🏋 Impressive performance on instructional medical video comprehension 🏋 Promising potential for multimodal medical dialogues and diagnostic reasoning 🚀 Pushing the Boundaries of Multimodal Medical AI: Med-Gemini outperforms industry leaders like GPT-4 and Med-PALM 2 by a significant margin, highlighting its exceptional proficiency in handling complex medical data across diverse modalities, including text, images, and long-form context. 📢 What's Unique?  💡 First AI model to establish new state-of-the-art across 10 out of 14 medical benchmarks 💡 Introduces novel uncertainty-guided search strategies for enhanced clinical reasoning 💡 Demonstrates unmatched long-context comprehension on medical records and videos 💡 Sets new standards in multimodal medical AI, surpassing GPT-4 by 44.5% on average 🔮 Future Outlook: 📈 Expansion towards broader generalist capabilities within healthcare domains   📈 Rigorous integration of ethical AI practices and regulatory compliance 📈 Collaborative efforts with clinical environments for comprehensive validation Med-Gemini represents a groundbreaking stride in AI-powered healthcare, offering unparalleled performance and unique capabilities that could revolutionize medical diagnosis, treatment planning, and patient care. Join us in celebrating this remarkable achievement and exploring the vast potential of Med-Gemini in advancing precision medicine and improving lives. Paper: https://lnkd.in/d3dBWZMF dataset: https://lnkd.in/dnzuWMK9 P.S. The era of precision medicine powered by advanced AI has officially begun. Buckle up!

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders 🚀 In the ever-evolving landscape of Natural Language Processing, text embedding models have traditionally been the domain of encoder-only or encoder-decoder architectures. However, a groundbreaking new approach called LLM2Vec is challenging this paradigm by harnessing the generative power of large decoder-only language models (LLMs) for text embedding tasks. The LLM2Vec methodology is a three-step process that transforms any pre-trained decoder-only LLM into a versatile text encoder: 1️⃣ Enabling bidirectional attention  2️⃣ Applying masked next token prediction (MNTP) training 3️⃣ Incorporating unsupervised contrastive learning (SimCSE) Through empirical evaluation, LLM2Vec has consistently outperformed traditional encoder-only models across a wide range of tasks, including: 💫 Word-level tasks (chunking, NER, POS tagging) 💫 Unsupervised text embedding on the Massive Text Embeddings Benchmark (MTEB) Remarkably, when combined with supervised contrastive learning, LLM2Vec achieves state-of-the-art performance on MTEB among models trained solely on publicly available data. But the real game-changer lies in LLM2Vec's ability to unlock the untapped potential of decoder-only LLMs for text embedding in a computationally efficient manner. This not only presents a paradigm shift in text encoding but also paves the way for democratizing access to advanced text embedding capabilities, even in resource-constrained environments. Code: https://lnkd.in/ddgY3qJx paper:https://lnkd.in/dmYA_gfV P.S. The future of text encoding has arrived, and LLM2Vec is leading the charge! 🌟

How to run Llama3 70B on a single GPU with just 4GB memory GPU See How : https://lnkd.in/deaVXJkg