SentientMatters’ Post

Introducing Star Attention: A Breakthrough in Long-Sequence Efficiency for LLMs 🌟 Large language models (LLMs) are pushing boundaries with long-context capabilities, yet challenges remain. Processing millions of tokens often means higher costs and slower inference. This is where Star Attention comes in, a novel block-sparse attention mechanism developed by NVIDIA, designed to revolutionize inference over long sequences. Uses a two-phase approach: - Phase 1: Local context blocks are processed efficiently with anchor blocks to approximate global attention. - Phase 2: Global query encoding ensures all relevant tokens are attended to for accurate token generation. Key highlights: - Efficient Inference: Star Attention reduces memory requirements and inference time by up to 11x while preserving 95-100% accuracy. - Scalability: Compatible with Transformer-based LLMs, scaling seamlessly to 1M tokens without additional fine-tuning. - Practical Gains: Enables repository-level code analysis, multi-document summarization, and more. Impact: Star Attention balances computational efficiency with accuracy, setting a new standard for long-context processing. Future research will refine anchor block mechanisms for even greater scalability. Read more here: https://lnkd.in/eM7pRetC #AI #DeepLearning #LLMs #MachineLearning #StarAttention #NVIDIA #Efficiency #Innovation #GenAI #AttentionMechanisms

🚀 LLMs quantization with EfficientQAT 🚀 Introducing Large Language Models (LLMs) quantization with EfficientQAT. This innovative algorithm has made it possible to create a 2-bit INT Llama-2-70B model that outperforms its full-precision (FP) counterpart, Llama-2-13B, while using less memory. Key Highlights: • EfficientQAT achieves comparable performance to vector quantization methods like AQLM and QUIP#, without the deployment challenges. • A 2-bit quantized Llama-2-70B model was trained on a single A100-80GB GPU in just 41 hours. • The accuracy degradation is less than 3% compared to full precision (69.48 vs. 72.41). • Remarkably, this 2-bit model surpasses the Llama-2-13B model in accuracy (69.48 vs. 67.81) with a reduced memory footprint (19.2GB vs. 24.2GB). For those interested in exploring this further, the code is available at GitHub - https://lnkd.in/gU_udPZY. Paper: https://lnkd.in/gJppyBsr This advancement not only reduces the memory requirements but also speeds up both training and inference times.

I am really curious to see and understand what are the concrete tools and components to implement real life computing environments for generative AI model development. This article introduces way to implement parallelism into processes. Of course same techniques can be used other HPC solutions such as scientific calculations, rendering etc. https://lnkd.in/d3Bm56PG

Hey AI enthusiasts! 👋 Are you ready to dive into the latest advancements in AI model quantization? 🤖 I've curated a list of cutting-edge research papers that push the boundaries of quantization techniques, including state-of-the-art methods and groundbreaking 2-bit quantization approaches. Let's explore together! State-of-the-Art AI Model Quantization Papers: Paper 1: https://lnkd.in/dCaCEWdc Paper 2: https://lnkd.in/dn9RgNG9 Paper 3: https://lnkd.in/dh5XVejH Paper 4: https://lnkd.in/dTGT8C-Y Paper 5: https://lnkd.in/dfswG9c4 These papers delve into advanced quantization methods that optimize model size and computational efficiency without compromising performance. They're a must-read for anyone interested in scaling AI models for deployment on resource-constrained devices. 2. Innovative 2-Bit Quantization Papers: Paper 1: https://lnkd.in/d_hg452Q Paper 2: https://lnkd.in/d992ihhJ Paper 3: https://lnkd.in/dtTUg9bW 2-bit quantization is a hot topic in the AI community, promising significant reductions in model size and inference latency while maintaining competitive accuracy. These papers introduce novel techniques to tackle the challenges of low-bit quantization, opening new avenues for efficient AI deployment. Excited to learn more? Stay tuned for my upcoming Medium post, where I'll provide an in-depth overview of all these quantization methods, breaking down the key concepts and implications for real-world applications. Don't miss out on the opportunity to stay at the forefront of AI optimization strategies! 🔍💡 Keep innovating, Arvind

Expedera NPUs run large language models natively on edge devices. Dive into the next era of edge computing and machine learning. AiThority.Com #EdgeComputing #MachineLearning #NeuralProcessingUnit https://lnkd.in/d9-3FSqB

Large Language Models (LLMs) are a type of artificial intelligence designed to process and understand human language. Here's how they work: Architecture: LLMs typically use a transformer-based architecture, which consists of: 1. Encoder: Processes input text into numerical representations (embeddings). 2. Decoder: Generates output text based on these embeddings. 3. Self-Attention Mechanism: Allows the model to focus on specific parts of the input. Training: LLMs are trained on massive datasets of text using: 1. Masked Language Modeling: Some input tokens are randomly replaced with masks. 2. Next Sentence Prediction: Model predicts whether two sentences are adjacent. 3. Self-Supervised Learning: Model learns to predict missing tokens. #LLM

A couple of weeks back some research papers surfaced on using RNN with improvement as possible enhancement/ replacement to the Transformer network, Another surfaces from Google the RecurrentGemma which is based on Griffin Architecture(Hawk). Hawk is an innovative recurrent neural network (RNN) featuring gated linear recurrences, designed to excel in handling long sequences of data. It surpasses the performance of existing RNN models like Mamba on various tasks while maintaining efficient training and scalability. With its advanced architecture, Hawk offers superior capabilities in processing extensive sequences, making it a powerful tool for tasks requiring nuanced understanding and analysis of large datasets. Hawk uses a special technique called gated linear recurrences, which helps it perform even better than another advanced RNN model called Mamba on various tasks. Griffin is another model we've developed, which is a mix of gated linear recurrences and something called local attention. This combination makes Griffin as good as another powerful RNN model called Llama-2, even though Griffin was trained on much less data. What's even cooler is that Griffin can understand and work with sequences of data that are much longer than what it was trained on. This means it can handle really long pieces of text or sequences of events without breaking a sweat. Both Hawk and Griffin are efficient when it comes to using computer hardware during training, and they're even faster than other models during actual use. We've scaled up Griffin to a whopping 14 billion parameters, and we've figured out how to train our models across many computers efficiently. In summary, our new RNN models, Hawk and Griffin, are super smart and efficient. They work great on long sequences of data, are easy to train, and scale well even on huge amounts of data. https://lnkd.in/dbUBeeYs #generativeai #llm

Excited to share “BitDelta: Your Fine-Tune May Only Be Worth One Bit” by James Liu et al. This paper introduces BitDelta, which compresses the weight delta between pre-trained and fine-tuned models to 1 bit without losing performance. Challenge: Fine-tuning large language models (LLMs) increases storage and GPU memory demands. Solution: BitDelta quantizes the delta between fine-tuned and base models to 1 bit, significantly cutting memory usage. Impact: This allows using a single high-precision base model with multiple 1-bit deltas, reducing GPU memory requirements by over 10× and enhancing generation latency. Validation: Experiments on models up to 70B parameters (e.g., Llama-2, Mistral) show minimal performance degradation. Read more and explore the code: Paper: https://lnkd.in/gyYbY4X2 #MachineLearning #AI #DeepLearning #LLMs #ModelCompression #BitDelta #Research

#GPU #optimization #energyoptimization As you know, we are all working on building our own neural networks ...because we can #Remember not to waste the resources and preprocess in the CU and only after compiling AND (if you are ready to train the model), then and only then switch to the GPU https://lnkd.in/dxMrEW23

I spent sometime reading the paper: "BitNet: Scaling 1-bit Transformers for Large Language Models", available here: https://lnkd.in/gXtCcJbd. This is fascinating paper and uses 1-bit weights as opposed to byte or integer type weights. This makes me think that the data type used for weights in a Neural Network are less important than we assume it to be. Below are some key takeaways from this paper: - The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. This work introduces BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. - Introduces BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. - Experimental results on language modeling show that BitNet achieves competitive performance while substantially reducing memory footprint and energy consumption, compared to state-of-the-art 8-bit quantization methods and FP16 Transformer baselines. - BitNet performs better as compared to post training quantized Models - As the number of parameters increases, the performance of BitNet and FP16 transformers are comparable. However, when the number of parameters is lower, BitNet has lower performance - For comparable performance(loss/accuracy), the energy consumption of the BitNet model consumes less energy by a factor of (1/10) peta Joule. To put this perspective, we can power almost 1000 households per month in India using the energy savings of BitNet - Even if we assume just 8-bits per parameter, the size of the BitNet model is smaller by a factor of 8 #neuralnetworks #deeplearning #generatieveai #llms #machinelearning #naturallanguageprocessing #artificialintelligence