Sanket Khandare’s Post

Another great #AIvisualexplainer on #AITransformers "Transformer Explainer features a live GPT-2 (small) model running directly in the browser. This model is derived from the PyTorch implementation of GPT by Andrej Karpathy's nanoGPT project and has been converted to ONNX Runtime for seamless in-browser execution." The image below illustrates the inner workings of a transformer model, specifically focusing on how it processes the input phrase "Data visualization empowers users to create". The diagram is divided into several key sections: ➡ Embedding: This is the first step where each word is converted into a numerical representation. ➡ Multi-head Self Attention: This is a core component of the transformer architecture. It's represented by three main parts: 👉 Query (Q): Shown in blue 👉 Key (K): Shown in red 👉 Value (V): Shown in green. The attention mechanism allows the model to focus on different parts of the input when processing each word. ➡ Attention: This is visualized as a grid of purple circles, showing how each word attends to other words in the sentence. ➡ MLP (Multi-Layer Perceptron): This processes the output of the attention layer. ➡ Probabilities: The final output shows the probabilities for the next word prediction (with "create" having the highest probability at 11.03%). The diagram also shows that there are 11 more identical Transformer Blocks ie. this is just one layer of a larger model. The visualization effectively demonstrates how the model processes language, from breaking down the input sentence to predicting the most likely next word. It's a great tool for understanding the flow of information through a transformer model and it also has accompanying explanations and a video overview which go into some great detail. ➡ Go visit the site: https://lnkd.in/eUKeQmUH #EnterpriseAI #AI #LLM #LLMs

💡💻Good news from the realm of LLM architecture: ✏ Machine Learning Architect Burak Gozlulu sheds light on a strategic approach to optimizing AI solution costs : agentic workflows with multiple, fine-tuned smaller LLMs. 📊 Task decomposition through multiple, smaller, fine-tuned LLMs showcases a promising avenue for enhancing cost efficiency. This approach holds the potential for significant infrastructure cost savings compared to using a single, large model. 📊 Remember, the key lies in balancing decomposition to steer clear of overengineering, which could lead to increased complexity and coordination overhead, ultimately diminishing returns. #largelanguagemodels #machinelearning #generativeAI #researchanddevelopment #softwarearchitecture #amazonscience

𝐌𝐢𝐱𝐭𝐮𝐫𝐞-𝐨𝐟-𝐀𝐠𝐞𝐧𝐭𝐬 (𝐌𝐨𝐀) 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐬 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 Mixture-of-Experts (MoE) uses multiple expert networks to tackle complex problems by leveraging diverse model capabilities. This method has shown great success across various applications. The Mixture-of-Agents (MoA) Framework draws inspiration from MoE but takes it a step further. Instead of operating at the activation level within a single model, MoA utilizes multiple full-fledged LLMs across different layers. MoA's innovative approach leverages the collective strengths of multiple models. Key Advantages of MoA: 🛠️ Eliminates computational overhead associated with fine-tuning. 🚀 Can be applied to the latest LLMs regardless of size or architecture. 📜 Operates through prompt interfaces, consolidating roles of gating and expert networks into LLMs. 🌐 Combining the strengths of multiple LLMs for better language generation. 📊 Achieved state-of-the-art results, even surpassing GPT-4o on key benchmarks. 🤝 Leverages various model capabilities to improve accuracy and quality. AlpacaEval 2.0: 📈 65.1% win rate vs. 57.5% for GPT-4 Omni. MT-Bench & FLASK: Significant improvements in response quality across various dimensions. Abs: https://lnkd.in/g9f652qR #AI #GenAI #LLM #MLLM #MachineLearning #LanguageModels #MoA #AIAgent #Benchmark #AIBenchmark

Like the new term "Mixture-of-Agents (MoA)" to take advantage of the mixture of LLMs. Just like human, trust a single person is always risky but getting independent opinions from another person or two will reduce significantly the risk. The same logic can be applied to LLM, trust response of a single LLM is always with high risk, but layered or combined or ranked responses from another LLM or two can definitively reduce the risk. Of course, who to ask is important for human as well as for LLM. Like to see this practice or workflow or architecture become the mainstream of GenAI applications. Hope authors can further explore along to this promising direction.

🎉 Day 24 of #LearningUtsav #FestivalOfLearning 🎉 #LUD24 Learning Utsav Today, I explored the Sequence-to-Sequence (Seq2Seq) Models – a crucial architecture for applications like machine translation, text summarization, and chatbots. 🚀 🔑 Key Concepts: Encoder-Decoder Architecture: Compresses input into a context vector and expands it to produce an output. Attention Mechanism: Enables the model to focus on relevant parts of the input, especially useful for longer sequences. Applications: Language translation, Q&A, and summarization tasks. 📚 Resources: 1. Seq2Seq Model Introduction - Analytics Vidhya : https://lnkd.in/dx2_QuiY 2. Encoder-Decoder with Attention - Machine Learning Mastery : https://lnkd.in/djDGhRrW #AI #Seq2Seq #DeepLearning #MachineLearning #LearningJourney

Iteratively enhanced LLM outputs outperform OpenAI GPT-4 Omni on AlpacaEval 2.0, MT-Bench, and FLASK! 🤯 Mixture-of-Agents (MoA) uses multiple LLMs in a layered architecture to iteratively enhance the generation quality. Mixture-of-Agents (MoA) 1️⃣ Select multiple LLMs with varying strengths 2️⃣ Create a multi-layer architecture where each layer consists of multiple LLMs. 3️⃣ Each new layer uses the outputs from previous layers as auxiliary information to generate its response. Insights 🌟 MoA models achieve superior performance with a 65.1% score on AlpacaEval 2.0 compared to 57.5% by GPT-4 Omni. 🤖 LLM tends to generate better responses when presented with outputs from other models. 📊 Layered aggregation of outputs from multiple LLMs leads to better responses. 🔍 Selection of LLMs for each MoA layer is crucial for Performance and Diversity. 🚀 MoA-Lite variant outperforms GPT-4 Turbo by 4%, being twice as cost-efficient. ⏳ Aggregation of model responses can lead to a high Time to First Token (TTFT). 🧪 Could be interesting for synthetic data generation and evaluation. Paper: https://lnkd.in/ez3rbuNb Code: https://lnkd.in/eCH6Cy6H Great work by Together AI!

This is truely an interesting integration of LLMs. Due to the sophisticated nature of Arabic Language, I think this presents an effective solution. The layered aggregation of outputs from multiple LLMs can complement the weakness from other trained models and generate much more effective outputs. Youtube: https://lnkd.in/dzx5AhTY #AI #LLMs #NLP #ML

Letting models correct each other’s responses is a quick and easy way to improve the accuracy of your pipeline output.

To read later

maybe this can be used to sharply align llms