Sanket Khandare’s Post

Mistral AI's Breakthrough in AI Innovation! Mistral AI is pushing the boundaries of AI with Mixtral 8x7B, a high-performance sparse mixture-of-experts model. Open-source and licensed under Apache 2.0, Mixtral outshines Llama 2 70B, boasting 6x faster inference and superior cost/performance trade-offs, even rivaling GPT3.5. Key Features: - Efficiently handles a 32k-token context, supporting English, French, Italian, German, and Spanish. - Strong performance in code generation; achieves an impressive 8.3 score on MT-Bench. - Sparse architecture with 46.7B total parameters, using only 12.9B parameters per token. Ethical Considerations: - Mixtral exhibits improved truthfulness and reduced biases, outperforming competitors on TruthfulQA, BBQ, and BOLD benchmarks. Innovation in Deployment: - Mistral AI enables community deployment with open-source changes to the vLLM project and the Skypilot deployment tool. - Early access available on the mistral-small beta endpoint for developers exploring generative and embedding endpoints. Explore the future of open AI with Mixtral 8x7B. Innovation at its core! #mixtral8x7B #aiinnovation #opensourceai #artificialintelligence #aiadvancements #aitechnology #aiapplications #futuretech

🧠 All you need to know about Vision-Language Models | http://vlm.aman.ai - Here is a primer that offers an overview of Vision-Language Models (VLMs) and ~25 popular VLM architectures such as GPT-4V, LLaVA, Frozen, Flamingo, PaLM-E, MiniGPT, etc. 🔹 A Detailed Overview of VLMs - Overview - Architecture ➡️ Architecture of Vision-Language Models ➡️ Examples of Popular VLMs and Their Architectural Choices ➡️ Differences from Large Language Models (LLMs) - Popular VLMs ➡️ GPT-4V ➡️ LLaVA ➡️ Frozen ➡️ Flamingo ➡️ OpenFlamingo ➡️ Idefics-80b ➡️ PaLI ➡️ PaLM-E ➡️ Qwen-VL ➡️ Fuyu-8B ➡️ LLaVA-Med ➡️ SPHINX ➡️ MIRASOL3B ➡️ InstructBLIP ➡️ MiniGPT-4 ➡️ MiniGPT-v2 ➡️ LLaVA-Plus ➡️ BakLLaVA ➡️ LLaVA-1.5 ➡️ CogVLM ➡️ FERRET - Popular Video LLMs ➡️ VideoPoet ➡️ LLaMA-VID - Popular Any-to-Any VLMs ➡️ CoDi ➡️ CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation ➡️ Gemini ➡️ NExT-GPT: Any-to-Any Multimodal LLM - Comparative Analysis of various VLMs 👉🏼 Connect/follow for more AI resources and drop me a message to share feedback! #artificialintelligence #machinelearning #ai #ml

📯 Yi, a new transformer-based language model that's comparable to GPT-3.5 on standard benchmarks. Yi-6B and Yi-34B both utilize Grouped-Query Attention and SwiGLU for post-attention layer. With a comprehensive data cleaning pipeline and a focus on data quality over quantity, fine-tuning on less than 10K data points yields impressive results. Yi also supports up to 200K context length and vision-language integration. Developed with a Responsible AI Safety Engine and efficient deployment infrastructure, Yi is a powerful addition to the transformer architecture family. #AI #transformer #LLM #machinelearning #yi

At last, Gemini is out. Despite being sixty pages lengthy, the technical report lacks detail. After a brief perusal, here is the synopsis. Gemini, the new language model, is out. Trained using TPUs and written in Jax, it boasts a distinct text and visual encoder architecture, similar to DeepMind's Flamingo. Gemini Ultra outperforms GPT-4, while Gemini Pro is comparable to GPT-3.5. With Nano-1 and Nano-2, Gemini is intended to run on-device. With an impressive context length of 32K, it excels at comprehending words and visuals. The significant increase in HumanEval over GPT-4 is amazing, but the Natural2Code benchmark reveals a narrower difference. While GPT-4 performs better in the 5-shot situation, Gemini is superior using COT@32. All we know about the training data is that "all data enrichment workers are paid at least a local living wage." Check out the technical report to learn more about Gemini's capabilities. #Gemini #LanguageModel #AI #DeepLearning

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

maybe this can be used to sharply align llms

Great research! One of my main focus areas is utilizing Domain Driven SLMs instead of LLMs. With a MoA of such Domain Driven Models, they are bound to provide accurate responses and better results. #NLP #LLM #GenAI #AI #Ollama #HuggingFace

𝐌𝐢𝐱𝐭𝐮𝐫𝐞-𝐨𝐟-��𝐠𝐞𝐧𝐭𝐬 (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

Just got the chance to check in-depth about Mixture-of-Experts (MoE) Simply a Mixture of Experts (MoE) for language models is a modification over the transformer architecture. The model consists of various experts. These experts each specialize in a different aspect or type of data. In an MoE model, there is also an additional gating mechanism that dynamically determines which expert or combination of experts is best suited for the given input during inference, leading to a computationally efficient algorithm.. A research by Pinecone demonstrated that the Mistral-8x-7B model implemented in a RAG architecture can surpass the performance of GPT-3.5 Turbo and Llama-2 70B. This provides evidence that mixture-of-experts models like Mistral AI can be competitive with individual large language models such as GPT-3, Llama, Claude, … References; RAG makes LLMs better and equal https://lnkd.in/dQvQksW4 Mistral 8x7B Part 1- So What is a Mixture of Experts Model? https://lnkd.in/diBSqGZS Mixture of Experts Explained https://lnkd.in/dxjaWEsu

🚀 Discover the diverse world of Large Language Models (LLMs) with our comprehensive mind map! Highlighting various categories, this visual guide simplifies the complex landscape of LLMs. 🧠 Architecture: Explore different model architectures such as Encoder-Decoder (BERT, T5), Causal Decoder (GPT-3, Llama), and Prefix Decoder (BART). 📚 Training Methodology: Understand training approaches like Supervised (RoBERTa), Unsupervised (Word2Vec), and Transfer Learning (T5). 🔍 Task Specialization: Learn about general-purpose models (Jurassic-1 Jumbo, Ollama) and domain-specific models (Biomed-BERT, LLamaIndex). 🌐 Accessibility: Compare open-source models (BERT, XLNet, Llama) with closed-source models (GPT-3, Gemini). 📏 Model Size: Discover models of all sizes, from large (GPT-3, Llama) to small (DistilBERT, TinyBERT). 📊 Modality: Differentiate between text-based models (GPT-3, BERT) and multimodal models (CLIP, DALL-E). ✨ Additional Features: Highlight interactive models (ChatGPT, Google's Meena) and enhanced memory models (Transformer-XL, Reformer). 🏢 Deployment Environment: Learn about cloud-based models (GPT-3), on-premise models (BERT), and edge-based models (TinyBERT). Stay ahead in AI with this simplified yet detailed overview. Whether you're a researcher, developer, or AI enthusiast, this guide is essential for navigating the LLM landscape. #AI #MachineLearning #LLMs #GenerativeAI #DeepLearning #ArtificialIntelligence