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Makar Vlasov

Makar7
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reacted to etemiz's post with 👍 2 days ago
Llama 4 Maverick got worse scores than Llama 3.1 405B in human alignment. I used CPU for inferencing from this size of a model (402B), and it ran fast. Being a mixture of experts it may be useful for CPU inference and having a big context useful for RAG. For beneficial answers there are other alternatives. Still it managed to beat Grok 3. I had so much expectations for Grok 3 because X is holding more beneficial ideas in my opinion. It got worse health scores compared to 3.1 and better bitcoin scores. I could post some comparisons of answers between the two. With which model should I publish comparisons? Llama 3.1 or Grok 3 or something else? https://sheet.zohopublic.com/sheet/published/mz41j09cc640a29ba47729fed784a263c1d08
reacted to Kseniase's post with 👍 3 days ago
11 new types of RAG RAG is evolving fast, keeping pace with cutting-edge AI trends. Today it becomes more agentic and smarter at navigating complex structures like hypergraphs. Here are 11 latest RAG types: 1. InstructRAG -> https://huggingface.co/papers/2504.13032 Combines RAG with a multi-agent framework, using a graph-based structure, an RL agent to expand task coverage, and a meta-learning agent for better generalization 2. CoRAG (Collaborative RAG) -> https://huggingface.co/papers/2504.01883 A collaborative framework that extends RAG to settings where clients train a shared model using a joint passage store 3. ReaRAG -> https://huggingface.co/papers/2503.21729 It uses a Thought-Action-Observation loop to decide at each step whether to retrieve information or finalize an answer, reducing unnecessary reasoning and errors 4. MCTS-RAG -> https://huggingface.co/papers/2503.20757 Combines RAG with Monte Carlo Tree Search (MCTS) to help small LMs handle complex, knowledge-heavy tasks 5. Typed-RAG - > https://huggingface.co/papers/2503.15879 Improves answers on open-ended questions by identifying question types (a debate, personal experience, or comparison) and breaking it down into simpler parts 6. MADAM-RAG -> https://huggingface.co/papers/2504.13079 A multi-agent system where models debate answers over multiple rounds and an aggregator filters noise and misinformation 7. HM-RAG -> https://huggingface.co/papers/2504.12330 A hierarchical multi-agent RAG framework that uses 3 agents: one to split queries, one to retrieve across multiple data types (text, graphs and web), and one to merge and refine answers 8. CDF-RAG -> https://huggingface.co/papers/2504.12560 Works with causal graphs and enables multi-hop causal reasoning, refining queries. It validates responses against causal pathways To explore what is Causal AI, read our article: https://www.turingpost.com/p/causalai Subscribe to the Turing Post: https://www.turingpost.com/subscribe Read further 👇
reacted to aiqtech's post with 🔥 3 days ago
🌐 AI Token Visualization Tool with Perfect Multilingual Support Hello! Today I'm introducing my Token Visualization Tool with comprehensive multilingual support. This web-based application allows you to see how various Large Language Models (LLMs) tokenize text. https://huggingface.co/spaces/aiqtech/LLM-Token-Visual ✨ Key Features 🤖 Multiple LLM Tokenizers: Support for Llama 4, Mistral, Gemma, Deepseek, QWQ, BERT, and more 🔄 Custom Model Support: Use any tokenizer available on HuggingFace 📊 Detailed Token Statistics: Analyze total tokens, unique tokens, compression ratio, and more 🌈 Visual Token Representation: Each token assigned a unique color for visual distinction 📂 File Analysis Support: Upload and analyze large files 🌏 Powerful Multilingual Support The most significant advantage of this tool is its perfect support for all languages: 📝 Asian languages including Korean, Chinese, and Japanese fully supported 🔤 RTL (right-to-left) languages like Arabic and Hebrew supported 🈺 Special characters and emoji tokenization visualization 🧩 Compare tokenization differences between languages 💬 Mixed multilingual text processing analysis 🚀 How It Works Select your desired tokenizer model (predefined or HuggingFace model ID) Input multilingual text or upload a file for analysis Click 'Analyze Text' to see the tokenized results Visually understand how the model breaks down various languages with color-coded tokens 💡 Benefits of Multilingual Processing Understanding multilingual text tokenization patterns helps you: Optimize prompts that mix multiple languages Compare token efficiency across languages (e.g., English vs. Korean vs. Chinese token usage) Predict token usage for internationalization (i18n) applications Optimize costs for multilingual AI services 🛠️ Technology Stack Backend: Flask (Python) Frontend: HTML, CSS, JavaScript (jQuery) Tokenizers: 🤗 Transformers library
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upvoted a collection 29 days ago