This section of the course focuses on learning how to build the best possible LLMs using the latest techniques. 1. The LLM architecture While an in-depth knowledge about the Transformer architecture is not required, it is important to have a good understanding of its inputs (tokens) and outp...
✨✨✨ Behold our meticulously curated trove of Multimodal Large Language Models (MLLM) resources! 📚🔍 Feast your eyes on an assortment of datasets, techniques for tuning multimodal instructions, methods for multimodal in-context learning, approaches for multimodal chain-of-thought, visual re...
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The quality of the responses is very good, approaching that of ChatGPT for many prompts. I have tested responses using prompts from theDeeplearning.ai short course ChatGPT Prompt Engineering for Developers(recommended!). Note that the model is trained on a different corpus than ChatGPT,...
Developing LLM Applications with LangChain course How to Build LLM Applications with LangChain tutorial Building LangChain Agents to Automate Tasks in Python tutorial An Example AI Learning Plan Below, we’ve created a potential learning plan outlining where to focus your time and efforts if you’...
aichat - All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI Tools & Agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more. ast-grep - A CLI tool for code structural search, lint and rewriting. Bartib [Bartib] - A simple timetracker for the com...
Developing LLM Applications with LangChain course How to Build LLM Applications with LangChain tutorial Building LangChain Agents to Automate Tasks in Python tutorial An Example AI Learning Plan Below, we’ve created a potential learning plan outlining where to focus your time and efforts if you’...
RQ1. How do LLMs make a positive impact on security and privacy across diverse domains, and what advantages do they offer to the security community? • RQ2. What potential risks and threats emerge from the utilization of LLMs within the realm of cybersecurity? • RQ3. What vulnerabiliti...
Agents in LangChain use recursive calls to the LLM to decide the next step to take based on the current state. The two planner implementations in SK are not self-correcting. Sequential planner tries to produce all the steps at the very beginning, so it is unable to handle unexpected errors...
⭐ Arize-Phoenix - ML observability for LLMs, vision, language, and tabular models ⭐ whylogs - open source standard for data and ML logging [GitHub, 2636 stars] ⭐ Rubrix - open-source tool for exploring and iterating on data for artificial intelligence projects [GitHub, 3843 stars]...