David Andrés 🤖📈🐍 @daansan_ml
📈 I summarise Machine Learning and Time Series concepts in an easy and visual way • 💊Follow me in https://t.co/hy44Q0jFQs 👉 Inquiries in [email protected] mlpills.substack.com Spain Joined May 2022-
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I bet you've heard a lot about RAG recently... 👉 But what is it? 👉 And what does it consist of? Find out more about this here 🧵 👇
Finetuning adapts a pre-trained LLM to a specific task or domain by further training it on relevant data, for example, to obtain an LLM specialized in law, coding or even medicine! Let's learn more about finetuning! 🧵👇
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2K Followers 486 Following Ingenieros de día e inversores de noche | Promovemos la Educación Financiera 💵| Hablamos de Finanzas Personales e Inversión 📈| Blog ⬇️@daansan_ml Thank you for your 🧵 Strangely not many people have heard about Decentralized #RAG yet. Which would really advance #AI into Real Openness (and could directly benefit You! ) x.com/origin_trail/s…
Discover how @origin_trail DKG unlocks the full potential of a Truly Open #AI & learn how #dRAG paves the way for a Verifiable Internet!🌐 Don't miss out as our very own @BranaRakic takes the stage at the 2024 @KGConference, May 8 & 9!🗽 events.knowledgegraph.tech/event/7ffec6d4… #KGC2024
@daansan_ml Expand the question 4️⃣ using LLM to get broader/context narrowed (as per wish) set of questions to embed and search for. Re-rank the results, etc. 😉
#RAG...
I bet you've heard a lot about RAG recently... 👉 But what is it? 👉 And what does it consist of? Find out more about this here 🧵 👇
@shabarish_99 @daansan_ml @UnrollHelper @shabarish_99 Salam, the unroll you asked for: threadreaderapp.com/thread/1784250… See you soon. 🤖
4️⃣ Magic AI Tool of the Week 🪄 Access Meta's Llama 3 easily through the HuggingChat app. 👇🏽 1. Download the HuggingChat app 2. Start the app. Sign in or sign up. 3. Click on the settings icon and select the model "meta-llama/Meta-Llama-3-70B-Instruct" 4. Start chatting!
3️⃣ Microsoft releases a new SLM for on-device AI - According to Microsoft "Phi-3 models are the most capable and cost-effective small language models (SLMs) available" - It is instruction-tuned and available on Microsoft Azure, Hugging Face and Ollama
2️⃣ Sanctuary AI presents Gen 7 of its humanoid robot Phoenix - The robot can learn new tasks in less than 24 hours - Longer operating times and significantly lower cost of manufacture
1️⃣ Apple unveils OpenELM for on-device AI - Family of eight models (four models are instruction-tuned) with four different parameter sizes (270M, 450M, 1.1B, 3B) - Apple also open-sourced its library CoreNet. CoreNet is a library for training deep neural networks.
🤖 Missed this week's AI News? Don't worry! We always keep you up to date! 😀 Let's explore this week's Magic AI News together. 🧵👇🏽
Mastering Bollinger Bands: A Comprehensive Guide with Python and OpenBB | by @tinztwins towardsfinance.com/mastering-boll…
@daansan_ml Great post! RAG is powerful! Well explained, David!
This approach allows LLMs to go beyond just their training data, dynamically incorporating relevant external knowledge into their outputs. It powers applications that require access to latest data not present in the original model training.
5️⃣ Language Model Generation The relevant retrieved chunks and the original query are fed into a large language model, which uses this combined context to generate a fluid, coherent response addressing the user's question or request.
4️⃣ Handling User Queries When a question is submitted, it too is converted into an embedding vector. The embedding is then compared against the document embeddings to identify and retrieve the most semantically similar chunks of text relevant to the query.
3️⃣ Document Embeddings The chunks are converted into numeric vector embeddings - compressed representations that capture the semantics and meaning of the text data. This allows matching queries to relevant chunks based on conceptual similarity rather than just keyword matching.
2️⃣ Data Chunking Large documents are broken down into smaller, topic-focused chunks or passages. This separates the data into more granular, context-specific sections to allow retrieval of just the relevant pieces needed to answer a given query.