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#Largelanguagemodels Reel by @codevaa - Why does one AI model perform better than another? It all comes down to how they're trained. Every AI model learns from huge datasets - but the quali
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@codevaa
Why does one AI model perform better than another? It all comes down to how they’re trained. Every AI model learns from huge datasets - but the quality, diversity, and amount of data matter. Then there’s the architecture: some models have more parameters (neurons) that help them understand context better, while others are optimized for speed or accuracy. Even the fine-tuning phase - where models are taught specific skills like reasoning, creativity, or coding - can make a huge difference in how smart and human-like they feel. Now, let’s talk about prompt engineering: Even the best model needs the right question to give the best answer. Here are a few quick tips: 🧠 Be specific : vague prompts = vague outputs. 🎯 Add context : tell the AI who it should be (e.g., “act as a teacher” or “marketing expert”). 📋 Use examples : showing structure helps the model mimic the style. ⏳ And always iterate : refine your prompt until you hit the perfect response. Follow for more tips on AI and informative content on software engineering. Link of the Website: https://claude.ai/public/artifacts/e323bfdf-e47b-4537-b285-d18663060a8a #AI #chatgpt #claudai #claud #artificialintelligence #agents #comparison #software #aimodels #aiagents #llmmodels #llm #largelanguagemodels
#Largelanguagemodels Reel by @awomanindatascience - It's Day 14 of building a LLM from scratch ✨ Most people think LLMs are complex because of code. They're complex because of configuration and scale.
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@awomanindatascience
It’s Day 14 of building a LLM from scratch ✨ Most people think LLMs are complex because of code. They’re complex because of configuration and scale. Today I broke down the GPT-2 config that defines how the model thinks, remembers, and attends. GPT-2 is just a set of numbers that define scale: vocab size, context length, embedding dimension, layers, and attention heads. Breaking down the GPT-2 (124M) configuration: 50,257-token vocabulary, 1,024-token context, 768-dimensional embeddings, 12 transformer layers with 12 attention heads, dropout 0.1, and bias-free QKV projections. Understanding these parameters is key to scaling LLMs efficiently. #deeplearning #generativeai #womenwhocode #largelanguagemodels
#Largelanguagemodels Reel by @waterforge_nyc - Breaking down how Large Language Models work, visualizing how data flows through. #llm #largelanguagemodels #visualization #data #ai
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@waterforge_nyc
Breaking down how Large Language Models work, visualizing how data flows through. #llm #largelanguagemodels #visualization #data #ai
#Largelanguagemodels Reel by @datascience.diaries (verified account) - Why does AI sometimes sound like a textbook… and other times like a poet? It's all about Temperature in Large Language Models. Think of temperature
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@datascience.diaries
Why does AI sometimes sound like a textbook… and other times like a poet? It’s all about Temperature in Large Language Models. Think of temperature as a randomness control knob: 🧊 Low (0–0.3) → Precise, factual, repetitive ⚖️ Medium (0.4–0.7) → Balanced, natural responses 🔥 High (0.8–1.0+) → Creative, surprising, unpredictable Same model. Same prompt. Different temperature → Completely different personality. 👉 Use LOW for coding, math, research 👉 Use HIGH for storytelling, brainstorming, marketing Master this one setting, and you’ll instantly get better AI outputs 💡 Follow for more simple, powerful AI concepts explained daily. #artificialintelligence #generativeai #datascience #aiforbeginners #largelanguagemodels [LLM ArtificialIntelligence MachineLearning GenerativeAI DataScience PromptEngineering AIExplained DeepLearning AItools TechEducation LearnAI AIReels AIContent OpenAI ChatGPTTips]
#Largelanguagemodels Reel by @rahulrajpvr7d - Best resources to learn Large Language Models (LLMs) | Episode 01 If you're serious about mastering LLMs, these are the books you need: 1. LLM Engin
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@rahulrajpvr7d
Best resources to learn Large Language Models (LLMs) | Episode 01 If you're serious about mastering LLMs, these are the books you need: 1. LLM Engineer's Handbook (by Paul Iusztin & Maxime Labonne) Who it's for: This one is perfect if you already know the basics and want to go deeper into deployment and production-level work. 2. Build a Large Language Model (by Sebastian Raschka) Who it's for: The best choice if you prefer learning in a simple, "from-scratch" way to understand how they're built from the ground up. 3.Hands-On Large Language Models (by Jay Alammar & Maarten Grootendorst) Who it's for: My personal favorite! It does an amazing job explaining everything simply, using lots of figures and visual guides. Which one are you adding to your list? Let me know your top LLM book recommendations in the comments! (Disclaimer: No AI was used to create this video content, only for writing this caption.) #LLM #LargeLanguageModels #AI #MachineLearning #DataScience #TechBooks #BookRecommendations #Engineering #SebastianRaschka #JayAlammar #LLMEngineer Thank you so much @uallibraries for hearing my suggestions and adding these books to the library ✨🩵 @unioftheartslondon @ual_ces @ual_cci
#Largelanguagemodels Reel by @tectalks.io (verified account) - Breaking down how Large Language Models work, visualizing how data flows through. #llm #largelanguagemodels #visualization #data
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@tectalks.io
Breaking down how Large Language Models work, visualizing how data flows through. #llm #largelanguagemodels #visualization #data
#Largelanguagemodels Reel by @codingmermaid.ai (verified account) - 12 months data science plan ⚙️ Save this reel for later and prepare to screenshot. There are 6 essential skills you should learn to become a data
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@codingmermaid.ai
12 months data science plan ⚙️ Save this reel for later and prepare to screenshot. There are 6 essential skills you should learn to become a data scientists. Others come as a bonus but are equally important throughout your career - Programming (Python or R) - Probability and statistics - EDA - Machine learning - Deep learning - SQL Many people want to get started with data science but they don't know what to learn first or where to start. Luckily, I have a plan - and only in the next 24h,I'll share it with you! Make sure to follow @codingmermaid.ai and comment "plan 📊" and I'll DM it to you! Don't let the career of your dreams wait 👩🏼‍💻 #datascience #researchpaper #womeninstem #datascientist #dataanalyst #machinelearningengineer #datasciencetools #datascienceeducation #machinelearningalgorithms #deeplearningmachine #datadriven #dataanalytics #dataset #machinelearning #deeplearning #computervision #naturallanguageprocessing #largelanguagemodels #sql
#Largelanguagemodels Reel by @harpercarrollai (verified account) - The Power of Chain-of-Thought Prompting and Reasoning Models We talk about a powerful prompting technique called chain-of-thought prompting, which ha
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@harpercarrollai
The Power of Chain-of-Thought Prompting and Reasoning Models We talk about a powerful prompting technique called chain-of-thought prompting, which has become the foundation for a new type of large language model called reasoning models. This technique prompts the model to reason through its steps, leading to improved accuracy on complex tasks. We’ll discuss how reasoning models work and how they are trained on chain-of-thought reasoning examples. #ChainOfThoughtPrompting #ReasoningModels #LargeLanguageModels #MachineLearning #ArtificialIntelligence
#Largelanguagemodels Reel by @ainehiedoro - This semester I am teaching a course called "AI AND THE IDEA OF WRITING", a literature course that brings together the study of gen-AI with the long h
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AI
@ainehiedoro
This semester I am teaching a course called “AI AND THE IDEA OF WRITING”, a literature course that brings together the study of gen-AI with the long history, politics, and culture of writing as a technology of knowledge-making. The biggest gift the course has given me and my students is the chance to create ENOUGH distance from both hype and panic, so we can figure out the right questions to ask about large language models like ChatGPT. 4 weeks in, and I’ve learned so much! More to come. #AIandWriting #TeachingWithAI #AIinEducation #DigitalHumanities #AICulture #LargeLanguageModels #AIandLiterature #HumanitiesAndAI
#Largelanguagemodels Reel by @s_07khudi - Die Podcast-Mini-Serie über Bias in Large Language Models (LLMs) startet bald 🎥📹 #mindwalkwithsina #biasinai #aipodcast #largelanguagemodels #c
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@s_07khudi
Die Podcast-Mini-Serie über Bias in Large Language Models (LLMs) startet bald 🎥📹 #mindwalkwithsina #biasinai #aipodcast #largelanguagemodels #chatgpt4o
#Largelanguagemodels Reel by @busycaesar - Human-in-the-loop #humanintheloop #generativeai #largelanguagemodels #artificalintelligence #aiengineering
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@busycaesar
Human-in-the-loop #humanintheloop #generativeai #largelanguagemodels #artificalintelligence #aiengineering
#Largelanguagemodels Reel by @priyal.py - Chatbot for FAQs Fine-tune a pretrained LLM to answer domain-specific questions (e.g., product FAQs). Tech Stack: Python, HuggingFace Transformers, Py
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@priyal.py
Chatbot for FAQs Fine-tune a pretrained LLM to answer domain-specific questions (e.g., product FAQs). Tech Stack: Python, HuggingFace Transformers, PyTorch, Datasets LegalDoc Assistant Fine-tune GPT/LLaMA on legal text to summarize contracts or answer legal queries. Tech Stack: HuggingFace, PyTorch, LangChain, PDF parsing libraries Code Completion Model Fine-tune CodeLlama or CodeT5 on a repo of code for auto-completion and suggestions. Tech Stack: HuggingFace, PyTorch, Tokenizers, GitHub API Emotion-Aware Chatbot Fine-tune an LLM to recognize emotions in messages and respond empathetically. Tech Stack: PyTorch, HuggingFace, GoEmotions Dataset, PEFT (LoRA/Adapters) Summarization Model Fine-tune BART or T5 to summarize articles, meeting notes, or emails. Tech Stack: HuggingFace, PyTorch Lightning, Datasets Customer Review Analyzer Fine-tune a small LLM on product reviews to generate insights, sentiment, or suggestions. Tech Stack: Transformers, PyTorch, Pandas, Sklearn Domain-Specific RAG Model Fine-tune an LLM to retrieve and answer questions from your company’s knowledge base. Tech Stack: LangChain, ChromaDB/FAISS, HuggingFace, PyTorch TinyGPT for Chat Fine-tune a small GPT model on your own chat logs for personal assistants. Tech Stack: PyTorch, HuggingFace, Tokenizers, WandB #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #ai #llm #largelanguagemodels

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