LLM Engineer's Handbook — Master the art of engineering Large Language Models #LLMs from concept to production: http://
amzn.to/4dUQrv6 v/ @PacktPublishing ——
#DataScience #DataScientist #ML #GenAI #AI #GenerativeAI #MachineLearning ——
What you will learn: Implement robust
GENERATIVE AI
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Master Large Language Models Engineering from Concept to Production
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Retrieval Augmented Generation RAG Steps Explained Infographic
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#Infographic – Retrieval Augmented Generation (RAG) Steps Explained Source: https://
linkedin.com/posts/kalyanks
nlp_rag-llms-nlproc-activity-7265195109359448066-4j9f/?utm_source=share&utm_medium=member_ios
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Survey of Large Language Model-Powered GUI Agents
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A Survey of Large Language Model-Brained GUI Agents: https://arxiv.org/abs/2411.18279 (download 78-page PDF) #LLMs #GenAI #GenerativeAI #AI #MachineLearning #DataScience #DataScientist
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Stanford CS224N: Natural Language Processing with Deep Learning Course
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I like this one. Introducing Stanford CS224N: Natural Language Processing with Deep Learning https://
web.stanford.edu/class/cs224n/ -
ICLR 2025 Diffusion Model Scaling for Illumination Harmonization
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ICLR 2025 full scores paper, 10, 10, 10, 10! Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport https://
openreview.net/pdf?id=u1cQYxR
I1H
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Removing 10 Minute Session Limit for Local AI NPC
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Yeah, we removed the 10 minute session limit. Plus when running AI NPC (LLM) on your local PC, there's no cost, and no time limit to it. Transparency notice: if you will continue using TTS (text to speech), this is still running in cloud and there's cost associated to it. We
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LLM-Based Generative Agents Simulate 1000 Real Individuals
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9). Generative Agent Simulations of 1,000 People – introduces a new agent architecture that uses LLMs to create behavioral simulations of real individuals, achieving 85% accuracy in replicating human responses on the General Social Survey and reducing demographic biases compared
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TÜLU 3 releases state-of-the-art open post-trained models
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8). TÜLU 3 – releases a family of fully-open state-of-the-art post-trained models, alongside its data, code, and training recipes, serving as a comprehensive guide for modern post-training techniques.
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Qwen2.5 Achieves State-of-the-Art Math Reasoning Surpassing GPT-4o
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5). High-Level Automated Reasoning – extends in-context learning through high-level automated reasoning; achieves state-of-the-art accuracy (79.6%) on the MATH benchmark with Qwen2.5-7B-Instruct, surpassing GPT-4o (76.6%) and Claude 3.5 (71.1%).
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o1 Replication: Distillation and Fine-tuning for Math Reasoning
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3). o1 Replication Journey – Part 2 – shows that combining simple distillation from o1's API with supervised fine-tuning significantly boosts performance on complex math reasoning tasks…