freeCodeCamp·37wHow to Fine-Tune Large Language Models
A comprehensive course covering fine-tuning techniques for large language models, including supervised fine-tuning, reinforcement learning with human feedback (RLHF), and QLoRA methodology. The course explains the differences between fine-tuning, pre-training, and prompt engineering, with practical applications and case studies for specializing LLMs for specific domains.
Daily Dose of Data Science | Avi Chawla | Substack·38w8 Key LLM Development Skills for AI Engineers
Outlines eight essential skills for AI engineers working with Large Language Models in production environments: prompt engineering, context engineering, fine-tuning, RAG systems, agents, deployment, optimization, and observability. Each skill covers practical techniques from crafting structured prompts to implementing monitoring systems, with emphasis on moving beyond basic prompting to building scalable, production-grade LLM applications.
Towards Data Science·36wHow to Become a Machine Learning Engineer (Step-by-Step)
A comprehensive roadmap for becoming a machine learning engineer, covering essential skills from mathematics and statistics to Python programming, SQL, machine learning algorithms, deep learning, software engineering practices, and MLOps. The guide emphasizes practical learning with specific resource recommendations for each area, highlighting that engineering skills are often more important than theoretical knowledge for career success.
ByteByteGo·35wHow Fine-Tuning Transforms Generic AI Models into Specialists
Fine-tuning transforms generic AI models into specialized tools by adjusting their neural network weights for specific tasks. While training models from scratch costs millions, fine-tuning existing models like GPT or Claude costs only hundreds or thousands of dollars. The process includes instruction fine-tuning, reinforcement learning from human feedback (RLHF), and domain adaptation. Breakthrough techniques like LoRA and QLoRA have democratized AI customization by reducing memory requirements from 500GB to 20GB and enabling fine-tuning on consumer hardware, making specialized AI accessible to small organizations and researchers.
Sebastian Raschka·37wUnderstanding and Implementing Qwen3 From Scratch
A comprehensive guide to implementing Qwen3, one of the leading open-source large language models, from scratch using pure PyTorch. The article explores why Qwen3 is popular among developers, including its Apache License v2.0, strong performance rankings, and variety of model sizes from 0.6B to 480B parameters. It provides hands-on code implementation to understand the architecture's inner workings.
Hugging Face·37wJupyter Agents: training LLMs to reason with notebooks
Hugging Face developed Jupyter Agent, a system that trains small language models to perform data science tasks by executing code in Jupyter notebooks. They created a comprehensive pipeline starting with 2TB of Kaggle notebooks, applied deduplication and quality filtering, generated synthetic question-answer pairs, and fine-tuned Qwen3-4B models. The approach achieved 75% accuracy on easy DABStep benchmark tasks, demonstrating that smaller models can become effective data science agents with proper training data and scaffolding. The project includes open-source datasets, trained models, and a simplified 200-line scaffolding system.
YouTube·37wAI & ML Full Course 2025 | Complete Artificial Intelligence and Machine Learning Tutorial | Edureka
A comprehensive beginner-friendly course covering artificial intelligence and machine learning fundamentals. Explores AI history from the Turing test to modern applications, explains the differences between AI, ML, and deep learning, and discusses various AI types from narrow to super intelligence. Covers Python's role in AI development, essential libraries like TensorFlow and scikit-learn, and practical applications in cybersecurity and entertainment. Includes hands-on examples and prepares learners for building intelligent systems that can make predictions and solve real-world problems.
Daily Dose of Data Science | Avi Chawla | Substack·37wBuild a Reasoning LLM using GRPO
Group Relative Policy Optimization (GRPO) is a reinforcement learning method that fine-tunes large language models for math and reasoning tasks using deterministic reward functions, eliminating the need for labeled data. The process involves generating multiple candidate responses, assigning rewards based on deterministic functions, and using GRPO loss to update the model through backpropagation. A practical implementation demonstrates using UnslothAI and HuggingFace TRL to transform a base model into a reasoning-capable system, with reward functions that validate response format and correctness without manual labeling.