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Fine-tuning

Pre-training, post-training, fine-tuning, distillation, quantization and the data that feeds them

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  1. RAG vs Fine-Tuning: Which One Do You Actually Need?
  2. I’m not an engineer. I fine-tuned my own language model on a MacBook Air
  3. I Reproduced Pinterest’s Quantization Numbers on a Laptop, and Accidentally Worked Out Their…
  4. Bolt is giving developers 50x more compute. But there’s a catch.
  5. 5 Free Microsoft GitHub Courses to Learn Data Science and Artificial Intelligence
  6. Same bytes, closer to the original: two lines of AutoRound we had wrong
  7. When Should You Fine-Tune Instead of Prompt-Engineer?
  8. What Is an LLM Model Registry and How Is It Different from an ML Model Registry?
  9. What If the Adaptation Were a Model? ShadowPEFT in 🤗 PEFT library
  10. Fine-Tuning vs Prompting: When to Specialize an SLM
  11. Run GLM 5.3 Flash Locally: GSQ and RCO Quantization Explained
  12. Accelerating Sovereign AI: Domyn's Journey with NVIDIA
  13. Foundation Models in Radiology: What Data Do You Actually Need to Fine-Tune Them?
  14. A Practical LLM Pretraining Pipeline with LanceDB
  15. Understanding W8A8 INT8 LLM quantization: Accuracy and performance results
  16. Run 40% more post-training experiments on the same GPUs with llm-d time-slicing
  17. Same bytes, closer to the original: two lines of AutoRound we had wrong
  18. Looking for lateral movement with a neural network trained on synthetic data
  19. Is building a manual evaluation set the right approach when no reliable labeled ground truth exists for resume-job matching?
  20. NeoHorse-1-4B: How to Run This Self-Improving 4B Model Locally
  21. Recursive Self-Improvement Training: How NeoHorse-1-4B Learns From Itself
  22. Rubric Design: The Missing Layer Between Guidelines and Good Annotations
  23. Edge0-35B Benchmarks: What 4-bit Quantization Really Costs You
  24. How to Generate Labeled UI Interaction Data at Scale With Synthetic Data

Podcasts

Recent episodes

  1. AI Safety Alarms, China's Distillation Reckoning, and Qualcomm's AWS Breakthrough: A Pivotal Week for Enterprise AI
  2. Fusion power startups find new partners in the defense world; plus, Mecka AI nears $500M valuation amid rush for robot training data
  3. It's A Doozy
  4. Risky Bulletin: Ukraine's top prosecutor resigns amid scam call center scandal
  5. Risky Business #852 -- Cyber Command wants to buy shells
  6. Java’s age is its AI superpower
  7. Aaron Levie on Why Open AI Wins
  8. Aaron Levie on Why Open AI Wins
  9. Aaron Levie on Why Open AI Wins
  10. Nvidia Buys Hugging Face For A Rabbit?
  11. #255 - Gemini 3.7, Jalapeño, Qwen 3.8, Drones
  12. SN 1093: Tokens in the Stream - Why LLMs are inherently insecure and prompt injection will persist

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