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Tuesday 10 November 2026 11:06:57 GMT
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Hỗ trợ eo thon dáng đẹp tại nhà tiện lợi
Hỗ trợ eo thon dáng đẹp tại nhà tiện lợi
AI for Beginners #11: what is fine-tuning? 🎯 Episode 1: LLMs. Episode 2: tokens. Episode 3: prompts. Episode 4: APIs. Episode 5: AI agents. Episode 6: temperature. Episode 7: context windows. Episode 8: embeddings. Episode 9: hallucinations. Episode 10: RAG. Today: fine-tuning, teaching a model your task with examples. • IBM: fine-tuning is the process of adapting a pre-trained model for specific tasks or use cases. It's easier and cheaper than training a new model from scratch. • Google Cloud: start with prompting to find the best prompt, then move to fine-tuning if needed to boost performance or fix recurring errors. Fine-tuning works best when you have labeled data (think 100 examples or more) and the task is too complex for prompting alone. • How it works (OpenAI supervised fine-tuning): build a dataset of example prompts and good answers in JSONL, upload it, create a fine-tuning job on a base model, then evaluate the result. The minimum is 10 examples; OpenAI recommends starting with 50 well-crafted ones. • Why bother? Shorter prompts with fewer examples save tokens and can cut latency, and a small, cheaper model can be trained to do one task well. • The cheap way: parameter-efficient methods like LoRA keep the original weights frozen and train two small matrices of updates instead, which speeds up tuning and needs less memory. • Heads up: OpenAI is winding down its fine-tuning platform. It's closed to new users, and eligible existing customers can create training jobs until January 6, 2027. Sources: IBM Think,
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تريند يقيف هنا 😌✨💋
تريند يقيف هنا 😌✨💋

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