5.85% → 77.4% accuracy in 30 steps—using a free Colab GPU. Full tutorial below. скачать в хорошем качестве

5.85% → 77.4% accuracy in 30 steps—using a free Colab GPU. Full tutorial below.

📓 Notebook: https://colab.research.google.com/git... ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Fine-tune embedding models 2x faster with Unsloth. This tutorial shows you how to fix your RAG retrieval by training embeddings on your own domain data. 🔗 RESOURCES Unsloth Embedding Docs: https://docs.unsloth.ai/ EmbeddingGemma-300M: https://huggingface.co/google/embeddi... Medical Dataset: https://huggingface.co/datasets/tomaa... ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 📚 GO DEEPER: Understand What Those Metrics Mean Want to know WHY those numbers improved? I break down: • Every retrieval metric explained (MRR, NDCG, Precision@k) • How the loss function actually works (contrastive learning deep dive) • Annotated notebook with hyperparameter experiments • Debugging guide when metrics don't improve First 100 founding members - Foundation tier:$15/month (locked in forever) → https://www.skool.com/llm-implementat... ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ⏱️ TIMESTAMPS 00:00 Intro & Results Preview 00:42 Why Retrieval Quality Matters 01:24 Unsloth Features & Speed 02:09 Setup & Loading Model 02:47 Adding LoRA Adapters 03:07 Medical Dataset Prep 03:44 Baseline Model Performance 04:32 Training Configuration 05:14 Fine-Tuned Results Evaluation 05:49 Real-World Inference Test 06:09 Saving & Exporting Models 06:22 Metrics Guide & Outro ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #unsloth #embeddings #rag #finetuning #machinelearning #llm #python #colab #tutorial