Fine-Tuning & Model Efficiency

Making models smaller, cheaper, and your own: LoRA and QLoRA fine-tuning, knowledge distillation, pruning, quantization, MoE architectures, and TPU/GPU trade-offs.

13 articles

• Geek Out Time: Agent Harness Exploration (Part 1) -What Happens When Success Is Impossible?Jul 2026
• Geek Out Time: Testing TPU vs GPU on Google Colab After Meta’s Reported Shift Toward TPUsNov 2025
• Geek Out Time: LoRA Fine-Tuning new Gemma-3–270M on a Free Colab GPUAug 2025
• Geek Out Time: Part 2- Super Cheap Fine-Tuning DeepSeek-7B with QLoRA, RunPod, and Cursor for your…Apr 2025
• Geek Out Time: Simulating Distributed Training on TPU & GPU in Google ColabFeb 2025
• Geek Out Time: Mixture of Experts(MoE) vs. CNN: A Google Colab ExperimentFeb 2025
• Geek Out Time: Knowledge Distillation in TensorFlow- Smaller, Smarter Models in Google ColabJan 2025
• Exploring LoRA on Google Colab: the Challenges of Base Model UpgradesDec 2024
• Geek Out Time: Exploration of Model Pruning for Efficient DeploymentNov 2024
• Geek Out Time: Experimenting with FP32 vs. FP16 Quantization on Google Colab’s Free T4 GPUOct 2024
• Geek Out Time: Simple Local Testing of Llama 3 on Its Release, Gemma, and Mistral MistralApr 2024
• Geek Out Time: Play with LangChain 3 — Simulate Full RAG Locally with word2vec and GemmaApr 2024
• Geek Out Time: Play with LangChain 2- locally with GemmaMar 2024