fine-tuning

🗂️ AI & Agents · View mindmap

title: “Fine-Tuning”

Fine-Tuning

Fine-tuning is the process of adapting a pre-trained machine learning model to perform well on a specific task or dataset by updating its parameters through additional training with new data. This technique leverages existing knowledge in the base model, reducing the need for large amounts of labeled data and enabling more efficient development cycles.

• transfer-learning
• unsloth
• gemma-4-e2b
• rag
• embedding-models
• ThinkingCap: Local AI Efficiency via Reduced Reasoning Tokens

Recent Resources

• Fine-Tune Gemma-4 on Your Own Dataset Locally: Step-by-Step Tutorial

  • Clip title: Fine-Tune Gemma-4 on Your Own Dataset Locally: Step-by-Step Tutorial
  • Author / channel: Fahd Mirza
  • URL: https

• ThinkingCap: Local AI Efficiency via Reduced Reasoning Tokens

  • Clip title: ThinkingCap - The Local Coding Model
  • Author / channel: Sam Witteveen
  • Summary: Discusses BottleCap AI’s “ThinkingCap” model series, a fine-tuned version of the Qwen 3.6-27B model, aimed at achieving local AI efficiency via reduced reasoning tokens.
  • URL: ThinkingCap: Local AI Efficiency via Reduced Reasoning Tokens