From transfer-learning-adapter
Fine-tunes pre-trained ML models like ResNet, BERT, GPT on new datasets via transfer learning, generating Python code with validation and metrics.
How this skill is triggered — by the user, by Claude, or both
Slash command
/transfer-learning-adapter:adapting-transfer-learning-modelsThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Adapt pre-trained models (ResNet, BERT, GPT) to new tasks and datasets through fine-tuning, layer freezing, and domain-specific optimization.
Adapt pre-trained models (ResNet, BERT, GPT) to new tasks and datasets through fine-tuning, layer freezing, and domain-specific optimization.
This skill streamlines the process of adapting pre-trained machine learning models via transfer learning. It enables you to quickly fine-tune models for specific tasks, saving time and resources compared to training from scratch. It handles the complexities of model adaptation, data validation, and performance optimization.
This skill activates when you need to:
User request: "Fine-tune a ResNet50 model to classify images of different types of flowers."
The skill will:
User request: "Adapt a BERT model to perform sentiment analysis on customer reviews."
The skill will:
This skill can be integrated with other plugins for data loading, model evaluation, and deployment. For example, it can work with a data loading plugin to fetch datasets and a model deployment plugin to deploy the adapted model to a serving infrastructure.
The skill produces structured output relevant to the task.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin transfer-learning-adapterProvides access to pre-trained transformer models for NLP, vision, audio, and multimodal tasks. Supports inference, fine-tuning, and tasks like text generation, classification, question answering.
Guides LLM fine-tuning with LoRA/QLoRA, dataset preparation, hyperparameter tuning, evaluation, and deployment. Useful for adapting foundation models to custom tasks.
Loads pre-trained Hugging Face Transformers for text, vision, audio, and multimodal inference and fine-tuning. Use for generation, classification, QA, translation, summarization, and object detection.