Fine-Tuning
AI / MCPDefinition
Fine-tuning is the process of further training a pre-trained language model on a specific dataset to specialize its behavior for a particular task or domain. It adapts general-purpose models to targeted use cases.
How It Relates to CrawlForge
Fine-tuning requires large, high-quality datasets of domain-specific text. Collecting this data from the web is one of the most common use cases for web scraping at scale. The quality of the training data directly impacts the fine-tuned model's performance.
CrawlForge batch_scrape and extract_content are designed for this workflow. Use batch_scrape to process hundreds of URLs in parallel, and extract_content to get clean, structured text suitable for training. This pipeline can build datasets from documentation sites, forums, academic papers, or any web source.
Related CrawlForge Tools
Related Terms
Large Language Model (LLM)
A large language model is a neural network trained on vast amounts of text data that can understand and generate human language. LLMs power AI assistants, code generators, and autonomous agents.
Token
A token is the basic unit of text that language models process. Text is split into tokens (roughly 4 characters or 0.75 words each) before being processed by the model. Token counts determine costs and context limits.
Embeddings
Embeddings are dense numerical vector representations of text, images, or other data. They capture semantic meaning in a format that enables similarity search, clustering, and other machine learning operations.
AI Agent
An AI agent is an autonomous system powered by a large language model that can reason about tasks, make decisions, and take actions by using tools. Agents go beyond simple chatbots by planning and executing multi-step workflows.
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