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The open-source large models released by Deepseek AI have garnered widespread attention due to their excellent performance and openness. Deploying these powerful models locally not only protects data privacy and enables offline use but also allows for customization and optimization according to individual needs, providing great convenience for developers and researchers.
In the face of the massive unstructured data (such as text, images, audio, and video) in the AI era and the demand for "semantic understanding," traditional methods are proving inadequate. At this juncture, vector databases have emerged, becoming an indispensable cornerstone of AI applications.
Let's explore this together, not with complex algorithms or technical jargon, but with a focus on understanding the core concepts and how AI is already shaping our world.
To bridge the gap between "general models" and "deep companionship," we need a comprehensive suite of plug-in technologies.
Simply put, LLaMA-Factory is currently the most popular and best-to-use "end-to-end fine-tuning toolkit" in the large model industry.
The core function of DPO (Direct Preference Optimization) is not to teach the model to "speak," but to teach it to "choose."
The essence of SFT (Supervised Fine-Tuning) is "instruction alignment," meaning teaching the model to learn: what format and tone to use when responding to a user's query.
The essence of PT (Incremental Pre-training) is: building upon the general capabilities of a large model, feeding it a massive amount of industry documents, books, or code to enable it to absorb new knowledge.
This article, from a practical engineering perspective, provides a clear and in-depth analysis of the three key actions in LLM training.