feature: <Add Support for Dynamic Model Fine-Tuning in vLLM>

Describe the feature

Description:

Support for dynamic model fine-tuning during inference.

Motivation:

Currently, vLLM is excellent for efficient inference, but many real-world applications involve adjusting models in real-time or fine-tuning them based on specific user inputs or new data. Adding the capability to fine-tune the model dynamically (while keeping inference performance optimized) would open up a wide range of possibilities, especially for applications such as:

Personalized models: Fine-tuning models based on user-specific data or preferences.
Continual learning: Allowing models to adapt to new information over time without retraining from scratch.
Domain adaptation: Dynamically adjusting models to better fit different tasks or data distributions during inference.

Proposed Feature:

1. Dynamic Fine-Tuning Mechanism- A set of API methods that allow for fine-tuning of models with minimal overhead during inference.

2. Memory-efficient Training- Implement methods that can fine-tune weights on a per-batch or even per-sample basis to minimize memory usage while performing updates.

3. Support for Adaptation to New Data- Enable the model to adjust to new or unseen data during inference, with careful control to avoid catastrophic forgetting or overfitting.

Why do you need this feature?

No response

Additional context

No response

The issue names no files, tests, or entry points. Start by reviewing the repository's model-serving and inference architecture, then identify how dynamic training could fit without disrupting inference. Done would require an agreed design, implementation scope, memory and safety constraints, and tests for the proposed behavior.