Hi guys.
Please. As there’s such term like System Prompt that specifies model basic identity, core behavior rules etc. So how actually to
fine-tune a model so that it is trained all the concepts similar to System Prompt as it’s general identity as own default chracteristic.
fine tune model to understand some set of specific short commands (phrases/words or special char sequences) so that each of that would represent a respective wide batch of rules similar to System Preference
E.g.
/polite => TL;DR Be nice and polite
/humor => TL;DR Be funny, use jokes
So that instead of long description as a prompt prefix it would be enough to add just a short tags. As Prompt Extensions still how to search this within fine tune terms.
It is possible to train a single model to be able to switch between modes, but it seems to be quite difficult. It also consumes the model’s parameters…
LLMs for role-playing (RP) are specialized for that purpose, so it might be a good idea to search for them.
If you want to be able to switch between modes with a short command, I think it would be more reliable to train several LoRA separately with PEFT, etc., and then switch between them with a program before passing them to the LLM.
To fine-tune an LLM to incorporate system prompt concepts as default behavior and recognize short commands, follow these steps:
System Prompt Integration with Prefix-Tuning: Use prefix-tuning to embed the core identity and behaviors into the model. This method involves adding a “soft prompt” that guides the model without altering its core weights, making the system prompt concepts the model’s default characteristics [2].
Short Command Recognition with PEFT: Implement Parameter-efficient Fine-tuning (PEFT) to train the model on recognizing short commands. PEFT allows efficient adaptation without full retraining, enabling the model to respond to commands like /polite or /humor with specific behaviors [4].
System Message Templates: Define clear command interpretations in the system message. Outline how each command triggers a specific behavior, ensuring the model understands the context and function of each command [3].
Retrieve and Generate (RAG) Integration: Post-retrieval, fine-tune the model to use commands for style adjustments, enhancing responses with polite or humorous tones as needed [5].
Token Management with Few-Shot Examples: Include concise examples in prompts to illustrate command use, aiding the model in learning without exceeding token limits [1].
By integrating these methods, the model will naturally adopt system prompt behaviors and respond to short commands efficiently.
Answer:
To achieve the desired fine-tuning, employ prefix-tuning for default system behaviors, PEFT for recognizing short commands, clear command definitions in prompts, and efficient token management. This approach ensures the model understands and applies commands as default behaviors without requiring lengthy prompts each time [2][4][3][5][1].