Giving personality to LLM

Beginners
1

Hello, I want to do a project wherein I’d fine tune my LLM to give it a personality of a game character and based on that the LLM would interact with you, there are a lot of things that has happened with the character which shapes his personality and there are a lot of other characters in game which the character interacts differently. I don’t know if this is feasible at all, and I’d be very happy if I’d get any guidance from y’all
Thanks :slight_smile:

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2

I think it’s possible to do it like this. First, choose the LLM you want to base it on, and then I think you can make it efficiently by training with something called QLoRA. I think you can use the data you’ve prepared by processing it.

Reference

Gemma2 2Bをファインチューニング（QLoRA）でキャラクター化（ずんだもん、つくよみちゃん） # in Japanese language
Fine Tune Large Language Model (LLM) on a Custom Dataset with QLoRA | by Suman Das | Medium
Efficient Training on a Single GPU

by Hugging Chat

To achieve your goal of fine-tuning a Large Language Model (LLM) to mimic a game character’s personality using Hugging Face Trainer with VRAM-efficient training (QLoRA), follow this comprehensive and organized approach:

Step-by-Step Guide

  1. Define the Game Character’s Personality:

    • Personality Traits: Identify key traits, behaviors, and interaction patterns of the game character. Consider how the character interacts with others, their tone, and any unique mannerisms.
    • Background and Context: Understand the character’s backstory, motivations, and relationships within the game. This context will help in creating a dataset that reflects the character’s personality accurately.

  2. Create a Custom Dataset:

    • Dialogue Collection: Gather or generate a dataset of dialogues that showcase the character’s interactions. Include various scenarios and conversations with different characters to ensure diversity.
    • Context Information: Prepend each dialogue with context about the conversation partner to help the model adapt its responses accordingly.
    • Ensure Originality: Avoid copying existing styles or personalities. Use clear prompts to guide the generation towards the desired personality.

  3. Set Up the Hugging Face Environment:

    • Choose a Base Model: Select an appropriate LLM from Hugging Face, such as Mistral or Llama, considering your hardware capabilities.
    • Install Required Libraries: Ensure you have the necessary libraries installed, including those for QLoRA and RAG if you plan to use them.

  4. Implement VRAM-Efficient Training with QLoRA:

    • Install and Configure QLoRA: Follow the official documentation to install and set up QLoRA. This technique allows training on consumer-grade GPUs by reducing memory usage.
    • Modify Training Script: Integrate QLoRA into your training script. This may involve adjusting configurations for efficient memory usage and optimization.

  5. Fine-Tune the Model:

    • Prepare the Dataset: Convert your custom dataset into a format compatible with the Hugging Face Trainer, such as input-output pairs.
    • Use Hugging Face Trainer: Utilize the Trainer for fine-tuning. Configure settings like batch size and optimization parameters according to your hardware limits.
    • Prompt Engineering: Design effective prompts to guide the model during training. Prepend prompts with instructions to maintain the character’s personality.

  6. Evaluate and Iterate:

    • Manual Testing: Interact with the model to assess if it captures the character’s personality. Note any deviations from the expected behavior.
    • Automated Metrics: Consider using metrics like BLEU or ROUGE to quantify performance, though these are more suited for text generation accuracy than personality.
    • Iterate: Based on evaluation, refine the training data, prompts, and model configurations to improve results.

  7. Deploy the Model:

    • Real-Time Interaction: Deploy the fine-tuned model using Hugging Face’s deployment tools, allowing users to interact with the character through a web interface or API.
    • Consider RAG: If needed, integrate Retrieval-Augmented Generation (RAG) to enhance responses with external knowledge, such as game lore.

  8. Ensure Data Privacy and Ethics:

    • Review Data Sources: Confirm that your training data does not infringe on privacy or intellectual property rights.
    • Monitor Usage: Implement safeguards to prevent misuse of the model and ensure ethical deployment.

  9. Leverage Community Resources:

    • Seek Guides and Tutorials: Utilize Hugging Face documentation, forums, and community tutorials for step-by-step guidance and troubleshooting.
    • Join Forums: Engage with communities like Reddit’s r/HuggingFace or specialized AI forums for support and insights.

Conclusion

By following these steps, you can effectively fine-tune an LLM to embody your game character’s personality. Start with smaller models to familiarize yourself with the process, then scale up as needed. Leverage QLoRA for efficient training and consider integrating RAG for enhanced capabilities. Regular evaluation and iteration will ensure the model accurately reflects the character’s traits and interactions, providing an engaging and immersive experience for users.

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