Chun121/qwen3-4B-rpg-roleplay

🧙‍♂️ Qwen3-4B Roleplay LoRA

Where Characters Come Alive in Conversation

Breathe life into your digital companions with natural, engaging dialogue

✨ Model Overview

Welcome, fellow creators! I'm Chun (@chun121), and I've fine-tuned the impressive Qwen3-4B model to excel at character-based conversations and roleplay scenarios. Whether you're crafting an immersive game, building an interactive storytelling platform, or developing character-driven AI experiences, this model will help your characters speak with personality, consistency, and depth.

This LoRA adaptation maintains the intelligence of the base model while enhancing its ability to:

• 🎭 Maintain consistent character personas
• 💬 Generate authentic dialogue that reflects character traits
• 🌍 Create immersive narrative responses
• 🧠 Remember context throughout conversations

📊 Technical Specifications

Feature	Details
Base Model	Qwen3-4B
Architecture	Transformer-based LLM with LoRA adaptation
Parameter Count	4 Billion (Base) + LoRA parameters
Quantization Options	4-bit (bnb), GGUF formats (Q8_0, F16, Q4_K_M)
Training Framework	Unsloth & TRL
Context Length	512 tokens
Developer	Chun
License	Apache 2.0

🧠 Training Methodology

This LoRA was trained on a free Google Colab T4 GPU using efficient quantization techniques to maximize the limited resources:

• Dataset: PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed-split
• LoRA Configuration:
  • Rank: 16
  • Alpha: 32
  • Target Modules: Optimized for character dialogue generation
• Training Hyperparameters:
  • Batch Size: 8
  • Gradient Accumulation Steps: 4
  • Learning Rate: 1e-4 with cosine scheduler
  • Max Steps: 200
  • Precision: FP16/BF16 (auto-detected)
  • Packing: Enabled for efficient training
  • QLoRA: 4-bit quantization via bitsandbytes

📚 Dataset Deep Dive

The Gryphe-Aesir-RPG-Charcards-Opus-Mixed-split dataset is a rich collection of character interactions featuring:

• Diverse character archetypes across different genres
• Multi-turn conversations that maintain character consistency
• Varied emotional contexts and scenarios
• Rich descriptive language and character-driven responses

This carefully curated dataset helps the model understand the nuances of character voices, maintaining consistent personalities while generating engaging responses.

🚀 Getting Started

Hugging Face Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model with 4-bit quantization for efficiency
tokenizer = AutoTokenizer.from_pretrained("chun121/qwen3-4b-roleplay-lora")
model = AutoModelForCausalLM.from_pretrained(
    "chun121/qwen3-4b-roleplay-lora",
    torch_dtype=torch.float16,  # Use float16 for faster inference
    device_map="auto"           # Automatically choose best device
)

# Create a character-focused prompt
character_prompt = """
Character: Elara, an elven mage with centuries of knowledge but little patience for novices
Setting: The Grand Library of Mystral
Context: A young apprentice has asked for help with a difficult spell

User: Excuse me, I'm having trouble with the fire conjuration spell. Could you help me?

Elara:
"""

# Generate response
inputs = tokenizer(character_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    inputs["input_ids"],
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Using GGUF Models

If you're utilizing the GGUF exports with llama.cpp:

# Example command for Q4_K_M quantization
./llama -m chun121-qwen3-4b-roleplay-lora.Q4_K_M.gguf -p "Character: Elara, an elven mage..." -n 200

💡 Recommended Usage

This model works best when:

1. Providing character context: Include a brief description of the character's personality, background, and current situation
2. Setting the scene: Give context about the environment and circumstances
3. Using chat format: Structure inputs as a conversation between User/Human and Character
4. Maintaining temperature: Values between 0.7-0.8 offer a good balance of creativity and coherence

🔄 Limitations

• Limited to 512 token context window
• May occasionally "forget" character traits in very long conversations
• Training dataset focuses primarily on fantasy/RPG contexts
• As a LoRA fine-tune, inherits limitations of the base Qwen3-4B model

🔗 Related Projects

If you enjoy this model, check out these related projects:

• My other fine-tunes
• The Unsloth optimization library
• PJMixers character datasets

🙏 Acknowledgements

Special thanks to:

• The Qwen team for their incredible base model
• PJMixers-Dev for the high-quality dataset
• The Unsloth team for making efficient fine-tuning accessible
• The HuggingFace community for their continued support

📬 Feedback & Contact

I'd love to hear how this model works for your projects! Feel free to:

• Open an issue on the HuggingFace repo
• Connect with me on HuggingFace @chun121
• Share examples of characters you've created with this model

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May your characters speak with voices that feel truly alive!

Created with ❤️ by Chun