Qwen3 4B Rpg Roleplay

🚀 Qwen3-4B Roleplay LoRA

Where Characters Come Alive in Conversation

This LoRA fine-tuned model based on Qwen3-4B is designed for character-based conversations and roleplay scenarios. It helps bring digital companions to life with natural and engaging dialogue.

🚀 Quick Start

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

✨ Features

• Maintain consistent character personas.
• Generate authentic dialogue that reflects character traits.
• Create immersive narrative responses.
• Remember context throughout conversations.

📦 Installation

The installation steps are included in the Quick Start section with the code examples for using the model in different ways.

📚 Documentation

Technical Specifications

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.

🔧 Technical Details

The model is a LoRA fine-tuned version of the Qwen3-4B base model. It uses efficient quantization techniques and is trained on a specific dataset to enhance its performance in character-based conversations and roleplay scenarios.

💡 Usage Tip

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.

⚠️ Important Note

• 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.

📄 License

This model is licensed under the Apache 2.0 license.

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.