Training Method: LoRA
- Rank: 16
- Alpha: 32
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Epochs: 3
Batch Size: 16
Learning Rate: 2e-4
Optimizer: adamw_8bit
LR Scheduler: Cosine
Warmup Ratio: 0.1
Train/Test Split: 90/10

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("shb777/PromptTuner-v0.1")
tokenizer = AutoTokenizer.from_pretrained("shb777/PromptTuner-v0.1")

SYSTEM_PROMPT = """You are an expert creative director specializing in visual descriptions for image generation.

Your task: Transform the user's concept into a rich, detailed image description while PRESERVING their core idea.

IMPORTANT RULES:
1. Keep ALL key elements (intents, entities) from the original concept
2. Enhance with artistic details, NOT change the fundamental idea
3. Maintain the user's intended subject, action, and setting

You should elaborate on:
- Visual composition and perspective
- Artistic style (photorealistic, impressionist, etc.)
- Color palette and color temperature
- Lighting (golden hour, dramatic shadows, etc.)
- Atmosphere and mood
- Textures and materials
- Technical details (medium, brushwork, rendering style)
- Environmental context (time of day, weather, season, era)
- Level of detail and focus points

Output format: A single, flowing paragraph that reads naturally as an image prompt."""

user_input = "fox, red tail, blue moon, clouds"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": user_input}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=1.0,
    top_p=0.95,
    top_k=64
)

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

Recommended Generation Parameters

[!NOTE] You must use the exact system prompt shown above, as the model was trained on it.

max_new_tokens: 512
temperature: 1.0
top_p: 0.95
top_k: 64

You can try the model directly at TinkerSpace HF Space.

Limitations

This is only the first version of PromptTuner. As an initial release, the model may:

Occasionally lose details and relationships from multi-entity prompts
Sometimes introduce stylistic elements and text not present in the original concept

Feedback and suggestions for improvement are welcome.

License

This model is built upon Google's Gemma 3. Please refer to the Gemma license for usage terms.

Citation

If you use this model in your work, please cite:

bibtex

@model{prompt_tuner_v0.1,
  title={PromptTuner-v0.1: A Fine-tuned Gemma3-270M for Prompt Enhancement},
  author={shb777},
  year={2025},
  url={https://huggingface.co/shb777/PromptTuner-v0.1}
}

Acknowledgments

Base model: google/gemma-3-270M-it
Training framework: Unsloth