Phi4-Mini-Prose2Tags-4B-GGUF/README.md

library_name, tags, base_model, license, pipeline_tag

library_name	tags	base_model	license	pipeline_tag
gguf	phi-4 danbooru art-tagger quantized text-generation	USS-Inferprise/Phi4-Mini-Prose2Tags-4B	mit	text-generation

Quants of (https://huggingface.co/USS-Inferprise/Phi4-Mini-Prose2Tags-4B)

quantized_by: USS-Inferprise

We also include a concept for a ComfyUI custom node for applying this model in a workflow.

Original Model Card Follows:

Phi4-Mini-Prose2Tags-4B

This model is a specialized fine-tune designed to translate natural language prose descriptions into structured Danbooru-style tags. It is intended to bridge the gap between human-readable image captions and the tag-based prompting systems used by many latent diffusion models.

Model Details

• Developed by: USS-Inferprise
• Model Name: Phi4-Mini-Prose2Tags-4B
• Base Model: huihui-ai/Phi-4-mini-instruct-abliterated
• Training Architecture: LoRA (Low-Rank Adaptation)
• Merging Method: Linear Merge (via Mergekit)
• Primary Task: Prose-to-Tag Translation

Training Methodology

Dataset Construction

The training data (USS-Inferprise/Phi4-Mini-Prose2Tags-4B-Raw-Training-Data) was generated using a synthetic pipeline:

1. Source Images: 100,000 images sourced from laion/conceptual-captions-12m-webdataset.
2. Prose Generation: Images were described using QwenVL.
3. Tag Generation: Images were tagged using WD 1.3.
4. Pairing: The resulting QwenVL descriptions and WD 1.3 tags were paired to create the final training instruction set.

⚠️ Safety & Content Note

Important

This model was trained exclusively on a curated subset of data intended for general audiences. No explicit, NSFW, or adult-oriented tags were included in the training dataset (Prose2Tags-4B-Raw-Training-Data).

While the base model (Phi-4-mini-instruct-abliterated) has been modified to reduce certain refusals, this specific fine-tune is designed for clean, descriptive tagging. It may not recognize or accurately generate tags related to explicit content. If it can... it didn't learn it from us.

Training Process

• Library: Unsloth
• Hardware: NVIDIA L40S
• Epochs: 1
• Method: LoRA fine-tuning merged into the base model using a Linear merge strategy.

Evaluation & Testing

Testing was performed on 100 images excluded from the training set. To ensure the model generalizes well across different captioning styles, the test inputs used gokaygokay/Florence-2-SD3-Captioner instead of the training-source QwenVL.

Detailed test outputs can be found here: USS-Inferprise/Phi4-Mini-P2T-4B-Testing.

Proper Prompt Format

Warning: You must strictly follow the prompt format below. Failure to do so may result in the model reverting to the standard Phi-4-Mini helpful persona rather than generating tags.

<|user|>
You are a Danbooru tag translator.
{prose_input}<|end|>
<|assistant|>