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Model: USS-Inferprise/Phi4-Mini-Prose2Tags-4B-GGUF
Source: Original Platform

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README.md

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+---
+library_name: gguf
+tags:
+- phi-4
+- danbooru
+- art-tagger
+- quantized
+- text-generation
+base_model:
+- USS-Inferprise/Phi4-Mini-Prose2Tags-4B
+license: mit
+pipeline_tag: 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](https://huggingface.co/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](https://huggingface.co/datasets/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](https://github.com/unslothai/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](https://huggingface.co/datasets/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.
+
+```text
+<|user|>
+You are a Danbooru tag translator.
+{prose_input}<|end|>
+<|assistant|>