Instructions to use USS-Inferprise/Phi4-Mini-Prose2Tags-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use USS-Inferprise/Phi4-Mini-Prose2Tags-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="USS-Inferprise/Phi4-Mini-Prose2Tags-4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("USS-Inferprise/Phi4-Mini-Prose2Tags-4B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("USS-Inferprise/Phi4-Mini-Prose2Tags-4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use USS-Inferprise/Phi4-Mini-Prose2Tags-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "USS-Inferprise/Phi4-Mini-Prose2Tags-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "USS-Inferprise/Phi4-Mini-Prose2Tags-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/USS-Inferprise/Phi4-Mini-Prose2Tags-4B

SGLang

How to use USS-Inferprise/Phi4-Mini-Prose2Tags-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "USS-Inferprise/Phi4-Mini-Prose2Tags-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "USS-Inferprise/Phi4-Mini-Prose2Tags-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "USS-Inferprise/Phi4-Mini-Prose2Tags-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "USS-Inferprise/Phi4-Mini-Prose2Tags-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use USS-Inferprise/Phi4-Mini-Prose2Tags-4B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for USS-Inferprise/Phi4-Mini-Prose2Tags-4B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for USS-Inferprise/Phi4-Mini-Prose2Tags-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for USS-Inferprise/Phi4-Mini-Prose2Tags-4B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="USS-Inferprise/Phi4-Mini-Prose2Tags-4B",
    max_seq_length=2048,
)

Docker Model Runner
How to use USS-Inferprise/Phi4-Mini-Prose2Tags-4B with Docker Model Runner:
```
docker model run hf.co/USS-Inferprise/Phi4-Mini-Prose2Tags-4B
```

Phi4-Mini-Prose2Tags-4B

File size: 3,133 Bytes

---
license: mit
datasets:
- USS-Inferprise/Phi4-Mini-Prose2Tags-4B-Raw-Training-Data
language:
- en
base_model:
- huihui-ai/Phi-4-mini-instruct-abliterated
tags:
- text-generation-inference
- unsloth
- mergekit
- danbooru
- image-captioning
- tagging
- phi-4
- finetune
- anime
pipeline_tag: text-generation
library_name: transformers
---
# 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|>