nuresens/pad_model_adapter_v1

PAD Model — Multi-Adapter Pack Architecture

Instead of blending parameter matrices together linearly or via TIES-merging, this configuration preserves the distinct, non-destructive weights of three task-oriented LoRA adapter tracks inside a unified model asset structure.

Contained Task Adapters

Adapter Name	Source Directory Track
pad1	Text Moderation Evaluation (Checkpoint 672)
pad2	Keyword Extraction Logic (Checkpoint 3382)
pad3	Image Visual Age Rating Engine (VLM CPT)

Dynamic Runtime Usage Pattern

from transformers import AutoModelForImageTextToText, AutoProcessor
from peft import PeftModel

# 1. Load structural framework layers
processor = AutoProcessor.from_pretrained("nuresens/pad_model_adapter_v1")
base_model = AutoModelForImageTextToText.from_pretrained("mistralai/Ministral-3-3B-Base-2512", torch_dtype=torch.float16, device_map="auto")

# 2. Register multi-adapter configuration paths
model = PeftModel.from_pretrained(base_model, "nuresens/pad_model_adapter_v1", adapter_name="pad1")
model.load_adapter("nuresens/pad_model_adapter_v1", adapter_name="pad2", subfolder="pad2")
model.load_adapter("nuresens/pad_model_adapter_v1", adapter_name="pad3", subfolder="pad3")

# 3. Target task context routing dynamically before generating text
model.set_adapter("pad3") # Routes current context to the vision processing matrices explicitly