---
language: id
tags:
  - mistral
  - multi-adapter
  - lora
  - vision-language
base_model: mistralai/Ministral-3-3B-Base-2512
---

# 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

```python
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
```