model_card_template.md

---
license: mit
tags:
- amop-optimized
- onnx
---

# AMOP-Optimized ONNX Model: {repo_name}

This model was automatically optimized for CPU inference using the **Adaptive Model Optimization Pipeline (AMOP)**.

- **Base Model:** [{model_id}](https://huggingface.co/{model_id})
- **Optimization Date:** {optimization_date}

## Optimization Details

The following AMOP ONNX pipeline stages were applied:
- **Pruning:** {pruning_status} (Percentage: {pruning_percent}%)
- **Quantization & ONNX Conversion:** Enabled ({quant_type} Quantization)

## How to Use

This model is in ONNX format and can be run with `optimum-onnxruntime`. Make sure you have `optimum`, `onnxruntime`, and `transformers` installed.

```python
from optimum.onnxruntime import ORTModelForCausalLM
from transformers import AutoTokenizer

model_id = "{repo_id}"
model = ORTModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = "The future of AI is"
inputs = tokenizer(prompt, return_tensors="pt")
gen_tokens = model.generate(**inputs)
print(tokenizer.batch_decode(gen_tokens))
```
## AMOP Pipeline Log
<details>
<summary>Click to expand</summary>

```
{pipeline_log}
```
</details>