Create model_card_template.md

model_card_template.md

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+---
+license: mit
+tags:
+- amop-optimized
+- onnx
+---
+
+# AMOP-Optimized CPU 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 stages were applied:
+- **Stage 2: Pruning:** {pruning_status} (Percentage: {pruning_percent}%)
+- **Stage 3 & 4: Quantization & ONNX Conversion:** Enabled (Dynamic Quantization)
+
+## Performance Metrics
+
+{eval_report}
+
+## 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
+
+{pipeline_log}