Create model_card_template_gguf.md

model_card_template_gguf.md

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+---
+license: mit
+tags:
+- amop-optimized
+- gguf
+---
+
+# AMOP-Optimized GGUF 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 GGUF pipeline stages were applied:
+- **GGUF Conversion & Quantization:** Enabled (Strategy: {quant_type})
+
+## How to Use
+
+This model is in GGUF format and can be run with libraries like `llama-cpp-python`.
+
+First, install the necessary libraries:
+```bash
+pip install llama-cpp-python
+```
+
+Then, use the following Python code to run inference:
+```python
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+
+# Download the GGUF model from the Hub
+model_path = hf_hub_download(
+    repo_id="{repo_id}",
+    filename="model.gguf" # Or the specific GGUF file name
+)
+
+# Instantiate the model
+llm = Llama(
+  model_path=model_path,
+  n_ctx=2048,  # Context window
+)
+
+# Run inference
+prompt = "The future of AI is"
+output = llm(
+  f"Q: {prompt} A: ", # Or your preferred prompt format
+  max_tokens=50,
+  stop=["Q:", "\n"],
+  echo=True
+)
+
+print(output)
+```
+## AMOP Pipeline Log
+<details>
+<summary>Click to expand</summary>
+
+```
+{pipeline_log}
+```
+</details>