model_card_template_gguf.md

---
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>