model_card_template_gguf.md

metadata

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}
• 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:

pip install llama-cpp-python

Then, use the following Python code to run inference:

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

Click to expand

{pipeline_log}