.. _auto_awq:

AutoAWQ
==================

To create a new 4-bit quantized model, you can leverage `AutoAWQ <https://github.com/casper-hansen/AutoAWQ>`_. 
Quantizing reduces the model's precision from FP16 to INT4 which effectively reduces the file size by ~70%.
The main benefits are lower latency and memory usage.

You can quantize your own models by installing AutoAWQ or picking one of the `400+ models on Huggingface <https://huggingface.co/models?sort=trending&search=awq>`_. 

.. code-block:: console

    $ pip install autoawq

After installing AutoAWQ, you are ready to quantize a model. Here is an example of how to quantize Vicuna 7B v1.5:

.. code-block:: python

    from awq import AutoAWQForCausalLM
    from transformers import AutoTokenizer

    model_path = 'lmsys/vicuna-7b-v1.5'
    quant_path = 'vicuna-7b-v1.5-awq'
    quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }

    # Load model
    model = AutoAWQForCausalLM.from_pretrained(model_path, **{"low_cpu_mem_usage": True})
    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

    # Quantize
    model.quantize(tokenizer, quant_config=quant_config)

    # Save quantized model
    model.save_quantized(quant_path)
    tokenizer.save_pretrained(quant_path)

To run an AWQ model with vLLM, you can use `TheBloke/Llama-2-7b-Chat-AWQ <https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ>`_ with the following command:

.. code-block:: console

    $ python examples/llm_engine_example.py --model TheBloke/Llama-2-7b-Chat-AWQ --quantization awq

AWQ models are also supported directly through the LLM entrypoint:

.. code-block:: python

    from vllm import LLM, SamplingParams

    # Sample prompts.
    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    # Create a sampling params object.
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    # Create an LLM.
    llm = LLM(model="TheBloke/Llama-2-7b-Chat-AWQ", quantization="AWQ")
    # Generate texts from the prompts. The output is a list of RequestOutput objects
    # that contain the prompt, generated text, and other information.
    outputs = llm.generate(prompts, sampling_params)
    # Print the outputs.
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")