.. _gguf:

GGUF
==================

.. warning::

   Please note that GGUF support in vLLM is highly experimental and under-optimized at the moment, it might be incompatible with other features. Currently, you can use GGUF as a way to reduce memory footprint. If you encounter any issues, please report them to the vLLM team.

.. warning::

   Currently, vllm only supports loading single-file GGUF models. If you have a multi-files GGUF model, you can use `gguf-split <https://github.com/ggerganov/llama.cpp/pull/6135>`_ tool to merge them to a single-file model.

To run a GGUF model with vLLM, you can download and use the local GGUF model from `TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF <https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF>`_ with the following command:

.. code-block:: console

   $ wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf
   $ # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
   $ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0

You can also add ``--tensor-parallel-size 2`` to enable tensor parallelism inference with 2 GPUs:

.. code-block:: console

   $ # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
   $ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 --tensor-parallel-size 2

.. warning::

   We recommend using the tokenizer from base model instead of GGUF model. Because the tokenizer conversion from GGUF is time-consuming and unstable, especially for some models with large vocab size.

You can also use the GGUF model directly through the LLM entrypoint:

.. code-block:: python

   from vllm import LLM, SamplingParams

   # In this script, we demonstrate how to pass input to the chat method:
   conversation = [
      {
         "role": "system",
         "content": "You are a helpful assistant"
      },
      {
         "role": "user",
         "content": "Hello"
      },
      {
         "role": "assistant",
         "content": "Hello! How can I assist you today?"
      },
      {
         "role": "user",
         "content": "Write an essay about the importance of higher education.",
      },
   ]

   # Create a sampling params object.
   sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

   # Create an LLM.
   llm = LLM(model="./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf",
            tokenizer="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
   # Generate texts from the prompts. The output is a list of RequestOutput objects
   # that contain the prompt, generated text, and other information.
   outputs = llm.chat(conversation, 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}")