vllm/docs/features/quantization/gguf.md

title
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 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 with the following command:

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:

# 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.

GGUF assumes that huggingface can convert the metadata to a config file. In case huggingface doesn't support your model you can manually create a config and pass it as hf-config-path

# If you model is not supported by huggingface you can manually provide a huggingface compatible config path
vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
   --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
   --hf-config-path Tinyllama/TInyLlama-1.1B-Chat-v1.0

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

??? Code

  ```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}")
  ```