74 lines
2.9 KiB
ReStructuredText
74 lines
2.9 KiB
ReStructuredText
.. _gguf:
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GGUF
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==================
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.. warning::
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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.
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.. warning::
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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.
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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:
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.. code-block:: console
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$ wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf
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$ # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
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$ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0
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You can also add ``--tensor-parallel-size 2`` to enable tensor parallelism inference with 2 GPUs:
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.. code-block:: console
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$ # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
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$ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 --tensor-parallel-size 2
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.. warning::
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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.
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You can also use the GGUF model directly through the LLM entrypoint:
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.. code-block:: python
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from vllm import LLM, SamplingParams
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# In this script, we demonstrate how to pass input to the chat method:
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conversation = [
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{
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"role": "system",
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"content": "You are a helpful assistant"
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},
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{
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"role": "user",
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"content": "Hello"
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},
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{
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"role": "assistant",
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"content": "Hello! How can I assist you today?"
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},
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{
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"role": "user",
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"content": "Write an essay about the importance of higher education.",
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},
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]
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# Create a sampling params object.
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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# Create an LLM.
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llm = LLM(model="./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf",
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tokenizer="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
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# Generate texts from the prompts. The output is a list of RequestOutput objects
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# that contain the prompt, generated text, and other information.
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outputs = llm.chat(conversation, sampling_params)
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# Print the outputs.
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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