110 lines
4.1 KiB
ReStructuredText
110 lines
4.1 KiB
ReStructuredText
.. _lora:
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Using LoRA adapters
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===================
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This document shows you how to use `LoRA adapters <https://arxiv.org/abs/2106.09685>`_ with vLLM on top of a base model.
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LoRA adapters can be used with any vLLM model that implements :class:`~vllm.model_executor.models.interfaces.SupportsLoRA`.
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Adapters can be efficiently served on a per request basis with minimal overhead. First we download the adapter(s) and save
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them locally with
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.. code-block:: python
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from huggingface_hub import snapshot_download
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sql_lora_path = snapshot_download(repo_id="yard1/llama-2-7b-sql-lora-test")
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Then we instantiate the base model and pass in the ``enable_lora=True`` flag:
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.. code-block:: python
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from vllm import LLM, SamplingParams
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from vllm.lora.request import LoRARequest
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llm = LLM(model="meta-llama/Llama-2-7b-hf", enable_lora=True)
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We can now submit the prompts and call ``llm.generate`` with the ``lora_request`` parameter. The first parameter
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of ``LoRARequest`` is a human identifiable name, the second parameter is a globally unique ID for the adapter and
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the third parameter is the path to the LoRA adapter.
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.. code-block:: python
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sampling_params = SamplingParams(
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temperature=0,
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max_tokens=256,
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stop=["[/assistant]"]
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)
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prompts = [
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"[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_74 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]",
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"[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_11 (nationality VARCHAR, elector VARCHAR)\n\n question: When Anchero Pantaleone was the elector what is under nationality? [/user] [assistant]",
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]
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outputs = llm.generate(
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prompts,
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sampling_params,
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lora_request=LoRARequest("sql_adapter", 1, sql_lora_path)
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)
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Check out `examples/multilora_inference.py <https://github.com/vllm-project/vllm/blob/main/examples/multilora_inference.py>`_
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for an example of how to use LoRA adapters with the async engine and how to use more advanced configuration options.
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Serving LoRA Adapters
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---------------------
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LoRA adapted models can also be served with the Open-AI compatible vLLM server. To do so, we use
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``--lora-modules {name}={path} {name}={path}`` to specify each LoRA module when we kickoff the server:
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.. code-block:: bash
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vllm serve meta-llama/Llama-2-7b-hf \
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--enable-lora \
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--lora-modules sql-lora=$HOME/.cache/huggingface/hub/models--yard1--llama-2-7b-sql-lora-test/snapshots/0dfa347e8877a4d4ed19ee56c140fa518470028c/
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.. note::
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The commit ID `0dfa347e8877a4d4ed19ee56c140fa518470028c` may change over time. Please check the latest commit ID in your environment to ensure you are using the correct one.
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The server entrypoint accepts all other LoRA configuration parameters (``max_loras``, ``max_lora_rank``, ``max_cpu_loras``,
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etc.), which will apply to all forthcoming requests. Upon querying the ``/models`` endpoint, we should see our LoRA along
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with its base model:
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.. code-block:: bash
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curl localhost:8000/v1/models | jq .
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{
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"object": "list",
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"data": [
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{
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"id": "meta-llama/Llama-2-7b-hf",
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"object": "model",
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...
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},
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{
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"id": "sql-lora",
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"object": "model",
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...
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}
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]
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}
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Requests can specify the LoRA adapter as if it were any other model via the ``model`` request parameter. The requests will be
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processed according to the server-wide LoRA configuration (i.e. in parallel with base model requests, and potentially other
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LoRA adapter requests if they were provided and ``max_loras`` is set high enough).
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The following is an example request
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.. code-block:: bash
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curl http://localhost:8000/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "sql-lora",
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"prompt": "San Francisco is a",
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"max_tokens": 7,
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"temperature": 0
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}' | jq
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