vllm/examples/offline_inference/structured_outputs.py
Russell Bryant e489ad7a21
[Misc] Add SPDX-License-Identifier headers to python source files (#12628)
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**

commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:18:24 2025 -0500

    Add SPDX license headers to python source files
    
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
    also be easily used by tools to help manage license compliance.
    
The Linux Foundation runs license scans against the codebase to help
ensure
    we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
    
    More information can be found on the SPDX site:
    
    - https://spdx.dev/learn/handling-license-info/
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:36:32 2025 -0500

    Check for SPDX headers using pre-commit
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

---------

Signed-off-by: Russell Bryant <rbryant@redhat.com>
2025-02-02 11:58:18 -08:00

81 lines
2.4 KiB
Python

# SPDX-License-Identifier: Apache-2.0
from enum import Enum
from pydantic import BaseModel
from vllm import LLM, SamplingParams
from vllm.sampling_params import GuidedDecodingParams
llm = LLM(model="Qwen/Qwen2.5-3B-Instruct", max_model_len=100)
# Guided decoding by Choice (list of possible options)
guided_decoding_params = GuidedDecodingParams(choice=["Positive", "Negative"])
sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
outputs = llm.generate(
prompts="Classify this sentiment: vLLM is wonderful!",
sampling_params=sampling_params,
)
print(outputs[0].outputs[0].text)
# Guided decoding by Regex
guided_decoding_params = GuidedDecodingParams(regex="\w+@\w+\.com\n")
sampling_params = SamplingParams(guided_decoding=guided_decoding_params,
stop=["\n"])
prompt = ("Generate an email address for Alan Turing, who works in Enigma."
"End in .com and new line. Example result:"
"alan.turing@enigma.com\n")
outputs = llm.generate(prompts=prompt, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
# Guided decoding by JSON using Pydantic schema
class CarType(str, Enum):
sedan = "sedan"
suv = "SUV"
truck = "Truck"
coupe = "Coupe"
class CarDescription(BaseModel):
brand: str
model: str
car_type: CarType
json_schema = CarDescription.model_json_schema()
guided_decoding_params = GuidedDecodingParams(json=json_schema)
sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
prompt = ("Generate a JSON with the brand, model and car_type of"
"the most iconic car from the 90's")
outputs = llm.generate(
prompts=prompt,
sampling_params=sampling_params,
)
print(outputs[0].outputs[0].text)
# Guided decoding by Grammar
simplified_sql_grammar = """
?start: select_statement
?select_statement: "SELECT " column_list " FROM " table_name
?column_list: column_name ("," column_name)*
?table_name: identifier
?column_name: identifier
?identifier: /[a-zA-Z_][a-zA-Z0-9_]*/
"""
guided_decoding_params = GuidedDecodingParams(grammar=simplified_sql_grammar)
sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
prompt = ("Generate an SQL query to show the 'username' and 'email'"
"from the 'users' table.")
outputs = llm.generate(
prompts=prompt,
sampling_params=sampling_params,
)
print(outputs[0].outputs[0].text)