2529d09b5a
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk> Co-authored-by: Roger Wang <ywang@roblox.com> Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com>
81 lines
2.0 KiB
Python
81 lines
2.0 KiB
Python
from vllm import LLM, SamplingParams
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llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
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sampling_params = SamplingParams(temperature=0.5)
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def print_outputs(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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print("-" * 80)
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print("=" * 80)
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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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outputs = llm.chat(conversation,
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sampling_params=sampling_params,
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use_tqdm=False)
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print_outputs(outputs)
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# You can run batch inference with llm.chat API
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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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conversations = [conversation for _ in range(10)]
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# We turn on tqdm progress bar to verify it's indeed running batch inference
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outputs = llm.chat(messages=conversations,
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sampling_params=sampling_params,
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use_tqdm=True)
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print_outputs(outputs)
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# A chat template can be optionally supplied.
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# If not, the model will use its default chat template.
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# with open('template_falcon_180b.jinja', "r") as f:
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# chat_template = f.read()
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# outputs = llm.chat(
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# conversations,
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# sampling_params=sampling_params,
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# use_tqdm=False,
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# chat_template=chat_template,
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# )
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