34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
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from vllm import LLM, SamplingParams
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# Sample prompts.
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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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(
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model="openlm-research/open_llama_3b",
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max_num_seqs=8,
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# The max_model_len and block_size arguments are required to be same as max sequence length,
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# when targeting neuron device. Currently, this is a known limitation in continuous batching
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# support in transformers-neuronx.
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# TODO(liangfu): Support paged-attention in transformers-neuronx.
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max_model_len=128,
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block_size=128,
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# The device can be automatically detected when AWS Neuron SDK is installed.
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# The device argument can be either unspecified for automated detection, or explicitly assigned.
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device="neuron")
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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.generate(prompts, 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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