52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
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from vllm import LLM, SamplingParams
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prefix = (
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"You are an expert school principal, skilled in effectively managing "
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"faculty and staff. Draft 10-15 questions for a potential first grade "
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"Head Teacher for my K-12, all-girls', independent school that emphasizes "
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"community, joyful discovery, and life-long learning. The candidate is "
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"coming in for a first-round panel interview for a 8th grade Math "
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"teaching role. They have 5 years of previous teaching experience "
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"as an assistant teacher at a co-ed, public school with experience "
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"in middle school math teaching. Based on these information, fulfill "
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"the following paragraph: ")
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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=0.0)
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# Create an LLM.
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llm = LLM(model="facebook/opt-125m")
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generating_prompts = [prefix + prompt for prompt in prompts]
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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(generating_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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print("-" * 80)
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# -1 since the last token can change when concatenating prompts.
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prefix_pos = len(llm.llm_engine.tokenizer.encode(prefix)) - 1
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# Generate with prefix
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outputs = llm.generate(generating_prompts, sampling_params,
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prefix_pos=[prefix_pos] * len(generating_prompts))
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# Print the outputs. You should see the same outputs as before
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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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