9831aec49f
Signed-off-by: Xiaowei Jiang <xwjiang2010@gmail.com> Co-authored-by: Xiaowei Jiang <xwjiang2010@gmail.com> Co-authored-by: ywang96 <ywang@roblox.com> Co-authored-by: xwjiang2010 <87673679+xwjiang2010@users.noreply.github.com> Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com>
43 lines
1.1 KiB
Python
43 lines
1.1 KiB
Python
from io import BytesIO
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import requests
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from PIL import Image
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from vllm import LLM, SamplingParams
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def run_llava_next():
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llm = LLM(
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model="llava-hf/llava-v1.6-mistral-7b-hf",
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image_token_id=32000,
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image_input_shape="1,3,336,336",
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# Use the maximum possible value for memory profiling
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image_feature_size=2928,
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)
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prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
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url = "https://h2o-release.s3.amazonaws.com/h2ogpt/bigben.jpg"
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image = Image.open(BytesIO(requests.get(url).content))
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sampling_params = SamplingParams(temperature=0.8,
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top_p=0.95,
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max_tokens=100)
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outputs = llm.generate(
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{
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"prompt": prompt,
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"multi_modal_data": {
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"image": image
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}
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},
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sampling_params=sampling_params)
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generated_text = ""
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for o in outputs:
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generated_text += o.outputs[0].text
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print(f"LLM output:{generated_text}")
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if __name__ == "__main__":
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run_llava_next()
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