45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
#!/share/vllm-0.8.5/bin/python
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# pip install accelerate
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import threading
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from time import time
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from appPublic.worker import awaitify
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from ahserver.serverenv import get_serverenv
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from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer
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from PIL import Image
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import requests
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import torch
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from llmengine.base_chat_llm import MMChatLLM, llm_register
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class Gemma3LLM(MMChatLLM):
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def __init__(self, model_id):
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self.model = Gemma3ForConditionalGeneration.from_pretrained(
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model_id, device_map="auto"
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).eval()
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self.processor = AutoProcessor.from_pretrained(model_id)
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self.tokenizer = self.processor.tokenizer
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self.messages = []
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self.model_id = model_id
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llm_register("gemma-3", Gemma3LLM)
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if __name__ == '__main__':
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gemma3 = Gemma3LLM('/share/models/google/gemma-3-4b-it')
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session = {}
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while True:
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print('input prompt')
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p = input()
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if p:
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if p == 'q':
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break;
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print('input image path')
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imgpath=input()
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for d in gemma3.stream_generate(session, p, image_path=imgpath):
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if not d['done']:
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print(d['text'], end='', flush=True)
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else:
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x = {k:v for k,v in d.items() if k != 'text'}
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print(f'\n{x}\n')
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