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from transformers import AutoProcessor
from transformers import AutoModelForCausalLM
from qwen_vl_utils import process_vision_info
model_path="../"
print(f"LOAD MODEL FROM: {model_path}")
key_mapping = {
"^visual": "model.visual",
r"^model(?!\.(language_model|visual))": "model.language_model",
}
model = AutoModelForCausalLM.from_pretrained(
model_path,
trust_remote_code=True,
torch_dtype='auto',
key_mapping=key_mapping).eval().cuda()
conversation = [
{
"role": "system",
"content": [
{"type": "text", "text": "你是华为公司开发的多模态大模型，名字是openPangu-VL-7B。你能够处理文本和视觉模态的输入，并给出文本输出。"},
]
},
{
"role": "user",
"content": [
{"type": "text", "text": "你好，你是谁？"},
]
}
]
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
text = processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(conversation)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=False,
return_tensors="pt",
)
inputs = inputs.to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
res = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(f"OUTPUT: {res}")
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