qwen_lora_test/model_download.py

69 lines
1.9 KiB
Python

import torch
from datasets import Dataset
from modelscope import snapshot_download, AutoTokenizer
from qwen_vl_utils import process_vision_info
from transformers import (
TrainingArguments,
Trainer,
DataCollatorForSeq2Seq,
Qwen2VLForConditionalGeneration,
AutoProcessor,
)
import json
# 在modelscope上下载Qwen2-VL模型到本地目录下
# model_dir = snapshot_download(
# model_id="Qwen/Qwen2-VL-2B-Instruct",
# cache_dir=model_path,
# revision="master"
# )
# print(f"模型已下载到: {model_dir}")
model_dir = "/root/PMN_WS/qwen-test/model/Qwen/Qwen2-VL-2B-Instruct"
# 使用Transformers加载模型权重
tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_dir)
model = Qwen2VLForConditionalGeneration.from_pretrained(
model_dir, torch_dtype="auto", device_map="auto"
)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
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)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)