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快速使用

我们提供简单的示例来说明如何利用 🤖 ModelScope 和 🤗 Transformers 快速使用 Qwen-VL 和 Qwen-VL-Chat。

在开始前，请确保你已经配置好环境并安装好相关的代码包。最重要的是，确保你满足上述要求，然后安装相关的依赖库。

pip install -r requirements.txt

接下来你可以开始使用Transformers或者ModelScope来使用我们的模型。关于视觉模块的更多用法，请参考教程。

🤗 Transformers

如希望使用 Qwen-VL-chat 进行推理，所需要写的只是如下所示的数行代码。请确保你使用的是最新代码。

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
import torch
torch.manual_seed(1234)

# 请注意：分词器默认行为已更改为默认关闭特殊token攻击防护。
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True)

# 打开bf16精度，A100、H100、RTX3060、RTX3070等显卡建议启用以节省显存
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval()
# 打开fp16精度，V100、P100、T4等显卡建议启用以节省显存
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval()
# 使用CPU进行推理，需要约32GB内存
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="cpu", trust_remote_code=True).eval()
# 默认gpu进行推理，需要约24GB显存
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="cuda", trust_remote_code=True).eval()

# 可指定不同的生成长度、top_p等相关超参（transformers 4.32.0及以上无需执行此操作）
# model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True)

# 第一轮对话
query = tokenizer.from_list_format([
    {'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'}, # Either a local path or an url
    {'text': '这是什么?'},
])
response, history = model.chat(tokenizer, query=query, history=None)
print(response)
# 图中是一名女子在沙滩上和狗玩耍，旁边是一只拉布拉多犬，它们处于沙滩上。

# 第二轮对话
response, history = model.chat(tokenizer, '框出图中击掌的位置', history=history)
print(response)
# <ref>击掌</ref><box>(536,509),(588,602)</box>
image = tokenizer.draw_bbox_on_latest_picture(response, history)
if image:
  image.save('1.jpg')
else:
  print("no box")

运行Qwen-VL同样非常简单。

运行Qwen-VL 
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
import torch
torch.manual_seed(1234)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)

# 打开bf16精度，A100、H100、RTX3060、RTX3070等显卡建议启用以节省显存
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, bf16=True).eval()
# 打开fp16精度，V100、P100、T4等显卡建议启用以节省显存
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, fp16=True).eval()
# 使用CPU进行推理，需要约32GB内存
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cpu", trust_remote_code=True).eval()
# 默认gpu进行推理，需要约24GB显存
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cuda", trust_remote_code=True).eval()

# 可指定不同的生成长度、top_p等相关超参（transformers 4.32.0及以上无需执行此操作）
# model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)

query = tokenizer.from_list_format([
    {'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'}, # Either a local path or an url
    {'text': 'Generate the caption in English with grounding:'},
])
inputs = tokenizer(query, return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False)
print(response)
# <img>https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg</img>Generate the caption in English with grounding:<ref> Woman</ref><box>(451,379),(731,806)</box> and<ref> her dog</ref><box>(219,424),(576,896)</box> playing on the beach<|endoftext|>
image = tokenizer.draw_bbox_on_latest_picture(response)
if image:
  image.save('2.jpg')
else:
  print("no box")

若在使用上述代码时由于各种原因无法从 HuggingFace 拉取模型和代码，可以先从 ModelScope 下载模型及代码至本地，再从本地加载模型：

from modelscope import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer

# Downloading model checkpoint to a local dir model_dir
# model_dir = snapshot_download('qwen/Qwen-VL')
model_dir = snapshot_download('qwen/Qwen-VL-Chat')


# Loading local checkpoints
# trust_remote_code is still set as True since we still load codes from local dir instead of transformers
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_dir,
    device_map="cuda",
    trust_remote_code=True
).eval()

🤖 ModelScope

魔搭（ModelScope）是开源的模型即服务共享平台，为泛AI开发者提供灵活、易用、低成本的一站式模型服务产品。使用ModelScope同样非常简单，代码如下所示：

from modelscope import (
    snapshot_download, AutoModelForCausalLM, AutoTokenizer, GenerationConfig
)
import torch
model_id = 'qwen/Qwen-VL-Chat'
revision = 'v1.0.0'

model_dir = snapshot_download(model_id, revision=revision)
torch.manual_seed(1234)

tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
if not hasattr(tokenizer, 'model_dir'):
    tokenizer.model_dir = model_dir
# 打开bf16精度，A100、H100、RTX3060、RTX3070等显卡建议启用以节省显存
# model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, bf16=True).eval()
# 打开fp16精度，V100、P100、T4等显卡建议启用以节省显存
model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, fp16=True).eval()
# 使用CPU进行推理，需要约32GB内存
# model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="cpu", trust_remote_code=True).eval()
# 默认gpu进行推理，需要约24GB显存
model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True).eval()

# 指定生成超参数（transformers 4.32.0及以上无需执行此操作）
# model.generation_config = GenerationConfig.from_pretrained(model_dir, trust_remote_code=True)

# 第一轮对话
# Either a local path or an url between <img></img> tags.
image_path = 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'
response, history = model.chat(tokenizer, query=f'<img>{image_path}</img>这是什么', history=None)
print(response)
# 图中是一名年轻女子在沙滩上和她的狗玩耍，狗的品种是拉布拉多。她们坐在沙滩上，狗的前腿抬起来，与人互动。

# 第二轮对话
response, history = model.chat(tokenizer, '输出击掌的检测框', history=history)
print(response)
# <ref>"击掌"</ref><box>(211,412),(577,891)</box>
image = tokenizer.draw_bbox_on_latest_picture(response, history)
if image:
  image.save('output_chat.jpg')
else:
  print("no box")