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@ -104,7 +104,7 @@ We have three models with 3, 7 and 72 billion parameters. This repo contains the
## Requirements
The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
```
pip install git+https://github.com/huggingface/transformer accelerate
pip install git+https://github.com/huggingface/transformers accelerate
```
or you might encounter the following error:
```
@ -118,7 +118,7 @@ Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelS
The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
```
pip install git+https://github.com/huggingface/transformer accelerate
pip install git+https://github.com/huggingface/transformers accelerate
```
or you might encounter the following error:
```
@ -142,10 +142,13 @@ Here we show a code snippet to show you how to use the chat model with `transfor
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
from modelscope import snapshot_download
model_dir=snapshot_download("Qwen/Qwen2.5-VL-7B-Instruct")
# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
model_dir, torch_dtype="auto", device_map="auto"
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
@ -157,7 +160,7 @@ model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
# )
# default processer
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
processor = AutoProcessor.from_pretrained(model_dir)
# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.