Qwen/Qwen2.5-VL-7B-Instruct-AWQ
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Cherrytest
2025-02-17 10:22:21 +00:00
parent 0421c94133
commit 5a7b5210b8
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@@ -96,22 +96,25 @@ Here we show a code snippet to show you how to use the chat model with `transfor
```python ```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info from qwen_vl_utils import process_vision_info
from modelscope import snapshot_download
model_dir = snapshot_download("Qwen/Qwen2.5-VL-7B-Instruct-AWQ")
# default: Load the model on the available device(s) # default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained( model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct-AWQ", 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. # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained( # model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
# "Qwen/Qwen2.5-VL-7B-Instruct-AWQ", # model_dir,
# torch_dtype=torch.bfloat16, # torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2", # attn_implementation="flash_attention_2",
# device_map="auto", # device_map="auto",
# ) # )
# default processer # default processer
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct-AWQ") processor = AutoProcessor.from_pretrained(model_dir)
# The default range for the number of visual tokens per image in the model is 4-16384. # 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. # 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.
@@ -207,7 +210,7 @@ The model supports a wide range of resolution inputs. By default, it uses the na
min_pixels = 256 * 28 * 28 min_pixels = 256 * 28 * 28
max_pixels = 1280 * 28 * 28 max_pixels = 1280 * 28 * 28
processor = AutoProcessor.from_pretrained( processor = AutoProcessor.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct-AWQ", min_pixels=min_pixels, max_pixels=max_pixels model_dir, min_pixels=min_pixels, max_pixels=max_pixels
) )
``` ```