Qwen2.5-VL is a multimodal vision-language model, available in 3B, 7B, and 72B parameters, pretrained on 4.1T tokens. The model introduces window attention in the ViT encoder to accelerate training and inference, dynamic FPS sampling on the spatial and temporal dimensions for better video understanding across different sampling rates, and an upgraded MRoPE (multi-resolutional rotary positional encoding) mechanism to better capture and learn temporal dynamics.
You can find all the original Qwen2.5-VL checkpoints under the Qwen2.5-VL collection.
Click on the Qwen2.5-VL models in the right sidebar for more examples of how to apply Qwen2.5-VL to different vision and language tasks.
The example below demonstrates how to generate text based on an image with Pipeline or the AutoModel class.
import torch
from transformers import pipeline
pipe = pipeline(
task="image-text-to-text",
model="Qwen/Qwen2.5-VL-7B-Instruct",
device=0,
torch_dtype=torch.bfloat16
)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
},
{ "type": "text", "text": "Describe this image."},
]
}
]
pipe(text=messages,max_new_tokens=20, return_full_text=False)
Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.
The example below uses torchao to only quantize the weights to int4.
import torch
from transformers import TorchAoConfig, Gemma3ForConditionalGeneration, AutoProcessor
quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct",
torch_dtype=torch.bfloat16,
device_map="auto",
quantization_config=quantization_config
)
Notes
Use Qwen2.5-VL for video inputs by setting "type": "video" as shown below.
conversation = [
{
"role": "user",
"content": [
{"type": "video", "path": "/path/to/video.mp4"},
{"type": "text", "text": "What happened in the video?"},
],
}
]
inputs = processor.apply_chat_template(
conversation,
video_fps=1,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(output_text)Use Qwen2.5-VL for a mixed batch of inputs (images, videos, text). Add labels when handling multiple images or videos for better reference as show below.
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct",
torch_dtype=torch.float16,
device_map="auto",
attn_implementation="sdpa"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
conversation = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "Hello, how are you?"}
]
},
{
"role": "assistant",
"content": "I'm doing well, thank you for asking. How can I assist you today?"
},
{
"role": "user",
"content": [
{"type": "text", "text": "Can you describe these images and video?"},
{"type": "image"},
{"type": "image"},
{"type": "video"},
{"type": "text", "text": "These are from my vacation."}
]
},
{
"role": "assistant",
"content": "I'd be happy to describe the images and video for you. Could you please provide more context about your vacation?"
},
{
"role": "user",
"content": "It was a trip to the mountains. Can you see the details in the images and video?"
}
]
prompt_without_id = processor.apply_chat_template(conversation, add_generation_prompt=True)
prompt_with_id = processor.apply_chat_template(conversation, add_generation_prompt=True, add_vision_id=True)
Use the min_pixels and max_pixels parameters in AutoProcessor to set the resolution.
min_pixels = 224*224
max_pixels = 2048*2048
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
Higher resolution can require more compute whereas reducing the resolution can save memory as follows:
min_pixels = 256*28*28
max_pixels = 1024*28*28
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)( vocab_size = 152064 hidden_size = 8192 intermediate_size = 29568 num_hidden_layers = 80 num_attention_heads = 64 num_key_value_heads = 8 hidden_act = 'silu' max_position_embeddings = 32768 initializer_range = 0.02 rms_norm_eps = 1e-05 use_cache = True tie_word_embeddings = False rope_theta = 1000000.0 use_sliding_window = False sliding_window = 4096 max_window_layers = 80 attention_dropout = 0.0 vision_config = None rope_scaling = None **kwargs )
Parameters
int, optional, defaults to 152064) — Vocabulary size of the Qwen2_5_VL model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling Qwen2_5_VLModel int, optional, defaults to 8192) — Dimension of the hidden representations. int, optional, defaults to 29568) — Dimension of the MLP representations. int, optional, defaults to 80) — Number of hidden layers in the Transformer encoder. int, optional, defaults to 64) — Number of attention heads for each attention layer in the Transformer encoder. int, optional, defaults to 8) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout this paper. If it is not specified, will default to 32. str or function, optional, defaults to "silu") — The non-linear activation function (function or string) in the decoder. int, optional, defaults to 32768) — The maximum sequence length that this model might ever be used with. float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. float, optional, defaults to 1e-05) — The epsilon used by the rms normalization layers. bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True. bool, optional, defaults to False) — Whether the model’s input and output word embeddings should be tied. float, optional, defaults to 1000000.0) — The base period of the RoPE embeddings. bool, optional, defaults to False) — Whether to use sliding window attention. int, optional, defaults to 4096) — Sliding window attention (SWA) window size. If not specified, will default to 4096. int, optional, defaults to 80) — The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities. Dict, optional) — The config for the visual encoder initialization. Dict, optional) — Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer max_position_embeddings, we recommend you to update this value accordingly. Expected contents: rope_type (str): The sub-variant of RoPE to use. Can be one of [‘default’, ‘linear’, ‘dynamic’, ‘yarn’, ‘longrope’, ‘llama3’], with ‘default’ being the original RoPE implementation. factor (float, optional): Used with all rope types except ‘default’. The scaling factor to apply to the RoPE embeddings. In most scaling types, a factor of x will enable the model to handle sequences of length x original maximum pre-trained length. original_max_position_embeddings (int, optional): Used with ‘dynamic’, ‘longrope’ and ‘llama3’. The original max position embeddings used during pretraining. attention_factor (float, optional): Used with ‘yarn’ and ‘longrope’. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the factor field to infer the suggested value. beta_fast (float, optional): Only used with ‘yarn’. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. beta_slow (float, optional): Only used with ‘yarn’. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. short_factor (List[float], optional): Only used with ‘longrope’. The scaling factor to be applied to short contexts (< original_max_position_embeddings). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 long_factor (List[float], optional): Only used with ‘longrope’. The scaling factor to be applied to long contexts (< original_max_position_embeddings). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 low_freq_factor (float, optional): Only used with ‘llama3’. Scaling factor applied to low frequency components of the RoPE high_freq_factor (float, optional*): Only used with ‘llama3’. Scaling factor applied to high frequency components of the RoPE This is the configuration class to store the configuration of a Qwen2_5_VLModel. It is used to instantiate a Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen2-VL-7B-Instruct Qwen/Qwen2-VL-7B-Instruct.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
>>> from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLConfig >>> >>> configuration = Qwen2_5_VLConfig() >>> >>> model = Qwen2_5_VLForConditionalGeneration(configuration) >>> >>> configuration = model.configQwen2_5_VLProcessor class transformers.Qwen2_5_VLProcessor < source >
( image_processor = None tokenizer = None chat_template = None **kwargs )
Parameters
str, optional) — A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor. Qwen2_5_VLProcessor offers all the functionalities of Qwen2VLImageProcessor and Qwen2TokenizerFast. See the __call__() and decode() for more information.
This method forwards all its arguments to Qwen2TokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to Qwen2TokenizerFast’s decode(). Please refer to the docstring of this method for more information.
post_process_image_text_to_text < source >( generated_outputs skip_special_tokens = True clean_up_tokenization_spaces = False **kwargs ) → List[str]
Parameters
torch.Tensor or np.ndarray) — The output of the model generate function. The output is expected to be a tensor of shape (batch_size, sequence_length) or (sequence_length,). bool, optional, defaults to True) — Whether or not to remove special tokens in the output. Argument passed to the tokenizer’s batch_decode method. bool, optional, defaults to False) — Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer’s batch_decode method. batch_decode method. The decoded text.
Post-process the output of the model to decode the text.
Qwen2_5_VLModel class transformers.Qwen2_5_VLModel < source >( config: Qwen2_5_VLConfig )
Parameters
The bare Qwen2_5_VL Model outputting raw hidden-states without any specific head on top. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward < source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None )
Qwen2_5_VLForConditionalGeneration class transformers.Qwen2_5_VLForConditionalGeneration < source >( config )
forward < source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None pixel_values: typing.Optional[torch.Tensor] = None pixel_values_videos: typing.Optional[torch.FloatTensor] = None image_grid_thw: typing.Optional[torch.LongTensor] = None video_grid_thw: typing.Optional[torch.LongTensor] = None rope_deltas: typing.Optional[torch.LongTensor] = None cache_position: typing.Optional[torch.LongTensor] = None second_per_grid_ts: typing.Optional[torch.Tensor] = None ) → transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VLCausalLMOutputWithPast or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).
If you want to change padding behavior, you should read modeling_opt._prepare_decoder_attention_mask and modify to your needs. See diagram 1 in the paper for more information on the default strategy.
torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1]. What are position IDs? tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).
torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix. bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. torch.FloatTensor of shape (seq_length, num_channels * image_size * image_size)) -- The tensors corresponding to the input images. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/v4.51.3/en/model_doc/auto#transformers.AutoImageProcessor). See Qwen2_5_VLImageProcessor.callfor details. [Qwen2_5_VLProcessor](/docs/transformers/v4.51.3/en/model_doc/qwen2_5_vl#transformers.Qwen2_5_VLProcessor) usesQwen2_5_VLImageProcessor` for processing images. torch.FloatTensor of shape (seq_length, num_channels * temporal_size * image_size * image_size)) -- The tensors corresponding to the input videos. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/v4.51.3/en/model_doc/auto#transformers.AutoImageProcessor). See Qwen2_5_VLImageProcessor.callfor details. [Qwen2_5_VLProcessor](/docs/transformers/v4.51.3/en/model_doc/qwen2_5_vl#transformers.Qwen2_5_VLProcessor) usesQwen2_5_VLImageProcessor` for processing videos. torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM. torch.LongTensor of shape (num_videos, 3), optional) — The temporal, height and width of feature shape of each video in LLM. torch.LongTensor of shape (batch_size, ), optional) — The rope index difference between sequence length and multimodal rope. torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]. Returns
transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VLCausalLMOutputWithPast or tuple(torch.FloatTensor)
A transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VLCausalLMOutputWithPast or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (Qwen2_5_VLConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head))
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
rope_deltas (torch.LongTensor of shape (batch_size, ), optional) — The rope index difference between sequence length and multimodal rope.
The Qwen2_5_VLForConditionalGeneration forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
>>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
>>> messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What is shown in this image?"},
],
},
]
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
>>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
>>>
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."< > Update on GitHub
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