LLaVa
Overview
LLaVa is an open-source chatbot trained by fine-tuning LlamA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. In other words, it is an multi-modal version of LLMs fine-tuned for chat / instructions.
The LLaVa model was proposed in Visual Instruction Tuning and improved in Improved Baselines with Visual Instruction Tuning by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
The abstract from the paper is the following:
Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA, namely, using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data with simple response formatting prompts, we establish stronger baselines that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint uses merely 1.2M publicly available data, and finishes full training in ∼1 day on a single 8-A100 node. We hope this can make state-of-the-art LMM research more accessible. Code and model will be publicly available
LLaVa architecture. Taken from the original paper.This model was contributed by ArthurZ and ybelkada. The original code can be found here.
Usage tips
We advise users to use
padding_side="left"
when computing batched generation as it leads to more accurate results. Simply make sure to callprocessor.tokenizer.padding_side = "left"
before generating.Note the model has not been explicitly trained to process multiple images in the same prompt, although this is technically possible, you may experience inaccurate results.
[!NOTE] LLaVA models after release v4.46 will raise warnings about adding
processor.patch_size = {{patch_size}}
,processor.num_additional_image_tokens = {{num_additional_image_tokens}}
and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}. It is strongly recommended to add the attributes to the processor if you own the model checkpoint, or open a PR if it is not owned by you. Adding these attributes means that LLaVA will try to infer the number of image tokens required per image and expand the text with as many
<image>placeholders as there will be tokens. Usually it is around 500 tokens per image, so make sure that the text is not truncated as otherwise there will be failure when merging the embeddings. The attributes can be obtained from model config, as
model.config.vision_config.patch_sizeor
model.config.vision_feature_select_strategy. The
num_additional_image_tokensshould be
1if the vision backbone adds a CLS token or
0` if nothing extra is added to the vision patches.
Single image inference
For best results, we recommend users to use the processor’s apply_chat_template()
method to format your prompt correctly. For that you need to construct a conversation history, passing in a plain string will not format your prompt. Each message in the conversation history for chat templates is a dictionary with keys “role” and “content”. The “content” should be a list of dictionaries, for “text” and “image” modalities, as follows:
from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
conversation = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What’s shown in this image?"},
],
},
{
"role": "assistant",
"content": [{"type": "text", "text": "This image shows a red stop sign."},]
},
{
"role": "user",
"content": [
{"type": "text", "text": "Describe the image in more details."},
],
},
]
text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
# Note that the template simply formats your prompt, you still have to tokenize it and obtain pixel values for your images
print(text_prompt)
>>> "USER: <image>\n<What’s shown in this image? ASSISTANT: This image shows a red stop sign.</s>USER: Describe the image in more details. ASSISTANT:"
Batched inference
LLaVa also supports batched inference. Here is how you can do it:
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration
# Load the model in half-precision
model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", torch_dtype=torch.float16, device_map="auto")
processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
# Get two different images
url = "https://www.ilankelman.org/stopsigns/australia.jpg"
image_stop = Image.open(requests.get(url, stream=True).raw)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image_cats = Image.open(requests.get(url, stream=True).raw)
# Prepare a batch of two prompts
conversation_1 = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What is shown in this image?"},
],
},
]
conversation_2 = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What is shown in this image?"},
],
},
]
prompt_1 = processor.apply_chat_template(conversation_1, add_generation_prompt=True)
prompt_2 = processor.apply_chat_template(conversation_2, add_generation_prompt=True)
prompts = [prompt_1, prompt_2]
# We can simply feed images in the order they have to be used in the text prompt
inputs = processor(images=[image_stop, image_cats], text=prompts, padding=True, return_tensors="pt").to(model.device, torch.float16)
# Generate
generate_ids = model.generate(**inputs, max_new_tokens=30)
processor.batch_decode(generate_ids, skip_special_tokens=True)
- If you want to construct a chat prompt yourself, below is a list of prompt formats accepted by each llava checkpoint:
llava-interleave models requires the following format:
"<|im_start|>user <image>\nWhat is shown in this image?<|im_end|><|im_start|>assistant"
For multiple turns conversation:
"<|im_start|>user <image>\n<prompt1><|im_end|><|im_start|>assistant <answer1><|im_end|><|im_start|>user <image>\n<prompt1><|im_end|><|im_start|>assistant "
llava-1.5 models requires the following format:
"USER: <image>\n<prompt> ASSISTANT:"
For multiple turns conversation:
"USER: <image>\n<prompt1> ASSISTANT: <answer1></s>USER: <prompt2> ASSISTANT: <answer2></s>USER: <prompt3> ASSISTANT:"
Note regarding reproducing original implementation
In order to match the logits of the original implementation, one needs to additionally specify do_pad=True
when instantiating LLavaImageProcessor
:
from transformers import LLavaImageProcessor
image_processor = LLavaImageProcessor.from_pretrained("https://huggingface.co./llava-hf/llava-1.5-7b-hf", do_pad=True)
Using Flash Attention 2
Flash Attention 2 is an even faster, optimized version of the previous optimization, please refer to the Flash Attention 2 section of performance docs.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BEiT.
- A Google Colab demo on how to run Llava on a free-tier Google colab instance leveraging 4-bit inference.
- A similar notebook showcasing batched inference. 🌎
LlavaConfig
class transformers.LlavaConfig
< source >( vision_config = None text_config = None ignore_index = -100 image_token_index = 32000 projector_hidden_act = 'gelu' vision_feature_select_strategy = 'default' vision_feature_layer = -2 image_seq_length = 576 multimodal_projector_bias = True **kwargs )
Parameters
- vision_config (
Union[AutoConfig, dict]
, optional, defaults toCLIPVisionConfig
) — The config object or dictionary of the vision backbone. - text_config (
Union[AutoConfig, dict]
, optional, defaults toLlamaConfig
) — The config object or dictionary of the text backbone. - ignore_index (
int
, optional, defaults to -100) — The ignore index for the loss function. - image_token_index (
int
, optional, defaults to 32000) — The image token index to encode the image prompt. - projector_hidden_act (
str
, optional, defaults to"gelu"
) — The activation function used by the multimodal projector. - vision_feature_select_strategy (
str
, optional, defaults to"default"
) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of"default"
or"full"
. - vision_feature_layer (
Union[int, List[int]]
, optional, defaults to -2) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. - image_seq_length (
int
, optional, defaults to 576) — Sequence length of one image embedding. - multimodal_projector_bias (
bool
, optional, defaults toTrue
) — Whether to use bias in the multimodal projector.
This is the configuration class to store the configuration of a LlavaForConditionalGeneration. It is used to instantiate an Llava model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Llava-9B.
e.g. llava-hf/llava-9b
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Example:
>>> from transformers import LlavaForConditionalGeneration, LlavaConfig, CLIPVisionConfig, LlamaConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = CLIPVisionConfig()
>>> # Initializing a Llama config
>>> text_config = LlamaConfig()
>>> # Initializing a Llava llava-1.5-7b style configuration
>>> configuration = LlavaConfig(vision_config, text_config)
>>> # Initializing a model from the llava-1.5-7b style configuration
>>> model = LlavaForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
LlavaImageProcessor
class transformers.LlavaImageProcessor
< source >( do_pad: bool = False do_resize: bool = True size: typing.Dict[str, int] = None resample: Resampling = <Resampling.BICUBIC: 3> do_center_crop: bool = True crop_size: typing.Dict[str, int] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_convert_rgb: bool = True **kwargs )
Parameters
- do_pad (
bool
, optional, defaults toFalse
) — Whether to pad the image to a square based on the longest edge. The padding value is determined by theimage_mean
parameter. Can be overridden bydo_pad
in thepreprocess
method. - do_resize (
bool
, optional, defaults toTrue
) — Whether to resize the image’s (height, width) dimensions to the specifiedsize
. Can be overridden bydo_resize
in thepreprocess
method. - size (
Dict[str, int]
optional, defaults to{"shortest_edge" -- 224}
): Size of the image after resizing. The shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio. Can be overridden bysize
in thepreprocess
method. - resample (
PILImageResampling
, optional, defaults toResampling.BICUBIC
) — Resampling filter to use if resizing the image. Can be overridden byresample
in thepreprocess
method. - do_center_crop (
bool
, optional, defaults toTrue
) — Whether to center crop the image to the specifiedcrop_size
. Can be overridden bydo_center_crop
in thepreprocess
method. - crop_size (
Dict[str, int]
optional, defaults to 224) — Size of the output image after applyingcenter_crop
. Can be overridden bycrop_size
in thepreprocess
method. - do_rescale (
bool
, optional, defaults toTrue
) — Whether to rescale the image by the specified scalerescale_factor
. Can be overridden bydo_rescale
in thepreprocess
method. - rescale_factor (
int
orfloat
, optional, defaults to1/255
) — Scale factor to use if rescaling the image. Can be overridden byrescale_factor
in thepreprocess
method. - do_normalize (
bool
, optional, defaults toTrue
) — Whether to normalize the image. Can be overridden bydo_normalize
in thepreprocess
method. - image_mean (
float
orList[float]
, optional, defaults to[0.48145466, 0.4578275, 0.40821073]
) — Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by theimage_mean
parameter in thepreprocess
method. - image_std (
float
orList[float]
, optional, defaults to[0.26862954, 0.26130258, 0.27577711]
) — Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by theimage_std
parameter in thepreprocess
method. Can be overridden by theimage_std
parameter in thepreprocess
method. - do_convert_rgb (
bool
, optional, defaults toTrue
) — Whether to convert the image to RGB.
Constructs a LLaVa image processor.
preprocess
< source >( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]] do_pad: bool = None do_resize: typing.Optional[bool] = None size: typing.Optional[typing.Dict[str, int]] = None resample: typing.Optional[PIL.Image.Resampling] = None do_center_crop: typing.Optional[bool] = None crop_size: typing.Optional[int] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_convert_rgb: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None **kwargs )
Parameters
- images (
ImageInput
) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, setdo_rescale=False
. - do_pad (
bool
, optional, defaults toself.do_pad
) — Whether to pad the image to a square based on the longest edge. The padding value is determined by theimage_mean
parameter. - do_resize (
bool
, optional, defaults toself.do_resize
) — Whether to resize the image. - size (
Dict[str, int]
, optional, defaults toself.size
) — Size of the image after resizing. Shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio. - resample (
int
, optional, defaults toself.resample
) — Resampling filter to use if resizing the image. This can be one of the enumPILImageResampling
. Only has an effect ifdo_resize
is set toTrue
. - do_center_crop (
bool
, optional, defaults toself.do_center_crop
) — Whether to center crop the image. - crop_size (
Dict[str, int]
, optional, defaults toself.crop_size
) — Size of the center crop. Only has an effect ifdo_center_crop
is set toTrue
. - do_rescale (
bool
, optional, defaults toself.do_rescale
) — Whether to rescale the image. - rescale_factor (
float
, optional, defaults toself.rescale_factor
) — Rescale factor to rescale the image by ifdo_rescale
is set toTrue
. - do_normalize (
bool
, optional, defaults toself.do_normalize
) — Whether to normalize the image. - image_mean (
float
orList[float]
, optional, defaults toself.image_mean
) — Image mean to use for normalization. Only has an effect ifdo_normalize
is set toTrue
. - image_std (
float
orList[float]
, optional, defaults toself.image_std
) — Image standard deviation to use for normalization. Only has an effect ifdo_normalize
is set toTrue
. - do_convert_rgb (
bool
, optional, defaults toself.do_convert_rgb
) — Whether to convert the image to RGB. - return_tensors (
str
orTensorType
, optional) — The type of tensors to return. Can be one of:- Unset: Return a list of
np.ndarray
. TensorType.TENSORFLOW
or'tf'
: Return a batch of typetf.Tensor
.TensorType.PYTORCH
or'pt'
: Return a batch of typetorch.Tensor
.TensorType.NUMPY
or'np'
: Return a batch of typenp.ndarray
.TensorType.JAX
or'jax'
: Return a batch of typejax.numpy.ndarray
.
- Unset: Return a list of
- data_format (
ChannelDimension
orstr
, optional, defaults toChannelDimension.FIRST
) — The channel dimension format for the output image. Can be one of:"channels_first"
orChannelDimension.FIRST
: image in (num_channels, height, width) format."channels_last"
orChannelDimension.LAST
: image in (height, width, num_channels) format.- Unset: Use the channel dimension format of the input image.
- input_data_format (
ChannelDimension
orstr
, optional) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:"channels_first"
orChannelDimension.FIRST
: image in (num_channels, height, width) format."channels_last"
orChannelDimension.LAST
: image in (height, width, num_channels) format."none"
orChannelDimension.NONE
: image in (height, width) format.
Preprocess an image or batch of images.
LlavaProcessor
class transformers.LlavaProcessor
< source >( image_processor = None tokenizer = None patch_size = None vision_feature_select_strategy = None chat_template = None image_token = '<image>' num_additional_image_tokens = 0 **kwargs )
Parameters
- image_processor (LlavaImageProcessor, optional) — The image processor is a required input.
- tokenizer (LlamaTokenizerFast, optional) — The tokenizer is a required input.
- patch_size (
int
, optional) — Patch size from the vision tower. - vision_feature_select_strategy (
str
, optional) — The feature selection strategy used to select the vision feature from the vision backbone. Shoudl be same as in model’s config - chat_template (
str
, optional) — A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. - image_token (
str
, optional, defaults to"<image>"
) — Special token used to denote image location. - num_additional_image_tokens (
int
, optional, defaults to 0) — Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other extra tokens appended, no need to set this arg.
Constructs a LLaVa processor which wraps a LLaVa image processor and a LLaMa tokenizer into a single processor.
LlavaProcessor offers all the functionalities of LlavaImageProcessor and LlamaTokenizerFast. See the
__call__()
and decode() for more information.
This method forwards all its arguments to LlamaTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to LlamaTokenizerFast’s decode(). Please refer to the docstring of this method for more information.
LlavaForConditionalGeneration
class transformers.LlavaForConditionalGeneration
< source >( config: LlavaConfig )
Parameters
- config (LlavaConfig or
LlavaVisionConfig
) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
The LLAVA model which consists of a vision backbone and a language model. 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: LongTensor = None pixel_values: FloatTensor = 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 vision_feature_layer: typing.Union[int, typing.List[int], NoneType] = None vision_feature_select_strategy: typing.Optional[str] = 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 cache_position: typing.Optional[torch.LongTensor] = None logits_to_keep: typing.Union[int, torch.Tensor] = 0 ) → transformers.models.llava.modeling_llava.LlavaCausalLMOutputWithPast
or tuple(torch.FloatTensor)
Parameters
- input_ids (
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.
- pixel_values (
torch.FloatTensor
of shape(batch_size, num_channels, image_size, image_size)) -- The tensors corresponding to the input images. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/main/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__) for details ([]
LlavaProcessor`] uses CLIPImageProcessor for processing images). - attention_mask (
torch.Tensor
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
If
past_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_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.- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- position_ids (
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? - past_key_values (
tuple(tuple(torch.FloatTensor))
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) — Tuple oftuple(torch.FloatTensor)
of lengthconfig.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 lastdecoder_input_ids
(those that don’t have their past key value states given to this model) of shape(batch_size, 1)
instead of alldecoder_input_ids
of shape(batch_size, sequence_length)
. - inputs_embeds (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the model’s internal embedding lookup matrix. - vision_feature_layer (
Union[int, List[int]], *optional*, defaults to -2
) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. - vision_feature_select_strategy (
str
, optional, defaults to"default"
) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of"default"
or"full"
. - use_cache (
bool
, optional) — If set toTrue
,past_key_values
key value states are returned and can be used to speed up decoding (seepast_key_values
). - output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple. - cache_position (
torch.LongTensor
of shape(sequence_length)
, optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_ids
, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. - Args —
labels (
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 (seeinput_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]
.logits_to_keep (
int
ortorch.Tensor
, optional): If anint
, compute logits for the lastlogits_to_keep
tokens. If0
, calculate logits for allinput_ids
(special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If atorch.Tensor
, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns
transformers.models.llava.modeling_llava.LlavaCausalLMOutputWithPast
or tuple(torch.FloatTensor)
A transformers.models.llava.modeling_llava.LlavaCausalLMOutputWithPast
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 (LlavaConfig) and inputs.
-
loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) — Tuple oftuple(torch.FloatTensor)
of lengthconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) — Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) — Tuple oftorch.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.
-
image_hidden_states (
torch.FloatTensor
, optional) — Atorch.FloatTensor
of size (batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
The LlavaForConditionalGeneration 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, LlavaForConditionalGeneration
>>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
>>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
>>> prompt = "USER: <image>\nWhat's the content of the image? ASSISTANT:"
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(**inputs, max_new_tokens=15)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"USER: \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed"