The Chameleon model was proposed in Chameleon: Mixed-Modal Early-Fusion Foundation Models by META AI Chameleon Team. Chameleon is a Vision-Language Model that use vector quantization to tokenize images which enables the model to generate multimodal output. The model takes images and texts as input, including an interleaved format, and generates textual response. Image generation module is not released yet.
The abstract from the paper is the following:
We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in unified modeling of full multimodal documents
This model was contributed by joaogante and RaushanTurganbay. The original code can be found here.
We advise users to use padding_side="left"
when computing batched generation as it leads to more accurate results. Simply make sure to set processor.tokenizer.padding_side = "left"
before generating.
Note that Chameleon was tuned for safety alignment. If the model is refusing to answer, consider asking a more concrete question, instead of an open question.
Chameleon generates in chat format which means that the generated text will always be the “assistant’s turn”. You can enable a text completion generation by passing return_for_text_completion=True
when calling the processor.
[!NOTE] Chameleon implementation in Transformers uses a special image token to indicate where to merge image embeddings. For special image token we didn’t add a new one but used one of the reserved tokens:
<reserved08707>
. You have to add<image>
to your prompt in the place where the image should be embedded for correct generation.
Chameleon is a gated model so make sure to have access and login to Hugging Face Hub using a token.
Here’s how to load the model and perform inference in half-precision (torch.bfloat16
):
from transformers import ChameleonProcessor, ChameleonForConditionalGeneration
import torch
from PIL import Image
import requests
processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
model = ChameleonForConditionalGeneration.from_pretrained("facebook/chameleon-7b", torch_dtype=torch.bfloat16, device_map="cuda")
# prepare image and text prompt
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
prompt = "What do you see in this image?<image>"
inputs = processor(prompt, image, return_tensors="pt").to(model.device)
# autoregressively complete prompt
output = model.generate(**inputs, max_new_tokens=50)
print(processor.decode(output[0], skip_special_tokens=True))
Chameleon can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). Here is how you can do it:
from transformers import ChameleonProcessor, ChameleonForConditionalGeneration
import torch
from PIL import Image
import requests
processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
model = ChameleonForConditionalGeneration.from_pretrained("facebook/chameleon-7b", torch_dtype=torch.bfloat16, device_map="cuda")
# Get three 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)
url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
image_snowman = Image.open(requests.get(url, stream=True).raw)
# Prepare a batched prompt, where the first one is a multi-image prompt and the second is not
prompts = [
"What do these images have in common?<image><image>",
"<image>What is shown in this image?"
]
# We can simply feed images in the order they have to be used in the text prompt
# Each "<image>" token uses one image leaving the next for the subsequent "<image>" tokens
inputs = processor(text=prompts, images=[image_stop, image_cats, image_snowman], padding=True, return_tensors="pt").to(device="cuda", dtype=torch.bfloat16)
# Generate
generate_ids = model.generate(**inputs, max_new_tokens=50)
processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, pip install bitsandbytes
and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
from transformers import ChameleonForConditionalGeneration, BitsAndBytesConfig
# specify how to quantize the model
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = ChameleonForConditionalGeneration.from_pretrained("facebook/chameleon-7b", quantization_config=quantization_config, device_map="cuda")
The models supports both, Flash-Attention 2 and PyTorch’s torch.nn.functional.scaled_dot_product_attention
which can be enables for optimization. SDPA is the default options when you load the model, If you want to switch for Flash Attention 2, first make sure to install flash-attn. Refer to the original repository regarding that package installation. Simply change the snippet above with:
from transformers import ChameleonForConditionalGeneration
model_id = "facebook/chameleon-7b"
model = ChameleonForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
attn_implementation="flash_attention_2"
).to(0)
( vocab_size = 65536 hidden_size = 4096 intermediate_size = 11008 num_hidden_layers = 32 num_attention_heads = 32 num_key_value_heads = 32 hidden_act = 'silu' max_position_embeddings = 4096 initializer_range = 0.02 rms_norm_eps = 1e-05 use_cache = True pad_token_id = None bos_token_id = 1 eos_token_id = 2 tie_word_embeddings = False rope_theta = 10000.0 rope_scaling = None attention_bias = False attention_dropout = 0.0 model_parallel_size = 1 swin_norm = False vq_config = None vocabulary_map = None mlp_bias = False **kwargs )
Parameters
int
, optional, defaults to 65536) —
Vocabulary size of the chameleon model. Defines the number of different tokens that can be represented by the
inputs_ids
passed when calling ChameleonModel; this includes text and image tokens. int
, optional, defaults to 4096) —
Dimension of the hidden representations. int
, optional, defaults to 11008) —
Dimension of the MLP representations. int
, optional, defaults to 32) —
Number of hidden layers in the Transformer decoder. int
, optional, defaults to 32) —
Number of attention heads for each attention layer in the Transformer decoder. int
, optional, defaults to 32) —
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](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
num_attention_heads`. str
or function
, optional, defaults to "silu"
) —
The non-linear activation function (function or string) in the decoder. int
, optional, defaults to 4096) —
The maximum sequence length that this model might ever be used with. Chameleon supports up to 4096 tokens. 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
. int
, optional) —
Padding token id. int
, optional, defaults to 1) —
Beginning of stream token id. int
, optional, defaults to 2) —
End of stream token id. bool
, optional, defaults to False
) —
Whether to tie weight embeddings float
, optional, defaults to 10000.0) —
The base period of the RoPE embeddings. Dict
, optional) —
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
{"type": strategy name, "factor": scaling factor}
. When using this flag, don’t update
max_position_embeddings
to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/Localchameleon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions. bool
, defaults to False
, optional, defaults to False
) —
Whether to use a bias in the query, key, value and output projection layers during self-attention. float
, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities. int
, optional, defaults to 1) —
Number of shards used when training the model. This will be used in qk layernorm because the original Chameleon inference
doesn’t do reduction in those layers and each rank has its own biases. bool
, optional, defaults to False
) —
Use Swin Transformer normalization. dict
, optional) —
ChameleonVQConfig instance containing the configuration for the VQ-VAE model. dict
, optional) —
A dictionary containing the vocabulary map from the tokenizer. Used to obtain tokens from the image inputs. bool
, optional, defaults to False
) —
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. This is the configuration class to store the configuration of a ChameleonModel. It is used to instantiate a chameleon 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 meta/chameleon-7B.
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 ChameleonModel, ChameleonConfig
>>> # Initializing a chameleon chameleon-7b style configuration
>>> configuration = ChameleonConfig()
>>> # Initializing a model from the chameleon-7b style configuration
>>> model = ChameleonModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
( embed_dim: int = 256 num_embeddings: int = 8192 double_latent: bool = False latent_channels: int = 256 resolution: int = 512 in_channels: int = 3 base_channels: int = 128 channel_multiplier: List = [1, 1, 2, 2, 4] num_res_blocks: int = 2 attn_resolutions: List = None dropout: float = 0.0 attn_type: str = 'vanilla' initializer_range = 0.02 **kwargs )
Parameters
int
, optional, defaults to 256) —
Dimensionality of each embedding vector. int
, optional, defaults to 8192) —
Number of codebook embeddings. bool
, optional, defaults to False
) —
Whether to use double z channels. int
, optional, defaults to 256) —
Number of channels for the latent space. int
, optional, defaults to 512) —
Resolution of the input images. int
, optional, defaults to 3) —
Number of input channels. int
, optional, defaults to 128) —
Base channel count. List[int]
, optional, defaults to [1, 1, 2, 2, 4]
) —
Channel multipliers for each resolution. int
, optional, defaults to 2) —
Number of residual blocks. List[int]
, optional) —
Resolutions to apply attention. float
, optional, defaults to 0.0) —
Dropout rate. str
, optional, defaults to "vanilla"
) —
Attention type used in VQ-GAN encoder. Can be “vanilla” or None. float
, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. This is the configuration class to store the configuration of a ChameleonVQModel
. It is used to instantiate a
ChameleonVQModel
according to the specified arguments, defining the model architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information. Instantiating a
configuration with the defaults will yield a similar configuration to the VQModel of the
meta/chameleon-7B.
( image_processor tokenizer image_seq_length: int = 1024 image_token: str = '<image>' )
Parameters
int
, optional, defaults to 1024) —
Sequence length of one image embedding. str
, optional, defaults to "<image>"
) —
The special token used to indicate image in the text. Constructs a Chameleon processor which wraps a Chameleon image processor and a Chameleon tokenizer into a single processor.
ChameleonProcessor offers all the functionalities of ChameleonImageProcessor 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.
( do_resize: bool = True size: Dict = None resample: Resampling = 1 do_center_crop: bool = True crop_size: Dict = None do_rescale: bool = True rescale_factor: Union = 0.0078 do_normalize: bool = True image_mean: Union = None image_std: Union = None do_convert_rgb: bool = True **kwargs )
Parameters
bool
, optional, defaults to True
) —
Whether to resize the image’s (height, width) dimensions to the specified size
. Can be overridden by
do_resize
in the preprocess
method. Dict[str, int]
optional, defaults to {"shortest_edge" -- 512}
):
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 by size
in the preprocess
method. PILImageResampling
, optional, defaults to 1) —
Resampling filter to use if resizing the image. Can be overridden by resample
in the preprocess
method. bool
, optional, defaults to True
) —
Whether to center crop the image to the specified crop_size
. Can be overridden by do_center_crop
in the
preprocess
method. Dict[str, int]
optional, defaults to {“height” — 512, “width”: 512}):
Size of the output image after applying center_crop
. Can be overridden by crop_size
in the preprocess
method. bool
, optional, defaults to True
) —
Whether to rescale the image by the specified scale rescale_factor
. Can be overridden by do_rescale
in
the preprocess
method. int
or float
, optional, defaults to 0.0078) —
Scale factor to use if rescaling the image. Can be overridden by rescale_factor
in the preprocess
method. bool
, optional, defaults to True
) —
Whether to normalize the image. Can be overridden by do_normalize
in the preprocess
method. float
or List[float]
, optional, defaults to [1.0, 1.0, 1.0]
) —
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 the image_mean
parameter in the preprocess
method. float
or List[float]
, optional, defaults to [1.0, 1.0, 1.0]
) —
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 the image_std
parameter in the preprocess
method.
Can be overridden by the image_std
parameter in the preprocess
method. bool
, optional, defaults to True
) —
Whether to convert the image to RGB. Constructs a Chameleon image processor.
( images: Union do_resize: bool = None size: Dict = None resample: Resampling = None do_center_crop: bool = None crop_size: int = None do_rescale: bool = None rescale_factor: float = None do_normalize: bool = None image_mean: Union = None image_std: Union = None do_convert_rgb: bool = None return_tensors: Union = None data_format: Optional = <ChannelDimension.FIRST: 'channels_first'> input_data_format: Union = None **kwargs )
Parameters
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, set do_rescale=False
. bool
, optional, defaults to self.do_resize
) —
Whether to resize the image. Dict[str, int]
, optional, defaults to self.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. int
, optional, defaults to self.resample
) —
Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling
. Only
has an effect if do_resize
is set to True
. bool
, optional, defaults to self.do_center_crop
) —
Whether to center crop the image. Dict[str, int]
, optional, defaults to self.crop_size
) —
Size of the center crop. Only has an effect if do_center_crop
is set to True
. bool
, optional, defaults to self.do_rescale
) —
Whether to rescale the image. float
, optional, defaults to self.rescale_factor
) —
Rescale factor to rescale the image by if do_rescale
is set to True
. bool
, optional, defaults to self.do_normalize
) —
Whether to normalize the image. float
or List[float]
, optional, defaults to self.image_mean
) —
Image mean to use for normalization. Only has an effect if do_normalize
is set to True
. float
or List[float]
, optional, defaults to self.image_std
) —
Image standard deviation to use for normalization. Only has an effect if do_normalize
is set to
True
. bool
, optional, defaults to self.do_convert_rgb
) —
Whether to convert the image to RGB. str
or TensorType
, optional) —
The type of tensors to return. Can be one of:np.ndarray
.TensorType.TENSORFLOW
or 'tf'
: Return a batch of type tf.Tensor
.TensorType.PYTORCH
or 'pt'
: Return a batch of type torch.Tensor
.TensorType.NUMPY
or 'np'
: Return a batch of type np.ndarray
.TensorType.JAX
or 'jax'
: Return a batch of type jax.numpy.ndarray
.ChannelDimension
or str
, optional, defaults to ChannelDimension.FIRST
) —
The channel dimension format for the output image. Can be one of:"channels_first"
or ChannelDimension.FIRST
: image in (num_channels, height, width) format."channels_last"
or ChannelDimension.LAST
: image in (height, width, num_channels) format.ChannelDimension
or str
, 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"
or ChannelDimension.FIRST
: image in (num_channels, height, width) format."channels_last"
or ChannelDimension.LAST
: image in (height, width, num_channels) format."none"
or ChannelDimension.NONE
: image in (height, width) format.Preprocess an image or batch of images.
( config: ChameleonVQVAEConfig )
Parameters
The VQ-VAE model used in Chameleon for encoding/decoding images into discrete tokens. This model follows the “Make-a-scene: Scene-based text-to-image generation with human priors” paper from Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman.
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.
Define the computation performed at every call.
Should be overridden by all subclasses.
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
registered hooks while the latter silently ignores them.
( config: ChameleonConfig )
Parameters
The bare chameleon 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.
Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a ChameleonDecoderLayer
( input_ids: LongTensor = None pixel_values: FloatTensor = None attention_mask: Optional = None position_ids: Optional = None past_key_values: Optional = None inputs_embeds: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None cache_position: Optional = None ) → transformers.modeling_outputs.BaseModelOutputWithPast 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.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. See ChameleonImageProcessor.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 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]
.
Cache
, optional) —
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values
returned by the model at a previous stage of decoding, when use_cache=True
or config.use_cache=True
.
Should always be a Cache instance and the model will output the same cache instance.
If past_key_values
are used, the user can optionally input only the last input_ids
(those that don’t
have their past key value states given to this model) of shape (batch_size, 1)
instead of all 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.LongTensor
of shape (sequence_length)
, optional) —
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_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. Returns
transformers.modeling_outputs.BaseModelOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPast 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 (ChameleonConfig) and inputs.
last_hidden_state (torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the model.
If past_key_values
is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size)
is output.
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)
) and optionally if
config.is_encoder_decoder=True
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 optionally if
config.is_encoder_decoder=True
in the cross-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.
The ChameleonModel 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 transformers import AutoTokenizer, ChameleonModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("meta/chameleon-7b")
>>> model = ChameleonModel.from_pretrained("meta/chameleon-7b")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
( config )
Parameters
Chameleon Model with a head on top used for outputting logits for next token prediction. 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.
( input_ids: LongTensor = None pixel_values: FloatTensor = None attention_mask: Optional = None position_ids: Optional = None past_key_values: Optional = None inputs_embeds: Optional = None labels: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None cache_position: Optional = None ) → transformers.modeling_outputs.CausalLMOutputWithPast 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.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. See ChameleonImageProcessor.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 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]
.
Cache
, optional) —
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values
returned by the model at a previous stage of decoding, when use_cache=True
or config.use_cache=True
.
Should always be a Cache instance and the model will output the same cache instance.
If past_key_values
are used, the user can optionally input only the last input_ids
(those that don’t
have their past key value states given to this model) of shape (batch_size, 1)
instead of all 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.LongTensor
of shape (sequence_length)
, optional) —
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_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 (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.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithPast 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 (ChameleonConfig) 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.
The ChameleonForConditionalGeneration 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 transformers import ChameleonProcessor, ChameleonForConditionalGeneration
>>> import torch
>>> import requests
>>> from PIL import Image
>>> model = ChameleonForConditionalGeneration.from_pretrained("facebook/chameleon-7b", torch_dtype=torch.bfloat16)
>>> processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
>>> prompt = "I used to know a lot about constellations when I was younger, but as I grew older, I forgot most of what I knew. These are the only two constellations that I really remember now.<image><image>I would like for you to tell me about 3 more constellations and give me a little bit of history about the constellation."
>>> image = Image.open(requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw)
>>> image_2 = Image.open(requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw)
>>> inputs = processor(prompt, images=[image, image_2], return_tensors="pt").to(model.device, torch.bfloat16)
>>> generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False)
>>> processor.batch_decode(generated_ids, skip_special_tokens=True)[0]