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Adding usage and preprocessing script

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  1. README.md +42 -1
  2. processing_llavagemma.py +138 -0
  3. usage.py +38 -0
README.md CHANGED
@@ -25,7 +25,48 @@ This model has not been assessed for harm or biases, and should not be used for
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  ## How to Get Started with the Model
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- Using the LLaVA-Gemma models currently requires a custom fork of the [`LLaVA`](https://github.com/haotian-liu/LLaVA) library. _We will release converted checkpoints compatible with the HuggingFace implementation of LLaVA shortly._
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## How to Get Started with the Model
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+ Currently using `llava-gemma` requires a [modified preprocessor](/processing_llavagemma.py).
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+
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+ For example usage, see [`usage.py`](/usage.py) or the following code block:
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+
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+
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+ ```python
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+ import requests
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+ from PIL import Image
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+ from transformers import (
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+ LlavaForConditionalGeneration,
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+ AutoTokenizer,
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+ CLIPImageProcessor
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+ )
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+ from processing_llavagemma import LlavaGemmaProcessor # This is in this repo
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+
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+ checkpoint = "Intel/llava-gemma-2b"
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+
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+ # Load model
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+ model = LlavaForConditionalGeneration.from_pretrained(checkpoint)
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+ processor = LlavaGemmaProcessor(
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+ tokenizer=AutoTokenizer.from_pretrained(checkpoint),
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+ image_processor=CLIPImageProcessor.from_pretrained(checkpoint)
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+ )
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+
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+ # Prepare inputs
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+ # Use gemma chat template
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+ prompt = processor.tokenizer.apply_chat_template(
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+ [{'role': 'user', 'content': "What's the content of the image?<image>"}],
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+ tokenize=False,
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+ add_generation_prompt=True
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+ )
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+ url = "https://www.ilankelman.org/stopsigns/australia.jpg"
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+ image = Image.open(requests.get(url, stream=True).raw)
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+ inputs = processor(text=prompt, images=image, return_tensors="pt")
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+ inputs = {k: v.to('cuda') for k, v in inputs.items()}
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+
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+ # Generate
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+ generate_ids = model.generate(**inputs, max_length=30)
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+ output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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+ print(output)
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+
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+ ```
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processing_llavagemma.py ADDED
@@ -0,0 +1,138 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # coding=utf-8
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+ # Copyright 2023 The HuggingFace Inc. team.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """
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+ Processor class for Llava.
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+ Modified to include support for Gemma tokenizer.
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+ """
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+
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+
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+ from typing import List, Optional, Union
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+
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+ from transformers.feature_extraction_utils import BatchFeature
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+ from transformers.image_utils import ImageInput
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+ from transformers.processing_utils import ProcessorMixin
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+ from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
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+ from transformers.utils import TensorType
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+
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+
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+ class LlavaGemmaProcessor(ProcessorMixin):
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+ r"""
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+ Constructs a Llava processor which wraps a Llava image processor and a Llava tokenizer into a single processor.
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+
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+ [`LlavaProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
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+ [`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information.
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+
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+ Args:
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+ image_processor ([`CLIPImageProcessor`], *optional*):
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+ The image processor is a required input.
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+ tokenizer ([`LlamaTokenizerFast`], *optional*):
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+ The tokenizer is a required input.
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+ """
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+
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+ attributes = ["image_processor", "tokenizer"]
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+ image_processor_class = "CLIPImageProcessor"
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+ tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast",
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+ "GemmaTokenizer", "GemmaTokenizerFast")
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+
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+ def __init__(self, image_processor=None, tokenizer=None):
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+ super().__init__(image_processor, tokenizer)
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+
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+ def __call__(
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+ self,
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+ text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
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+ images: ImageInput = None,
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+ padding: Union[bool, str, PaddingStrategy] = False,
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+ truncation: Union[bool, str, TruncationStrategy] = None,
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+ max_length=None,
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+ return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
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+ ) -> BatchFeature:
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+ """
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+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
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+ and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
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+ the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
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+ CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
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+ of the above two methods for more information.
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+
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+ Args:
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+ text (`str`, `List[str]`, `List[List[str]]`):
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+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
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+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
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+ tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
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+ number of channels, H and W are image height and width.
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+ padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
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+ Select a strategy to pad the returned sequences (according to the model's padding side and padding
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+ index) among:
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+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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+ sequence if provided).
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+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
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+ acceptable input length for the model if that argument is not provided.
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+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
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+ lengths).
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+ max_length (`int`, *optional*):
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+ Maximum length of the returned list and optionally padding length (see above).
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+ truncation (`bool`, *optional*):
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+ Activates truncation to cut input sequences longer than `max_length` to `max_length`.
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+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
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+ If set, will return tensors of a particular framework. Acceptable values are:
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+
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+ - `'tf'`: Return TensorFlow `tf.constant` objects.
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+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
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+ - `'np'`: Return NumPy `np.ndarray` objects.
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+ - `'jax'`: Return JAX `jnp.ndarray` objects.
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+
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+ Returns:
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+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
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+
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+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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+ `None`).
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+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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+ """
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+ if images is not None:
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+ pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
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+ else:
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+ pixel_values = None
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+ text_inputs = self.tokenizer(
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+ text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
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+ )
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+
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+ return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
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+
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+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
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+ def batch_decode(self, *args, **kwargs):
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+ """
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+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
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+ refer to the docstring of this method for more information.
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+ """
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+ return self.tokenizer.batch_decode(*args, **kwargs)
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+
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+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
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+ def decode(self, *args, **kwargs):
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+ """
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+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
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+ the docstring of this method for more information.
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+ """
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+ return self.tokenizer.decode(*args, **kwargs)
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+
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+ @property
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+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
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+ def model_input_names(self):
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+ tokenizer_input_names = self.tokenizer.model_input_names
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+ image_processor_input_names = self.image_processor.model_input_names
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+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
usage.py ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import transformers
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+
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+ print(transformers.__version__)
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+
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+ import requests
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+ from PIL import Image
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+ from transformers import (
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+ LlavaForConditionalGeneration,
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+ AutoTokenizer,
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+ CLIPImageProcessor
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+ )
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+ from processing_llavagemma import LlavaGemmaProcessor
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+
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+ checkpoint = "Intel/llava-gemma-2b"
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+
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+ model = LlavaForConditionalGeneration.from_pretrained(checkpoint)
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+ processor = LlavaGemmaProcessor(
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+ tokenizer=AutoTokenizer.from_pretrained(checkpoint),
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+ image_processor=CLIPImageProcessor.from_pretrained(checkpoint)
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+ )
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+
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+ model.to('cuda')
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+
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+
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+ prompt = processor.tokenizer.apply_chat_template(
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+ [{'role': 'user', 'content': "What's the content of the image?<image>"}],
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+ tokenize=False,
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+ add_generation_prompt=True
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+ )
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+ url = "https://www.ilankelman.org/stopsigns/australia.jpg"
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+ image = Image.open(requests.get(url, stream=True).raw)
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+ inputs = processor(text=prompt, images=image, return_tensors="pt")
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+ inputs = {k: v.to('cuda') for k, v in inputs.items()}
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+
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+ # Generate
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+ generate_ids = model.generate(**inputs, max_length=30)
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+ output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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+ print(output)