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v4.47.0

huggingface/transformers

版本发布时间: 2024-12-06 01:45:28

huggingface/transformers最新发布版本:v4.47.1(2024-12-17 23:42:54)

New models

PaliGemma-2

PaliGemma 2 and PaliGemma are lightweight open vision-language models (VLM) inspired by PaLI-3, and based on open components like the SigLIP vision model and the Gemma language model. PaliGemma takes both images and text as inputs and can answer questions about images with detail and context, meaning that PaliGemma can perform deeper analysis of images and provide useful insights, such as captioning for images and short videos, object detection, and reading text embedded within images.

PaliGemma 2 is available in 3B, 10B, and 28B parameter sizes, which are based on Gemma 2 2B, 9B, and 27B models, respectively. The original PaliGemma models are available in the 3B size. For more information on Gemma model variants, see the Gemma models list. PaliGemma model variants support different pixel resolutions for image inputs, including 224 x 224, 448 x 448, and 896 x 896 pixels.

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I-JEPA

The I-JEPA model was proposed in Image-based Joint-Embedding Predictive Architecture by Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas. I-JEPA is a self-supervised learning method that predicts the representations of one part of an image based on other parts of the same image. This approach focuses on learning semantic features without relying on pre-defined invariances from hand-crafted data transformations, which can bias specific tasks, or on filling in pixel-level details, which often leads to less meaningful representations.

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OLMo 2

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The OLMo2 model is the successor of the OLMo model, which was proposed in OLMo: Accelerating the Science of Language Models.

The architectural changes from the original OLMo model to this model are:

Commits:

Layer-Skip Llama

We add support for Meta's Layer-Skip Llama 3.2 1B model.

The Llama3.2 1B model was continually pretrained with LayerSkip recipe, early exit loss and layer dropout, as presented in Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding and is capable of performing self-speculative decoding: decode with earlier layers and verify with remaining layers.

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Tensor Parallel implementation

This PR uses the torch.distributed.tensor.parallel subpackage to implement Tensor Parallel for Llama (as an example).

The motivation is multi-fold:

  1. to make modeling code simple as single-worker case:
    all manual TP implementations under if self.config.pretraining_tp > 1 can be removed.

  2. to make tensor parallelism easily accessible by users:
    added a model.tensor_parallel(device_mesh) method that allows users to turn a single-proc model into a parallel model. !- Please guide me to a right place to put this function/method if PreTrainedModel is not a preferred place. -!

This is the first PR of many to simplify and enable Tensor Parallel across models.

Farewell, Python 3.8

Python 3.8 reaches end of life, and, as such, we drop it from our CI.

GGUF improvements

Several improvements have been done to the GGUF support in transformers; notably by adding new architectures to the list of supported architectures.

Fast processors

We continue the work to improve the speed of fast processors as detailed in this roadmap.

We contribute a fast processor to RT-DETR.

New pipelines

A new pipeline has been added to transformers: image-text-to-text!

the pipeline support the following inputs:

Notable refactors

Separate chat templates into a single file

We have had several issues with chat templates because they're stored as single lines in the JSON config files:

The solution:

For now, we continue saving in the old format by default. I'll probably keep it this way for several versions before making the new format the default, to ensure that most users are able to load the new format before it becomes common. Until then, the new format should mostly be used for testing, to make sure it's ready for deployment when we do the switch.

Large modular logic refactor

This PR largely rework the logic we use in the modular converter. It is (hopefully) clearer and maintainable. Instead of going in all directions, adding stuff, then deleting it if not needed, we now do the following:

Community bugfixes and improvements

Significant community contributions

The following contributors have made significant changes to the library over the last release:

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