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v4.48.0

huggingface/transformers

版本发布时间: 2025-01-10 20:14:21

huggingface/transformers最新发布版本:v4.48.0(2025-01-10 20:14:21)

New models

ModernBERT

The ModernBert model was proposed in Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference by Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Galalgher, Raja Bisas, Faisal Ladhak, Tom Aarsen, Nathan Cooper, Grifin Adams, Jeremy Howard and Iacopo Poli.

It is a refresh of the traditional encoder architecture, as used in previous models such as BERT and RoBERTa.

It builds on BERT and implements many modern architectural improvements which have been developed since its original release, such as:

image

Aria

The Aria model was proposed in Aria: An Open Multimodal Native Mixture-of-Experts Model by Li et al. from the Rhymes.AI team.

Aria is an open multimodal-native model with best-in-class performance across a wide range of multimodal, language, and coding tasks. It has a Mixture-of-Experts architecture, with respectively 3.9B and 3.5B activated parameters per visual token and text token.

TimmWrapper

We add a TimmWrapper set of classes such that timm models can be loaded in as transformer models into the library.

Here's a general usage example:

import torch
from urllib.request import urlopen
from PIL import Image
from transformers import AutoConfig, AutoModelForImageClassification, AutoImageProcessor

checkpoint = "timm/resnet50.a1_in1k"
img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

image_processor = AutoImageProcessor.from_pretrained(checkpoint)
inputs = image_processor(img, return_tensors="pt")
model = AutoModelForImageClassification.from_pretrained(checkpoint)

with torch.no_grad():
    logits = model(**inputs).logits

top5_probabilities, top5_class_indices = torch.topk(logits.softmax(dim=1) * 100, k=5)

Thanks to this, timm models now have access to pipelines, as well as Trainer, accelerate device maps, quantization, etc:

import torch
from urllib.request import urlopen
from PIL import Image

from transformers import pipeline

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
pipe = pipeline("image-classification", model="timm/resnet18.a1_in1k")
print(pipe(img))

Pixtral-Large

Pixtral modeling and checkpoint conversion code has been updated to support the new Pixtral-Large model.

ColPali

The ColPali model was proposed in ColPali: Efficient Document Retrieval with Vision Language Models by Manuel Faysse*, Hugues Sibille*, Tony Wu*, Bilel Omrani, Gautier Viaud, Céline Hudelot, Pierre Colombo (* denotes equal contribution). Work lead by ILLUIN Technology.

In the proposed ColPali approach, the authors leverage VLMs to construct efficient multi-vector embeddings directly from document images (“screenshots”) for document retrieval. They train the model to maximize the similarity between these document embeddings and the corresponding query embeddings, using the late interaction method introduced in ColBERT.

colpali_architecture

Falcon3

Falcon3 represents a natural evolution from previous releases, emphasizing expanding the models’ science, math, and code capabilities. This iteration includes five base models: Falcon3-1B-Base, Falcon3-3B-Base, Falcon3-Mamba-7B-Base, Falcon3-7B-Base, and Falcon3-10B-Base. In developing these models, the authors incorporated several key innovations aimed at improving the models’ performances while reducing training costs:

One pre-training: They conducted a single large-scale pretraining run on the 7B model, using 2048 H100 GPU chips, leveraging 14 trillion tokens featuring web, code, STEM, and curated high-quality and multilingual data. Depth up-scaling for improved reasoning: Building on recent studies on the effects of model depth, they upscaled the 7B model to a 10B parameters model by duplicating the redundant layers and continuing pre-training with 2TT of high-quality data. This yielded Falcon3-10B-Base which achieves state-of-the-art zero-shot and few-shot performance for models under 13B parameters. Knowledge distillation for better tiny models: To provide compact and efficient alternatives, we developed Falcon3-1B-Base and Falcon3-3B-Base by leveraging pruning and knowledge distillation techniques, using less than 100GT of curated high-quality data, thereby redefining pre-training efficiency.

Bamba

Bamba-9B is a decoder-only language model based on the Mamba-2 architecture and is designed to handle a wide range of text generation tasks. It is trained from scratch using a two-stage training approach. In the first stage, the model is trained on 2 trillion tokens from the Dolma v1.7 dataset. In the second stage, it undergoes additional training on 200 billion tokens, leveraging a carefully curated blend of high-quality data to further refine its performance and enhance output quality.

Checkout all Bamba-9B model checkpoints here.

VitPose

ViTPose is a state-of-the-art vision transformer-based model for human pose estimation, introduced by Yufei Xu, Jing Zhang, Qiming Zhang, and Dacheng Tao in "ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation”.

The model leverages the capabilities of vision transformers to accurately predict 2D human keypoints. Adopting a top-down approach, ViTPose estimates keypoints locations for each detected person, allowing it to be easily used with any object detection model.

vitpose

DINOv2 with registers

The DINOv2 with Registers model was proposed in Vision Transformers Need Registers by Timothée Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski.

The Vision Transformer (ViT) is a transformer encoder model (BERT-like) originally introduced to do supervised image classification on ImageNet.

Next, people figured out ways to make ViT work really well on self-supervised image feature extraction (i.e. learning meaningful features, also called embeddings) on images without requiring any labels. Some example papers here include DINOv2 and MAE.

The authors of DINOv2 noticed that ViTs have artifacts in attention maps. It’s due to the model using some image patches as “registers”. The authors propose a fix: just add some new tokens (called “register” tokens), which you only use during pre-training (and throw away afterwards). This results in:

Emu3

The Emu3 model was proposed in Emu3: Next-Token Prediction is All You Need by Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, Yingli Zhao, Yulong Ao, Xuebin Min, Tao Li, Boya Wu, Bo Zhao, Bowen Zhang, Liangdong Wang, Guang Liu, Zheqi He, Xi Yang, Jingjing Liu, Yonghua Lin, Tiejun Huang, Zhongyuan Wang.

Emu3 sets a new standard in multimodal AI by using next-token prediction to handle images, text, and videos. It simplifies multimodal modeling by tokenizing all data into a unified format and training a single transformer. Visual data is tokenized using vector quantization methods based on VQ-VAE model. Discretized visual tokens are later fused with text token ids for image and text generation.

Emu3 outperforms leading models like SDXL and LLaVA-1.6 in both generation and perception tasks, without relying on diffusion or compositional methods..

Cohere2

A new Cohere update was added through a new "Cohere2" set of classes.

TextNet

TextNet is a lightweight and efficient architecture designed specifically for text detection, offering superior performance compared to traditional models like MobileNetV3. With variants TextNet-T, TextNet-S, and TextNet-B (6.8M, 8.0M, and 8.9M parameters respectively), it achieves an excellent balance between accuracy and inference speed.

DiffLlama

Differential Transformer combines the Llama architecture with Differential Transformer's Attention.

PixtralLarge

The conversion script needed a few update, while the modeling code was barely changed!

Moonshine

Moonshine is an autoregressive speech recognition encoder-decoder model that improves upon Whisper's architecture. Namely, it replaces absolute position embeddings with Rotary Position Embeddings (RoPE). This allows Moonshine to handle audio inputs of any length, unlike Whisper, which is restricted to fixed 30-second windows. It was introduced by Nat Jeffries, Evan King, Manjunath Kudlur, Guy Nicholson, James Wang, and Pete Warden in Moonshine: Speech Recognition for Live Transcription and Voice Commands .

Quantization methods

VPTQ Quantization

From the VPTQ contributors:

VPTQ is a novel Post-Training Quantization method that leverages Vector Quantization to high accuracy on LLMs at an extremely low bit-width (<2-bit). VPTQ can compress 70B, even the 405B model, to 1-2 bits without retraining and maintain high accuracy.. More details here: https://github.com/microsoft/vptq

HIGGS Quantization

From the contributors:

HIGGS is a new 0-shot quantization algorithm that combines Hadamard preprocessing with MSE-Optimal quantization grids to achieve lower quantization error and SOTA performance. You can find more information in the paper.

Runtime support for HIGGS is implemented through FLUTE, and its library.

This PR adds support for HIGGS+FLUTE into transformers allowing for low-error 0-shot quantization and fast LLM inference.

Cleanup

We merged a cleanup for vision language models, to make sure it all models are standardized.

Breaking changes

Conversion scripts

Many models in Transformers include scripts to convert the original model checkpoints into a Transformers-compatible format. These scripts can be found in the repo using the glob pattern models/**/convert_*.py. They were a recurring source of vulnerability reports and CVEs because many models were originally released using insecure formats like older PyTorch .bin weights or pickle files. The conversion scripts had to open these formats, and this meant that they were vulnerable to maliciously crafted inputs.

In practice, we do not see this as a serious vulnerability. The conversion scripts are never imported or called by the rest of the library; each script is standalone, and so the only way to exploit the vulnerability is to create a malicious checkpoint, induce a user to download it, and then also induce them to manually call a specific conversion script on it.

However, even if there is little practical risk of an exploit, we are aware that open vulnerability reports create a compliance problem for users, and so beginning with this release we will be excluding these conversion scripts from release branches and wheels. They will remain accessible to developers on the main branch.

Backtracking in Nougat

A regular expression used within the Nougat code has been modified to ensure it does not hang. The method should output the same results but we cannot guarantee it; we recommend upgrading to the latest transformers if you use this model to ensure your code is performance-optimized.

Whisper decoding

This PR finalizes work that aimes to enable short-form (< 30 secs) and long-form generation using temperature fallback. It is a significant improvement to the whisper codebase, but it does result in the following breaking changes:

➡️ Previously:
• Short-form: Returned a ModelOutput or torch.LongTensor, including decoder input IDs and the EOS token ID.
• Long-form: Returned a Dict or torch.LongTensor, excluding decoder input IDs and the EOS token ID.

➡️ From now on:
Short-form and long-form generation are now treated identically, meaning output differentiation based on these modes is no longer applicable.

Decoder input IDs and EOS token IDs are never returned, except in two specific cases: when return_dict_in_generate=True and (return_timestamps=False or force_unique_generate_call=True).

In this case, the output will be a ModelOutput, which is the result of the underlying call to GenerationMixin’s generate. Indeed, return_timestamps=False ensures no seeking occurs; only a single call to generate is made. Therefore, this output includes both decoder input IDs and the EOS token ID.

Attention refactor

In order to have a cleaner, isolated, future-proof code for the attention layers, they have been refactored so as to keep the model attention code within their files; but attention definitions relating to SDPA, Flash Attention, and other types of attention have been moved to a common file.

Bugfixes and improvements

Significant community contributions

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

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