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Patch release: v4.35.1

14 Nov 14:59
@LysandreJik LysandreJik
v4.35.1
10f3e7b
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Patch release: v4.35.1

A patch release was made for the following three commits:

  • Fix FA2 import + deprecation cycle (#27330)
  • Fix from_pt flag when loading with safetensors (#27394)
  • Default to msgpack for safetensors (#27460)
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Safetensors serialization by default, DistilWhisper, Fuyu, Kosmos-2, SeamlessM4T, Owl-v2

02 Nov 17:00
@LysandreJik LysandreJik
v4.35.0
f1185a4
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Safetensors serialization by default, DistilWhisper, Fuyu, Kosmos-2, SeamlessM4T, Owl-v2

New models

Distil-Whisper

Distil-Whisper is a distilled version of Whisper that is 6 times faster, 49% smaller, and performs within 1% word error rate (WER) on out-of-distribution data. It was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling.

Distil-Whisper copies the entire encoder from Whisper, meaning it retains Whisper's robustness to different audio conditions. It only copies 2 decoder layers, which significantly reduces the time taken to auto-regressively generate text tokens:

Distil-Whisper is MIT licensed and directly available in the Transformers library with chunked long-form inference, Flash Attention 2 support, and Speculative Decoding. For details on using the model, refer to the following instructions.

Joint work from @sanchit-gandhi, @patrickvonplaten and @srush.

Fuyu

image

The Fuyu model was created by ADEPT, and authored by Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, Sağnak Taşırlar.

The authors introduced Fuyu-8B, a decoder-only multimodal model based on the classic transformers architecture, with query and key normalization. A linear encoder is added to create multimodal embeddings from image inputs.

By treating image tokens like text tokens and using a special image-newline character, the model knows when an image line ends. Image positional embeddings are removed. This avoids the need for different training phases for various image resolutions. With 8 billion parameters and licensed under CC-BY-NC, Fuyu-8B is notable for its ability to handle both text and images, its impressive context size of 16K, and its overall performance.

Joint work from @molbap, @pcuenca, @amyeroberts, @ArthurZucker

SeamlessM4T

image

The SeamlessM4T model was proposed in SeamlessM4T — Massively Multilingual & Multimodal Machine Translation by the Seamless Communication team from Meta AI.

SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text.

SeamlessM4T enables multiple tasks without relying on separate models:

  • Speech-to-speech translation (S2ST)
  • Speech-to-text translation (S2TT)
  • Text-to-speech translation (T2ST)
  • Text-to-text translation (T2TT)
  • Automatic speech recognition (ASR)

SeamlessM4TModel can perform all the above tasks, but each task also has its own dedicated sub-model.

Kosmos-2

The KOSMOS-2 model was proposed in Kosmos-2: Grounding Multimodal Large Language Models to the World by Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, Furu Wei.

KOSMOS-2 is a Transformer-based causal language model and is trained using the next-word prediction task on a web-scale dataset of grounded image-text pairs GRIT. The spatial coordinates of the bounding boxes in the dataset are converted to a sequence of location tokens, which are appended to their respective entity text spans (for example, a snowman followed by <patch_index_0044><patch_index_0863>). The data format is similar to “hyperlinks” that connect the object regions in an image to their text span in the corresponding caption.

Owl-v2

OWLv2 was proposed in Scaling Open-Vocabulary Object Detection by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2 scales up OWL-ViT using self-training, which uses an existing detector to generate pseudo-box annotations on image-text pairs. This results in large gains over the previous state-of-the-art for zero-shot object detection.

🚨🚨🚨 Safetensors by default for torch serialization 🚨🚨🚨

Version v4.35.0 now puts safetensors serialization by default. This is a significant change targeted at making users of the Hugging Face Hub, transformers, and any downstream library leveraging it safer.

The safetensors library is a safe serialization framework for machine learning tensors. It has been audited and will become the default serialization framework for several organizations (Hugging Face, EleutherAI, Stability AI).

It was already the default loading mechanism since v4.30.0 and would therefore already default to loading model.safetensors files instead of pytorch_model.bin if these were present in the repository.

With v4.35.0, any call to save_pretrained for torch models will now save a safetensors file. This safetensors file is in the PyTorch format, but can be loaded in TensorFlow and Flax models alike.

⚠️ If you run into any issues with this, please let us know ASAP in the issues so that we may help you. Namely, the following errors may indicate something is up:

  • Loading a safetensors file and having a warning mentioning missing weights unexpectedly
  • Obtaining completely wrong/random results at inference after loading a pretrained model that you have saved in safetensors

If you wish to continue saving files in the .bin format, you can do so by specifying safe_serialization=False in all your save_pretrained calls.

Chat templates

Chat templates have been expanded with the addition of the add_generation_prompt argument to apply_chat_template(). This has also enabled us to rework the ConversationalPipeline class to use chat templates. Any model with a chat template is now automatically usable through ConversationalPipeline.

Guides

Two new guides on LLMs were added the library:

Quantization

Exllama-v2 integration

Exllama-v2 provides better GPTQ kernel for higher throughput and lower latency for GPTQ models. The original code can be found here.

You will need the latest versions of optimum and auto-gptq. Read more about the integration here.

AWQ integration

AWQ is a new and popular quantization scheme, already used in various libraries such as TGI, vllm, etc. and known to be faster than GPTQ models according to some benchmarks. The original code can be found here and here you can read more about the original paper.

Screenshot 2023-10-24 at 17 56 56

We support AWQ inference with original kernels as well as kernels provided through autoawq package that you can simply install with pip install autoawq.

We also provide an example script on how to push quantized weights on the hub on the original repository.

Read more about the benchmarks and the integration here

GPTQ on CPU !

You can now run GPTQ models on CPU using the latest version of auto-gptq thanks to @vivekkhandelwal1 !

Attention mask refactor

We refactored the attention mask logic for major models in transformers. For instance, we removed padding_mask argument which was ambiguous for some users

Flash Attention 2 for more models + quantizat...

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srush, dotneet, and 118 other contributors
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Patch release: v4.34.1

18 Oct 21:15
@ArthurZucker ArthurZucker
v4.34.1
acc394c
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Patch release: v4.34.1

A patch release was made for the following three commits:

  • Add add_generation_prompt argument to apply_chat_template (#26573)
  • Fix backward compatibility of Conversation (#26741)
  • [Tokenizer] Fix slow and fast serialization (#26570)
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v4.34: Mistral, Persimmon, Prompt templating, Flash Attention 2, Tokenizer refactor

03 Oct 15:00
@LysandreJik LysandreJik
v4.34.0
b71f20a
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v4.34: Mistral, Persimmon, Prompt templating, Flash Attention 2, Tokenizer refactor

New models

Mistral

Mistral-7B-v0.1 is a decoder-based LM with the following architectural choices:

  • Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
  • GQA (Grouped Query Attention) - allowing faster inference and lower cache size.
  • Byte-fallback BPE tokenizer - ensures that characters are never mapped to out-of-vocabulary tokens.

Persimmon

The authors introduced Persimmon-8B, a decoder model based on the classic transformers architecture, with query and key normalization. Persimmon-8B is a fully permissively licensed model with approximately 8 billion parameters, released under the Apache license. Some of the key attributes of Persimmon-8B are long context size (16K), performance, and capabilities for multimodal extensions.

BROS

BROS stands for BERT Relying On Spatiality. It is an encoder-only Transformer model that takes a sequence of tokens and their bounding boxes as inputs and outputs a sequence of hidden states. BROS encode relative spatial information instead of using absolute spatial information.

ViTMatte

ViTMatte leverages plain Vision Transformers for the task of image matting, which is the process of accurately estimating the foreground object in images and videos.

Nougat

Nougat uses the same architecture as Donut, meaning an image Transformer encoder and an autoregressive text Transformer decoder to translate scientific PDFs to markdown, enabling easier access to them.

Prompt templating

We've added a new template feature for chat models. This allows the formatting that a chat model was trained with to be saved with the model, ensuring that users can exactly reproduce that formatting when they want to fine-tune the model or use it for inference. For more information, see our template documentation.

🚨🚨 Tokenizer refactor

🚨Workflow Changes 🚨:

These are not breaking changes per se but rather bugfixes. However, we understand that this may result in some workflow changes so we highlight them below.

  • unique_no_split_tokens attribute removed and not used in the internal logic
  • sanitize_special_tokens() follows a deprecation cycle and does nothing
  • All attributes in SPECIAL_TOKENS_ATTRIBUTES are stored as AddedTokens and no strings.
  • loading a slow from a fast or a fast from a slow will no longer raise and error if the tokens added don't have the correct index. This is because they will always be added following the order of the added_tokens but will correct mistakes in the saved vocabulary if there are any. (And there are a lot in old format tokenizers)
  • the length of a tokenizer is now max(set(self.get_vocab().keys())) accounting for holes in the vocab. The vocab_size no longer takes into account the added vocab for most of the tokenizers (as it should not). Mostly breaking for T5
  • Adding a token using tokenizer.add_tokens([AddedToken("hey", rstrip=False, normalized=True)]) now takes into account rstrip, lstrip, normalized information.
  • added_tokens_decoder holds AddedToken, not strings.
  • add_tokens() for both fast and slow will always be updated if the token is already part of the vocab, allowing for custom stripping.
  • initializing a tokenizer form scratch will now add missing special tokens to the vocab.
  • stripping is not always done for special tokens! 🚨 Only if the AddedToken has lstrip=True and rstrip=True
  • fairseq_ids_to_tokens attribute removed for Barthez (was not used)

➕ Most visible features:

  • printing a tokenizer now shows tokenizer.added_tokens_decoder for both fast and slow tokenizers. Moreover, additional tokens that were already part of the initial vocab are also found there.
  • faster from_pretrained, faster add_tokens because special and non special can be mixed together and the trie is not always rebuilt.
  • faster encode/decode with caching mechanism for added_tokens_decoder/encoder.
  • information is fully saved in the tokenizer_config.json

For any issues relating to this, make sure to open a new issue and ping @ArthurZucker.

Flash Attention 2

FA2 support added to transformers for most popular architectures (llama, mistral, falcon) architectures actively being contributed in this issue (#26350). Simply pass use_flash_attention_2=True when calling from_pretrained

In the future, PyTorch will support Flash Attention 2 through torch.scaled_dot_product_attention, users would be able to benefit from both (transformers core & transformers + SDPA) implementations of Flash Attention-2 with simple changes (model.to_bettertransformer() and force-dispatch the SDPA kernel to FA-2 in the case of SDPA)

For our future plans regarding integrating F.sdpa from PyTorch in core transformers, see here: #26557

Lazy import structure

Support for lazy loading integration libraries has been added. This will drastically speed up importing transformers and related object from the library.

Example before this change:

2023-09-11 11:07:52.010179: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
python3 -c "from transformers import CLIPTextModel"  3.31s user 3.06s system 220% cpu 2.893 total

After this change:

python3 -c "from transformers import CLIPTextModel"  1.70s user 1.49s system 220% cpu 1.447 total

Bugfixes and improvements

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jbochi, tleyden, and 92 other contributors
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Patch release: v4.33.3

27 Sep 15:09
@LysandreJik LysandreJik
v4.33.3
bffac92
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Patch release: v4.33.3

A patch release was made for the following three commits:

  • DeepSpeed ZeRO-3 handling when resizing embedding layers (#26259)
  • [doc] Always call it Agents for consistency (#25958)
  • deepspeed resume from ckpt fixes and adding support for deepspeed optimizer and HF scheduler (#25863)
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Patch release: v4.33.2

15 Sep 20:24
@LysandreJik LysandreJik
v4.33.2
6da93f5
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Patch release: v4.33.2

A patch release was done for these two commits:

  • Fix pad to multiple of (#25732)
  • fix _resize_token_embeddings will set lm head size to 0 when enabled deepspeed zero3 (#26024)
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Falcon, Code Llama, ViTDet, DINO v2, VITS

06 Sep 21:14
@LysandreJik LysandreJik
v4.33.1
fa6107c
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Falcon, Code Llama, ViTDet, DINO v2, VITS

Falcon

Falcon is a class of causal decoder-only models built by TII. The largest Falcon checkpoints have been trained on >=1T tokens of text, with a particular emphasis on the RefinedWeb corpus. They are made available under the Apache 2.0 license.

Falcon’s architecture is modern and optimized for inference, with multi-query attention and support for efficient attention variants like FlashAttention. Both ‘base’ models trained only as causal language models as well as ‘instruct’ models that have received further fine-tuning are available.

Code Llama

Code Llama, is a family of large language models for code based on Llama 2, providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks.

ViTDet

ViTDet reuses the ViT model architecture, adapted to object detection.

DINO v2

DINO v2 is the next iteration of the DINO model. It is added as a backbone class, allowing it to be re-used in downstream models.

VITS

VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.

Breaking changes:

  • 🚨🚨🚨 [Refactor] Move third-party related utility files into integrations/ folder 🚨🚨🚨 by @younesbelkada in #25599

Moves all third party libs (outside HF ecosystem) related utility files inside integrations/ instead of having them in transformers directly.

In order to get the previous usage you should be changing your call to the following:

- from transformers.deepspeed import HfDeepSpeedConfig
+ from transformers.integrations import HfDeepSpeedConfig

Bugfixes and improvements

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tmm1, jpizarrom, and 51 other contributors
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Patch release: v4.32.1

28 Aug 12:48
@LysandreJik LysandreJik
v4.32.1
ccb92be
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Patch release: v4.32.1

Patch release including several patches from v4.31.0, listed below:

  • Put IDEFICS in the right section of the doc (#25650)
  • removing unnecesssary extra parameter (#25643)
  • [SPM] Patch spm Llama and T5 (#25656)
  • Fix bloom add prefix space (#25652)
  • Generate: add missing logits processors docs (#25653)
  • [idefics] small fixes (#25764)
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IDEFICS, GPTQ Quantization

22 Aug 13:11
@LysandreJik LysandreJik
v4.32.0
41aef33
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sgugger Sylvain Gugger
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IDEFICS, GPTQ Quantization

IDEFICS

The IDEFICS model was proposed in OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh

IDEFICS is the first open state-of-the-art visual language model at the 80B scale!

The model accepts arbitrary sequences of image and text and produces text, similarly to a multimodal ChatGPT.

Blogpost: hf.co/blog/idefics
Playground: HuggingFaceM4/idefics_playground

image

MPT

MPT has been added and is now officially supported within Transformers. The repositories from MosaicML have been updated to work best with the model integration within Transformers.

GPTQ Integration

GPTQ quantization is now supported in Transformers, through the optimum library. The backend relies on the auto_gptq library, from which we use the GPTQ and QuantLinear classes.

See below for an example of the API, quantizing a model using the new GPTQConfig configuration utility.

from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_name = "facebook/opt-125m"

tokenizer = AutoTokenizer.from_pretrained(model_name)
config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer,  group_size=128, desc_act=False)
# works also with device_map (cpu offload works but not disk offload)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, quantization_config=config)

Most models under TheBloke namespace with the suffix GPTQ should be supported, for example, to load a GPTQ quantized model on TheBloke/Llama-2-13B-chat-GPTQ simply run (after installing latest optimum and auto-gptq libraries):

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "TheBloke/Llama-2-13B-chat-GPTQ"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

For more information about this feature, we recommend taking a look at the following announcement blogpost: https://huggingface.co/blog/gptq-integration

Pipelines

A new pipeline, dedicated to text-to-audio and text-to-speech models, has been added to Transformers. It currently supports the 3 text-to-audio models integrated into transformers: SpeechT5ForTextToSpeech, MusicGen and Bark.

See below for an example:

from transformers import pipeline

classifier = pipeline(model="suno/bark")
output = pipeline("Hey it's HuggingFace on the phone!")

audio = output["audio"]
sampling_rate = output["sampling_rate"]

Classifier-Free Guidance decoding

Classifier-Free Guidance decoding is a text generation technique developed by EleutherAI, announced in this paper. With this technique, you can increase prompt adherence in generation. You can also set it up with negative prompts, ensuring your generation doesn't go in specific directions. See its docs for usage instructions.

Task guides

A new task guide going into Visual Question Answering has been added to Transformers.

Model deprecation

We continue the deprecation of models that was introduced in #24787.

By deprecating, we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub, but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models. The usage of the models drives this choice and aims to ease the burden on our CI so that it may be used to focus on more critical aspects of the library.

Translation Efforts

There are ongoing efforts to translate the transformers' documentation in other languages. These efforts are driven by groups independent to Hugging Face, and their work is greatly appreciated further to lower the barrier of entry to ML and Transformers.

If you'd like to kickstart such an effort or help out on an existing one, please feel free to reach out by opening an issue.

Explicit input data format for image processing

Addition of input_data_format argument to image transforms and ImageProcessor methods, allowing the user to explicitly set the data format of the images being processed. This enables processing of images with non-standard number of channels e.g. 4 or removes error which occur when the data format was inferred but the channel dimension was ambiguous.

import numpy as np
from transformers import ViTImageProcessor

img = np.random.randint(0, 256, (4, 6, 3))
image_processor = ViTImageProcessor()
inputs = image_processor(img, image_mean=0, image_std=1, input_data_format="channels_first")

Documentation clarification about efficient inference through torch.scaled_dot_product_attention & Flash Attention

Users are not aware that it is possible to force dispatch torch.scaled_dot_product_attention method from torch to use Flash Attention kernels. This leads to considerable speedup and memory saving, and is also compatible with quantized models. We decided to make this explicit to users in the documentation.

  • [Docs / BetterTransformer ] Added more details about flash attention + SDPA : #25265

In a nutshell, one can just run:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m").to("cuda")

# convert the model to BetterTransformer
model.to_bettertransformer()

input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")

+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
    outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

to enable Flash-attenion in their model. However, this feature does not support padding yet.

FSDP and DeepSpeed Changes

Users will no longer encounter CPU RAM OOM when using FSDP to train very large models in multi-gpu or multi-node multi-gpu setting.
Users no longer have to pass fsdp_transformer_layer_cls_to_wrap as the code now use _no_split_modules by default which is available for most of the popular models. DeepSpeed Z3 init now works properly with Accelerate Launcher + Trainer.

Breaking changes

Default optimizer in the Trainer class

The defaul...

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tmc, kashif, and 100 other contributors
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v4.31.0: Llama v2, MusicGen, Bark, MMS, EnCodec, InstructBLIP, Umt5, MRa, vIvIt

18 Jul 20:16
@LysandreJik LysandreJik
v4.31.0
e42587f
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sgugger Sylvain Gugger
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v4.31.0: Llama v2, MusicGen, Bark, MMS, EnCodec, InstructBLIP, Umt5, MRa, vIvIt

New models

Llama v2

Llama 2 was proposed in LLaMA: Open Foundation and Fine-Tuned Chat Models by Hugo Touvron et al. It builds upon the Llama architecture adding Grouped Query Attention for efficient inference.

Musicgen

The MusicGen model was proposed in the paper Simple and Controllable Music Generation by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.

MusicGen is a single stage auto-regressive Transformer model capable of generating high-quality music samples conditioned on text descriptions or audio prompts. The text descriptions are passed through a frozen text encoder model to obtain a sequence of hidden-state representations. MusicGen is then trained to predict discrete audio tokens, or audio codes, conditioned on these hidden-states. These audio tokens are then decoded using an audio compression model, such as EnCodec, to recover the audio waveform.

Through an efficient token interleaving pattern, MusicGen does not require a self-supervised semantic representation of the text/audio prompts, thus eliminating the need to cascade multiple models to predict a set of codebooks (e.g. hierarchically or upsampling). Instead, it is able to generate all the codebooks in a single forward pass.

Bark

Bark is a transformer-based text-to-speech model proposed by Suno AI in suno-ai/bark.

MMS

The MMS model was proposed in Scaling Speech Technology to 1,000+ Languages by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli

EnCodec

The EnCodec neural codec model was proposed in High Fidelity Neural Audio Compression by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.

InstructBLIP

The InstructBLIP model was proposed in InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. InstructBLIP leverages the BLIP-2 architecture for visual instruction tuning.

Umt5

The UMT5 model was proposed in UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.

MRA

The MRA model was proposed in Multi Resolution Analysis (MRA) for Approximate Self-Attention by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, and Vikas Singh.

ViViT

The Vivit model was proposed in ViViT: A Video Vision Transformer by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid. The paper proposes one of the first successful pure-transformer based set of models for video understanding.

Python 3.7

The last version to support Python 3.7 was 4.30.x, as it reached end-of-life on June 27, 2023 and is no longer supported by the Python Software Foundation.

PyTorch 1.9

The last version to support PyTorch 1.9 was 4.30.x. As it has been more than 2 years, and we're looking forward to using features available in PyTorch 1.10 and up, we do not support PyTorch 1.9 for v4.31 and up.

RoPE scaling

This PR adds RoPE scaling to the LLaMa and GPTNeoX families of models. It allows us to extrapolate and go beyond the original maximum sequence length (e.g. 2048 tokens on LLaMA), without fine-tuning. It offers two strategies:

  • Linear scaling
  • Dynamic NTK scaling

Agents

Tools now return a type that is specific to agents. This type can return a serialized version of itself (a string), that either points to a file on-disk or to the object's content. This should make interaction with text-based systems much simpler.

Tied weights load

Models with potentially tied weights dropped off some keys from the state dict even when the weights were not tied. This has now been fixed and more generally, the whole experience of loading a model with state dict that don't match exactly should be improved in this release.

Whisper word-level timestamps

This PR adds a method of predicting timestamps at the word (or even token) level, by analyzing the cross-attentions and applying dynamic time warping.

Auto model addition

A new auto model is added, AutoModelForTextEncoding. It is to be used when you want to extract the text encoder from an encoder-decoder architecture.

Model deprecation

Transformers is growing a lot and to ease a bit the burden of maintenance on our side, we have taken the decision to deprecate models that are not used a lot. Those models will never actually disappear from the library, but we will stop testing them or accepting PRs modifying them.
(enfin ça
The criteria to identify models to deprecate was less than 1,000 unique downloads in the last 30 days for models that are at least one year old. The list of deprecated models is:

  • BORT
  • M-CTC-T
  • MMBT
  • RetriBERT
  • TAPEX
  • Trajectory Transformer
  • VAN

Breaking changes

Fixes an issue with stripped spaces for the T5 family tokenizers. If this impacts negatively inference/training with your models, please let us know by opening an issue.

Bugfixes and improvements

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kashif, lig, and 105 other contributors
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