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v4.37 Qwen2, Phi-2, SigLIP, ViP-LLaVA, Fast2SpeechConformer, 4-bit serialization, Whisper longform generation
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@amyeroberts amyeroberts released this 22 Jan 11:20
· 2829 commits to main since this release
v4.37.0
8e3e145

Model releases

Qwen2

Qwen2 is the new model series of large language models from the Qwen team. Previously, the Qwen series was released, including Qwen-72B, Qwen-1.8B, Qwen-VL, Qwen-Audio, etc.

Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.

Phi-2

Phi-2 is a transformer language model trained by Microsoft with exceptionally strong performance for its small size of 2.7 billion parameters. It was previously available as a custom code model, but has now been fully integrated into transformers.

SigLIP

The SigLIP model was proposed in Sigmoid Loss for Language Image Pre-Training by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer. SigLIP proposes to replace the loss function used in CLIP by a simple pairwise sigmoid loss. This results in better performance in terms of zero-shot classification accuracy on ImageNet.

ViP-LLaVA

The VipLlava model was proposed in Making Large Multimodal Models Understand Arbitrary Visual Prompts by Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae Lee.

VipLlava enhances the training protocol of Llava by marking images and interact with the model using natural cues like a “red bounding box” or “pointed arrow” during training.

FastSpeech2Conformer

The FastSpeech2Conformer model was proposed with the paper Recent Developments On Espnet Toolkit Boosted By Conformer by Pengcheng Guo, Florian Boyer, Xuankai Chang, Tomoki Hayashi, Yosuke Higuchi, Hirofumi Inaguma, Naoyuki Kamo, Chenda Li, Daniel Garcia-Romero, Jiatong Shi, Jing Shi, Shinji Watanabe, Kun Wei, Wangyou Zhang, and Yuekai Zhang.

FastSpeech 2 is a non-autoregressive model for text-to-speech (TTS) synthesis, which develops upon FastSpeech, showing improvements in training speed, inference speed and voice quality. It consists of a variance adapter; duration, energy and pitch predictor and waveform and mel-spectrogram decoder.

Wav2Vec2-BERT

The Wav2Vec2-BERT model was proposed in Seamless: Multilingual Expressive and Streaming Speech Translation by the Seamless Communication team from Meta AI.

This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification.

4-bit serialization

Enables saving and loading transformers models in 4bit formats - you can now push bitsandbytes 4-bit weights on Hugging Face Hub. To save 4-bit models and push them on the hub, simply install the latest bitsandbytes package from pypi pip install -U bitsandbytes, load your model in 4-bit precision and call save_pretrained / push_to_hub. An example repo here

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "facebook/opt-125m"
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)

model.push_to_hub("ybelkada/opt-125m-bnb-4bit")

4D Attention mask

Enable passing in 4D attention masks to models that support it. This is useful for reducing memory footprint of certain generation tasks.

Improved quantization support

Ability to customise which modules are quantized and which are not.

  • [Awq] Enable the possibility to skip quantization for some target modules by @younesbelkada in #27950
  • add modules_in_block_to_quantize arg in GPTQconfig by @SunMarc in #27956

Added fused modules support

SDPA Support for LLaVa, Mixtral, Mistral

Whisper: Batched state-of-the-art long-form transcription

All decoding strategies (temperature fallback, compression/log-prob/no-speech threshold, ...) of OpenAI's long-form transcription (see: https://github.com/openai/whisper or section 4.5 in paper) have been added. Contrary to https://github.com/openai/whisper, Transformers long-form transcription is fully compatible with pure FP16 and Batching!

For more information see: #27658.

Generation: assisted generation upgrades, speculative decoding, and ngram speculation

Assisted generation was reworked to accept arbitrary sources of candidate sequences. This enabled us to smoothly integrate ngram speculation, and opens the door for new candidate generation methods. Additionally, we've added the speculative decoding strategy on top of assisted generation: when you call assisted generation with an assistant model and do_sample=True, you'll benefit from the faster speculative decoding sampling 🏎️💨

  • Generate: assisted_decoding now accepts arbitrary candidate generators by @gante in #27751
  • Generate: assisted decoding now uses generate for the assistant by @gante in #28031
  • Generate: speculative decoding by @gante in #27979
  • Generate: fix speculative decoding by @gante in #28166
  • Adding Prompt lookup decoding by @apoorvumang in #27775
  • Fix _speculative_sampling implementation by @ofirzaf in #28508

torch.load pickle protection

Adding pickle protection via weights_only=True in the torch.load calls.

Build methods for TensorFlow Models

Unlike PyTorch, TensorFlow models build their weights "lazily" after model initialization, using the shape of their inputs to figure out what their weight shapes should be. We previously needed a full forward pass through TF models to ensure that all layers received an input they could use to build their weights, but with this change we now have proper build() methods that can correctly infer shapes and build model weights. This avoids a whole range of potential issues, as well as significantly accelerating model load times.

Remove support for torch 1.10

The last version to support PyTorch 1.10 was 4.36.x. As it has been more than 2 years, and we're looking forward to using features available in PyTorch 1.11 and up, we do not support PyTorch 1.10 for v4.37 (i.e. we don't run the tests against torch 1.10).

Model tagging

You can now add custom tags into your model before pushing it on the Hub! This enables you to filter models that contain that tag on the Hub with a simple URL filter. For example if you want to filter models that have trl tag you can search: https://huggingface.co/models?other=trl&sort=created

  • [core/ FEAT] Add the possibility to push custom tags using PreTrainedModel itself by @younesbelkada in #28405 - e.g.
from transformers import AutoModelForCausalLM

model_name = "HuggingFaceM4/tiny-random-LlamaForCausalLM"
model = AutoModelForCausalLM.from_pretrained(model_name)

model.add_model_tags(["tag-test"])
model.push_to_hub("llama-tagged")

Bugfixes and improvements

Significant community contributions

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

Contributors

dbuos, julien-c, and 110 other contributors
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