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v4.47.0: PaliGemma-2, I-JEPA, OLMo-2, LayerSkip, Tensor Parallel

05 Dec 17:45
@LysandreJik LysandreJik
v4.47.0
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v4.47.0: PaliGemma-2, I-JEPA, OLMo-2, LayerSkip, Tensor Parallel Latest
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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:

  • RMSNorm is used instead of standard layer norm.
  • Norm is applied to attention queries and keys.
  • Norm is applied after attention/feedforward layers rather than before.

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.

  • Simplify Tensor Parallel implementation with PyTorch TP by @kwen2501 in #34184

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:

  • unbatched images and text - images=image, text=text
  • batched images and text - images = [image, image], text= [text, text]
  • several images per prompt (only for models supporting the use of an image token) - images = [[image, image], [image]] or images=[image, image, image], text = ["... ......", "......"]
  • Chat templates (for models supporting them).

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:

  • Impossible to review diffs
  • Very hard to edit in the web UI (or in general)
  • Differences between processor templates in chat_template.json and tokenizer templates in tokenizer_config.json causing confusion
  • Some models use multiple templates, requiring a template dict, but we're trying to discourage that in future and move those models to single templates with conditional behaviour instead

The solution:

  • Just move chat templates to a single chat_template.jinja file in the repo
  • If multiple templates are required, then they should still be stored in the JSON file. This is not supported for Processor classes, so processors should always be able to save their template as a raw Jinja file. In general, we'll be gently deprecating multiple templates in future.
  • If a chat_template.jinja file is present, it overrides the JSON files. If a tokenizer is loaded with both Jinja and JSON chat templates and resaved, it should save only the Jinja file, and not have any chat_template entry in tokenizer_config.json.

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:

  • visit all the modular file (record imports/functions/classes/assignments nodes)
    • create function dependency mapping
  • for each import coming from another model:
    • visit the corresponding file
    • create function dependency mapping
    • update mapping with function/assignment from the modular (updated/new functions)
    • create the class dependency graph based on merged dependencies
  • update dependency graph of the modular with the functions and assignments imported from the other files
  • for each class recorded in the modular:
    • if inherithing from class in another file:
      • replace call to super
      • find the dependencies after the node was replaced
      • follow (updated with modular defs) dependency mapping to add all nodes
    • else:
      • only add needed imported functions (and their dependencies)
  • determine the needed imports and add them

Community bugfixes and improvements

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winglian, pcuenca, and 125 other contributors
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Patch release v4.46.3

18 Nov 22:13
@ArthurZucker ArthurZucker
v4.46.3
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Patch release v4.46.3

One small fix for FSDP + gradient accumulation loss issue!

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winglian
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Patch release v4.46.2

05 Nov 18:21
@ArthurZucker ArthurZucker
v4.46.2
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Patch release v4.46.2

Patch release v4.46.2

Mostly had to finish the gradient accumulation !
Thanks to @techkang and @Ryukijano 🤗

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muellerzr, gante, and 4 other contributors
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Patch release v4.46.1

29 Oct 15:50
@ArthurZucker ArthurZucker
v4.46.1
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Patch release v4.46.1

Patch release v4.4.61

This is mostly for fx and onnx issues!

** Fix regression loading dtype #34409 by @SunMarc
** LLaVa: latency issues #34460 by @zucchini-nlp
** Fix pix2struct #34374 by @IlyasMoutawwakil
** Fix onnx non-exposable inplace aten op #34376 by @IlyasMoutawwakil
** Fix torch.fx issue related to the new loss_kwargs keyword argument #34380 by @michaelbenayoun

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michaelbenayoun, SunMarc, and 2 other contributors
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Release v4.46.0

24 Oct 08:15
@LysandreJik LysandreJik
v4.46.0
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Release v4.46.0

New model additions

Moshi

The Moshi model was proposed in Moshi: a speech-text foundation model for real-time dialogue by Alexandre Défossez,
Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave and Neil Zeghidour.

Moshi is a speech-text foundation model that casts spoken dialogue as speech-to-speech generation. Starting from a
text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec,
while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of
explicit speaker turns, and the modeling of arbitrary conversational dynamics. Moshi also predicts time-aligned text
tokens as a prefix to audio tokens. This “Inner Monologue” method significantly improves the linguistic quality of
generated speech and provides streaming speech recognition and text-to-speech. As a result, Moshi is the first
real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice.

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Zamba

Zamba-7B-v1 is a hybrid between state-space models (Specifically Mamba) and transformer, and was trained using
next-token prediction. Zamba uses a shared transformer layer after every 6 mamba blocks. It uses the Mistral
v0.1 tokenizer. We came to this architecture after a series of ablations at small scales. Zamba-7B-v1 was
pre-trained on 1T tokens of text and code data.

zamba

GLM

The GLM Model was proposed in ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools by GLM Team,
THUDM & ZhipuAI.

The abstract from the paper starts with the following:

We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This
report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B.

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Idefics 3

The Idefics3 model was proposed in Building and better understanding vision-language models: insights and future directions by Hugo Laurençon, Andrés Marafioti, Victor Sanh, and Léo Tronchon.

Idefics3 is an adaptation of the Idefics2 model with three main differences:

  • It uses Llama3 for the text model.
  • It uses an updated processing logic for the images.
  • It removes the perceiver.

image

PhiMoE

The PhiMoE model was proposed in Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone by Microsoft.

This model is very similar to Mixtral with the main difference of Phi3LongRoPEScaledRotaryEmbedding, where they are
used to extend the context of the rotary embeddings. The query, key and values are fused, and the MLP’s up and gate
projection layers are also fused.

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Watermarking

This release adds SynthID, a novel state-of-the-art watermarking technique by Google DeepMind. SynthID has a low generation-time computational cost and can be configured to be nearly imperceptible (at the cost of harder watermarking detection). The release also comes with the code to train and run the corresponding detector, which is a machine learning model itself.

from transformers import AutoModelForCausalLM, AutoTokenizer, SynthIDTextWatermarkingConfig

tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b', padding_side="left")
model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b')

# SynthID Text configuration
watermarking_config = SynthIDTextWatermarkingConfig(
    keys=[654, 400, 836, 123, 340, 443, 597, 160, 57],
    ngram_len=5,
)

# Generation with watermarking
tokenized_prompts = tokenizer(["Once upon a time, "], return_tensors="pt", padding=True)
output_sequences = model.generate(
    **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True, max_new_tokens=10
)
watermarked_text = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
print(watermarked_text)

Docs for applying SynthID watermarking: https://huggingface.co/docs/transformers/internal/generation_utils#transformers.SynthIDTextWatermarkLogitsProcessor
Docs for detecting SynthID watermarking: https://huggingface.co/docs/transformers/internal/generation_utils#transformers.SynthIDTextWatermarkDetector

how-synthid-works-high-level
  • Add SynthID (watermerking by Google DeepMind) by @gante in #34350

Quantization

BitNet

BitNet is an architecture introduced by Microsoft Research that uses extreme quantization, representing each parameter with only three values: -1, 0, and 1. This results in a model that uses just 1.58 bits per parameter, significantly reducing computational and memory requirements. It replaces traditional Linear layers in Multi-Head Attention and Feed-Forward Networks with specialized layers called BitLinears that use ternary precision (or even binary, in the initial version)
image

  • FEAT : Adding BitNet quantization method to HFQuantizer by @MekkCyber in #33410

GGUF loading in transformers

More architectures are now supported in our GGUF loader; GGUF files saved with this architecture can now
be loaded directly in transformers to be fine-tuned. We recommend using tooling from llama.cpp to requantize
the models after further training has been done.

Notable improvements and additions

Pipeline API synchronisation

We are pushing for a unified inference API across multiple libraries. As part of this, we are cleaning up the input and output signatures for our pipeline classes and deprecating some rarely-used arguments. This is still a work-in-progress, but when it's finished, transformers pipelines should exactly match workflows in deployment libraries like transformers.js or TGI, allowing you to seamlessly move from development to production.

Also, pipelines now fully support the Processor class, used by vision-language models. Expect full pipeline support for chatting with VLMs in the very near future!

Executorch compatibility

ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch ecosystem and supports the deployment of PyTorch models with a focus on portability, productivity, and performance.

We are collaborating with the executorch team so that 🤗 Transformers models can be exported using torch.export. The goal of this integration is not only to enable export but also to ensure that the exported artifact can be further lowered and optimized to run efficiently in ExecuTorch, particularly for mobile and edge use cases.

how-executorch-works-high-level

Gradient accumulation bugfix

  • Fix Gradient Accumulation issue by @ArthurZucker in #34191
  • Enable users to use their own loss functions + deal with prefetching for grad accum by @muellerzr in #34198
  • Enable Gradient Accumulation fix across all models + trainer fully in forward() by @muellerzr #34283

Bugfixes and improvements

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jbn, chanind, and 118 other contributors
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Release v4.45.2

07 Oct 17:42
@ArthurZucker ArthurZucker
v4.45.2
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Release v4.45.2

Patch release v4.45.2

Mostly some warnings that were not properly removed ⚠️ :

🔴 Had a small regression with dynamic Cache 🔴
*Cache: revert DynamicCache init for BC #33861 by @gante

A small fix for idefic 🐩 :

And a fix for Siglip 🤧 !

  • hot fix self.position_embeddings->self.position_embedding #33958 and properly fix and RUN_SLOW #33965 thanks to @mranzinger

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mranzinger, gante, and 3 other contributors
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Patch Release v4.45.1

26 Sep 18:07
@ArthurZucker ArthurZucker
v4.45.1
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Patch Release v4.45.1

Patches for v4.45.1

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gante, itazap, and ArthurZucker
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Llama 3.2, mllama, Qwen2-Audio, Qwen2-VL, OLMoE, Llava Onevision, Pixtral, FalconMamba, Modular Transformers

25 Sep 18:11
@LysandreJik LysandreJik
v4.45.0
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Llama 3.2, mllama, Qwen2-Audio, Qwen2-VL, OLMoE, Llava Onevision, Pixtral, FalconMamba, Modular Transformers

New model additions

mllama

The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of pretrained and instruction-tuned image reasoning generative models in 11B and 90B sizes (text + images in / text out). The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The models outperform many of the available open source and closed multimodal models on common industry benchmarks.

image

Qwen2-VL

The Qwen2-VL is a major update from the previous Qwen-VL by the Qwen team.

An extract from the Qwen2-VL blogpost available here is as follows:

Qwen2-VL is the latest version of the vision language models based on Qwen2 in the Qwen model familities. Compared with Qwen-VL, Qwen2-VL has the capabilities of:

  • SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.
  • Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.
  • Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions.
  • Multilingual Support: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.

image

Qwen2-Audio

The Qwen2-Audio is the new model series of large audio-language models from the Qwen team. Qwen2-Audio is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions.

They introduce two distinct audio interaction modes:

  • voice chat: users can freely engage in voice interactions with Qwen2-Audio without text input
  • audio analysis: users could provide audio and text instructions for analysis during the interaction

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OLMoE

OLMoE is a series of Open Language Models using sparse Mixture-of-Experts designed to enable the science of language models. The team releases all code, checkpoints, logs, and details involved in training these models.

image

Llava Onevision

LLaVA-Onevision is a Vision-Language Model that can generate text conditioned on one or several images/videos. The model consists of SigLIP vision encoder and a Qwen2 language backbone. The images are processed with anyres-9 technique where the image is split into 9 patches to better process high resolution images and capture as much details as possible. However, videos are pooled to a total sequence length of 196 tokens each frame for more memory efficient computation. LLaVA-Onevision is available in three sizes: 0.5B, 7B and 72B and achieves remarkable performance on benchmark evaluations.

image

FalconMamba

The FalconMamba model was proposed by TII UAE (Technology Innovation Institute) in their release.

The model has been trained on approximtely 6T tokens consisting a mixture of many data sources such as RefineWeb, Cosmopedia and Math data.

The team releases an accompanying blog post.

image

Granite Language Models

he Granite model was proposed in Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler by Yikang Shen, Matthew Stallone, Mayank Mishra, Gaoyuan Zhang, Shawn Tan, Aditya Prasad, Adriana Meza Soria, David D. Cox and Rameswar Panda.

PowerLM-3B is a 3B state-of-the-art small language model trained with the Power learning rate scheduler. It is trained on a wide range of open-source and synthetic datasets with permissive licenses. PowerLM-3B has shown promising results compared to other models in the size categories across various benchmarks, including natural language multi-choices, code generation, and math reasoning.

image

Granite MOE

The GraniteMoe model was proposed in Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler by Yikang Shen, Matthew Stallone, Mayank Mishra, Gaoyuan Zhang, Shawn Tan, Aditya Prasad, Adriana Meza Soria, David D. Cox and Rameswar Panda.

PowerMoE-3B is a 3B sparse Mixture-of-Experts (sMoE) language model trained with the Power learning rate scheduler. It sparsely activates 800M parameters for each token. It is trained on a mix of open-source and proprietary datasets. PowerMoE-3B has shown promising results compared to other dense models with 2x activate parameters across various benchmarks, including natural language multi-choices, code generation, and math reasoning.

Descript-Audio-Codec

The Descript Audio Codec (DAC) model is a powerful tool for compressing audio data, making it highly efficient for storage and transmission. By compressing 44.1 KHz audio into tokens at just 8kbps bandwidth, the DAC model enables high-quality audio processing while significantly reducing the data footprint. This is particularly useful in scenarios where bandwidth is limited or storage space is at a premium, such as in streaming applications, remote conferencing, and archiving large audio datasets.

image

Pixtral

The Pixtral model was released by the Mistral AI team. Pixtral is a multimodal model, taking images and text as input, and producing text as output. This model follows the Llava family, meaning image embeddings are placed instead of the [IMG] token placeholders.

The model uses PixtralVisionModel for its vision encoder, and MistralForCausalLM for its language decoder. The main contribution is the 2d ROPE (rotary postiion embeddings) on the images, and support for arbitrary image sizes (the images are not padded together nor are they resized).

Mimi

The Mimi model was proposed in Moshi: a speech-text foundation model for real-time dialogue by Alexandre Défossez, Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave and Neil Zeghidour. Mimi is a high-fidelity audio codec model developed by the Kyutai team, that combines semantic and acoustic information into audio tokens running at 12Hz and a bitrate of 1.1kbps. In other words, it can be used to map audio waveforms into “audio tokens”, known as “codebooks”.

image

OmDet-Turbo

The OmDet-Turbo model was proposed in Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head by Tiancheng Zhao, Peng Liu, Xuan He, Lu Zhang, Kyusong Lee. OmDet-Turbo incorporates components from RT-DETR and introduces a swift multimodal fusion module to achieve real-time open-vocabulary object detection capabilities while maintaining high accuracy. The base model achieves performance of up to 100.2 FPS and 53.4 AP on COCO zero-shot.

image

Quantization

GGUF

GGUF support continues to be enhanced in the library by offering a way to load GGUF models within transformers by unquantizing them, before re-quantizing them for re-use within the GGUF/GGML ecosystem.

Torch AO

An ongoing effort is to add the ability to use torchao as a quantization backend. Future PRs will enable saving and fine-tuning with peft.

Liger Kernel

The Liger kernel is now supported in the Trainer class.

  • Integrate Liger (Linkedin GPU Efficient Runtime) Kernel to Trainer by @JasonZhu1313 in #32860

Modular Transformers

This PR i...

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jpizarrom, ankane, and 167 other contributors
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Release v4.44.2

22 Aug 16:56
@ArthurZucker ArthurZucker
v4.44.2
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Release v4.44.2

Patch release v4.44.2, mostly 2 regressions that were not caught for Jamba and for processors!

  • Fix: Jamba cache fails to use torch.nn.module (#32894) Authored by @xgal
  • Fix: No need to dtype A in Jamba (#32924) @xgal
  • Fix: Regression on Processor.save_pretrained caused by #31691 (#32921) Authored by @leloykun

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xgal and leloykun
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Patch release v4.44.1

20 Aug 17:51
@ArthurZucker ArthurZucker
v4.44.1
ca56cd7
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Patch release v4.44.1

Here are the different fixes, mostly Gemma2 context length, nits here and there, and generation issues

Full Changelog: v4.44.0...v4.44.1

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muellerzr, gante, and 7 other contributors
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