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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

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.

image

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.

image

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.

image

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

image

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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Release v4.44.0

06 Aug 18:39
@ArthurZucker ArthurZucker
v4.44.0
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Release v4.44.0

Release v4.44.0: End to end compile generation!!! Gemma2 (with assisted decoding), Codestral (Mistral for code), Nemotron, Efficient SFT training, CPU Offloaded KVCache, torch export for static cache

This release comes a bit early in our cycle because we wanted to ship important and requested models along with improved performances for everyone!

All of these are included with examples in the awesome https://github.com/huggingface/local-gemma repository! 🎈 We tried to share examples of what is now possible with all the shipped features! Kudos to @gante, @sanchit-gandhi and @xenova

💥 End-to-end generation compile

Generate: end-to-end compilation #30788 by @gante: model.generate now supports compiling! There are a few limitations, but here is a small snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import copy

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3.1-8B", torch_dtype=torch.bfloat16, device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B")

# compile generate
compiled_generate = torch.compile(model.generate, fullgraph=True, mode="reduce-overhead")

# compiled generate does NOT accept parameterization except a) model inputs b) a generation config
generation_config = copy.deepcopy(model.generation_config)
generation_config.pad_token_id = model.config.eos_token_id

model_inputs = tokenizer(["Write a poem about the market crashing in summer"], return_tensors="pt")
model_inputs = model_inputs.to(model.device)
output_compiled = compiled_generate(**model_inputs, generation_config=generation_config)
print(output_compiled)

⚡ 3 to 5x compile speedup (compilation time 👀 not runtime)

  • 3-5x faster torch.compile forward compilation for autoregressive decoder models #32227* by @fxmarty .
    As documented on the PR, this makes the whole generation a lot faster when you re-use the cache!
    You can see this when you run model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)

🪶 Offloaded KV cache: offload the cache to CPU when you are GPU poooooor 🚀

  • Offloaded KV Cache #31325* by @n17s : you just have to set cache_implementation="offloaded" when calling from_pretrained or using this:
from transformers import GenerationConfig
gen_config = GenerationConfig(cache_implementation="offloaded", # other generation options such as num_beams=4,num_beam_groups=2,num_return_sequences=4,diversity_penalty=1.0,max_new_tokens=50,early_stopping=True)
outputs = model.generate(inputs["input_ids"],generation_config=gen_config)

📦 Torch export for static cache

pytorch team gave us a great gift: you can now use torch.export directly compatible with Executorch! Find examples here.

This also unlocks support for prompt reuse:

import os, torch, copy
from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache
device = "cuda"
ckpt = "meta-llama/Meta-Llama-3.1-8B-Instruct"

INITIAL_PROMPT = "From now on, you are going to answer all my questions with historical details. Make sure to always add a bit of french here and there, for style."

model = AutoModelForCausalLM.from_pretrained(ckpt, torch_dtype=torch.float16)
model.to(device)
tokenizer = AutoTokenizer.from_pretrained(ckpt)

prompt_cache = DynamicCache()
inputs = tokenizer(INITIAL_PROMPT, return_tensors="pt").to("cuda")
prompt_cache = model(**inputs, past_key_values = prompt_cache).past_key_values

prompt = "Why are french people obsessed with french?"
new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda")
past_key_values = copy.deepcopy(prompt_cache)
outputs = model.generate(**new_inputs, past_key_values=past_key_values,max_new_tokens=20) 
response = tokenizer.batch_decode(outputs)[0]
print(response)

prompt = "What is the best city to swim in?"
new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**new_inputs, past_key_values=copy.deepcopy(prompt_cache),max_new_tokens=20) 
response = tokenizer.batch_decode(outputs)[0]

Gemma2: assisted decoding

Gemma 2: support assisted generation #32357 by @gante

We now have a 2B Gemma 2 model -- a perfect sidekick for the 27B with assisted generation. We've enabled assisted generation in gemma 2, with a caveat: assisted generation currently requires the use of a windowless cache (as opposed to the default cache for gemma 2), so you might observe some output mismatch on long sequences. Read more about it here.

# transformers assisted generation reference: 
# https://huggingface.co/docs/transformers/main/en/llm_optims#speculative-decoding 
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# we DON’T recommend using the 9b model with the 2b model as its assistant
assistant_model_name = 'google/gemma-2-2b-it'
reference_model_name = 'google/gemma-2-27b-it'

tokenizer = AutoTokenizer.from_pretrained(reference_model_name)
model = AutoModelForCausalLM.from_pretrained(
   reference_model_name, device_map='auto', torch_dtype=torch.bfloat16
)
assistant_model = AutoModelForCausalLM.from_pretrained(
   assistant_model_name, device_map='auto', torch_dtype=torch.bfloat16
)

model_inputs = tokenizer("Einstein's theory of relativity states", return_tensors="pt").to(model.device)
generation_options = {
   "assistant_model": assistant_model,
   "do_sample": True,
   "temperature": 0.7,
   "max_new_tokens": 64,
}

outputs = model.generate(**model_inputs, **generation_options)
tokenizer.batch_decode(outputs, skip_special_tokens=True)

Nemotron support

image

Nemotron-4-340B-Instruct is a large language model (LLM) that can be used as part of a synthetic data generation pipeline to create training data that helps researchers and developers build their own LLMs. It is a fine-tuned version of the Nemotron-4-340B-Base model, optimized for English-based single and multi-turn chat use-cases. It supports a context length of 4,096 tokens.

The conversion script should be able to cover Minitron and Nemotron, thanks and kudos to @suiyoubi. See:

  • Add Nemotron HF Support #31699

Codestral support

image

Codestral is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash. It also performs well on more specific ones like Swift and Fortran. This broad language base ensures Codestral can assist developers in various coding environments and projects.

Codestral saves developers time and effort: it can complete coding functions, write tests, and complete any partial code using a fill-in-the-middle mechanism. Interacting with Codestral will help level up the developer’s coding game and reduce the risk of errors and bugs.

It's mamba2 architecture, was a bit of a pain to remove all einops but hope we made it better for everyone!

Breaking changes:

We removed the chat template in the code, they should all be on the hub!

Long-form decoding for whisper, even faster:

Our great @sanchit-gandhi worked on porting the recent compile upgrades to long form decoding in

  • [whisper] compile compatibility with long-form decoding #31772

What's Changed

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kashif, n17s, and 55 other contributors
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v4.43.4 Patch Release

05 Aug 10:57
@ArthurZucker ArthurZucker
v4.43.4
868d36d
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v4.43.4 Patch Release

Patch Release v4.43.4

There was a mick mack, now deepseep issues are properly pushed with:

  • Resize embeds with DeepSpeed #32214

🤗 Enjoy holidays

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