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Patch release v4.39.2

28 Mar 17:36
@amyeroberts amyeroberts
v4.39.2
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Patch release v4.39.2

Series of fixes for backwards compatibility (AutoAWQ and other quantization libraries, imports from trainer_pt_utils) and functionality (LLaMA tokenizer conversion)

  • Safe import of LRScheduler #29919
  • [BC] Fix BC for other libraries #29934
  • [LlamaSlowConverter] Slow to Fast better support #29797
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Patch release v4.39.1

22 Mar 17:01
@amyeroberts amyeroberts
v4.39.1
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Patch release v4.39.1

Patch release to fix some breaking changes to LLaVA model, fixes/cleanup for Cohere & Gemma and broken doctest

  • Correct llava mask & fix missing setter for vocab_size #29389
  • [cleanup] vestiges of causal mask #29806
  • [SuperPoint] Fix doc example (#29816)
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Release v4.39.0

21 Mar 01:18
@ArthurZucker ArthurZucker
v4.39.0
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Release v4.39.0

v4.39.0

🚨 VRAM consumption 🚨

The Llama, Cohere and the Gemma model both no longer cache the triangular causal mask unless static cache is used. This was reverted by #29753, which fixes the BC issues w.r.t speed , and memory consumption, while still supporting compile and static cache. Small note, fx is not supported for both models, a patch will be brought very soon!

New model addition

Cohere open-source model

Command-R is a generative model optimized for long context tasks such as retrieval augmented generation (RAG) and using external APIs and tools. It is designed to work in concert with Cohere's industry-leading Embed and Rerank models to provide best-in-class integration for RAG applications and excel at enterprise use cases. As a model built for companies to implement at scale, Command-R boasts:

  • Strong accuracy on RAG and Tool Use
  • Low latency, and high throughput
  • Longer 128k context and lower pricing
  • Strong capabilities across 10 key languages
  • Model weights available on HuggingFace for research and evaluation

LLaVA-NeXT (llava v1.6)

Llava next is the next version of Llava, which includes better support for non padded images, improved reasoning, OCR, and world knowledge. LLaVA-NeXT even exceeds Gemini Pro on several benchmarks.

Compared with LLaVA-1.5, LLaVA-NeXT has several improvements:

  • Increasing the input image resolution to 4x more pixels. This allows it to grasp more visual details. It supports three aspect ratios, up to 672x672, 336x1344, 1344x336 resolution.
  • Better visual reasoning and OCR capability with an improved visual instruction tuning data mixture.
  • Better visual conversation for more scenarios, covering different applications.
  • Better world knowledge and logical reasoning.
  • Along with performance improvements, LLaVA-NeXT maintains the minimalist design and data efficiency of LLaVA-1.5. It re-uses the pretrained connector of LLaVA-1.5, and still uses less than 1M visual instruction tuning samples. The largest 34B variant finishes training in ~1 day with 32 A100s.*

drawing

LLaVa-NeXT incorporates a higher input resolution by encoding various patches of the input image. Taken from the original paper.

MusicGen Melody

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

MusicGen Melody 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.

PvT-v2

The PVTv2 model was proposed in PVT v2: Improved Baselines with Pyramid Vision Transformer by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. As an improved variant of PVT, it eschews position embeddings, relying instead on positional information encoded through zero-padding and overlapping patch embeddings. This lack of reliance on position embeddings simplifies the architecture, and enables running inference at any resolution without needing to interpolate them.

UDOP

The UDOP model was proposed in Unifying Vision, Text, and Layout for Universal Document Processing by Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal. UDOP adopts an encoder-decoder Transformer architecture based on T5 for document AI tasks like document image classification, document parsing and document visual question answering.

drawing

UDOP architecture. Taken from the original paper.

Mamba

This model is a new paradigm architecture based on state-space-models, rather than attention like transformer models.
The checkpoints are compatible with the original ones

StarCoder2

StarCoder2 is a family of open LLMs for code and comes in 3 different sizes with 3B, 7B and 15B parameters. The flagship StarCoder2-15B model is trained on over 4 trillion tokens and 600+ programming languages from The Stack v2. All models use Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and were trained using the Fill-in-the-Middle objective.

SegGPT

The SegGPT model was proposed in SegGPT: Segmenting Everything In Context by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. SegGPT employs a decoder-only Transformer that can generate a segmentation mask given an input image, a prompt image and its corresponding prompt mask. The model achieves remarkable one-shot results with 56.1 mIoU on COCO-20 and 85.6 mIoU on FSS-1000.

Galore optimizer

image

With Galore, you can pre-train large models on consumer-type hardwares, making LLM pre-training much more accessible to anyone from the community.

Our approach reduces memory usage by up to 65.5% in optimizer states while maintaining both efficiency and performance for pre-training on LLaMA 1B and 7B architectures with C4 dataset with up to 19.7B tokens, and on fine-tuning RoBERTa on GLUE tasks. Our 8-bit GaLore further reduces optimizer memory by up to 82.5% and total training memory by 63.3%, compared to a BF16 baseline. Notably, we demonstrate, for the first time, the feasibility of pre-training a 7B model on consumer GPUs with 24GB memory (e.g., NVIDIA RTX 4090) without model parallel, checkpointing, or offloading strategies.

Galore is based on low rank approximation of the gradients and can be used out of the box for any model.

Below is a simple snippet that demonstrates how to pre-train mistralai/Mistral-7B-v0.1 on imdb:

import torch
import datasets
from transformers import TrainingArguments, AutoConfig, AutoTokenizer, AutoModelForCausalLM
import trl

train_dataset = datasets.load_dataset('imdb', split='train')

args = TrainingArguments(
    output_dir="./test-galore",
    max_steps=100,
    per_device_train_batch_size=2,
    optim="galore_adamw",
    optim_target_modules=["attn", "mlp"]
)

model_id = "mistralai/Mistral-7B-v0.1"

config = AutoConfig.from_pretrained(model_id)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_config(config).to(0)

trainer = trl.SFTTrainer(
    model=model, 
    args=args,
    train_dataset=train_dataset,
    dataset_text_field='text',
    max_seq_length=512,
)

trainer.train()

Quantization

Quanto integration

Quanto has been integrated with transformers ! You can apply simple quantization algorithms with few lines of code with tiny changes. Quanto is also compatible with torch.compile

Check out the announcement blogpost for more details

Exllama 🤝 AWQ

Exllama and AWQ combined together for faster AWQ inference - check out the relevant documentation section for more details on how to use Exllama + AWQ.

MLX Support

Allow models saved or fine-tuned with Apple’s MLX framework to be loaded in transformers (as long as the model parameters use the same names), and improve tensor interoperability. This leverages MLX's adoption of safetensors as their checkpoint format.

Highligted improvements

Notable memory reduction in Gemma/LLaMa by changing the causal mask buffer type from int64 to boolean.

  • Use torch.bool instead of torch.int64 for non-persistant causal mask buffer by @fxmarty in #29241

Remote code improvements

  • Allow remote code repo names to contain "." by @Rocketknight1 in #29175
  • simplify get_class_in_m...
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Contributors

tleyden, bmuskalla, and 94 other contributors
Assets 2

v4.38.2

01 Mar 03:24
@ArthurZucker ArthurZucker
v4.38.2
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v4.38.2

Fix backward compatibility issues with Llama and Gemma:

We mostly made sure that performances are not affected by the new change of paradigm with ROPE. Fixed the ROPE computation (should always be in float32) and the causal_mask dtype was set to bool to take less RAM.

YOLOS had a regression, and Llama / T5Tokenizer had a warning popping for random reasons

  • FIX [Gemma] Fix bad rebase with transformers main (#29170)
  • Improve _update_causal_mask performance (#29210)
  • [T5 and Llama Tokenizer] remove warning (#29346)
  • [Llama ROPE] Fix torch export but also slow downs in forward (#29198)
  • RoPE loses precision for Llama / Gemma + Gemma logits.float() (#29285)
  • Patch YOLOS and others (#29353)
  • Use torch.bool instead of torch.int64 for non-persistant causal mask buffer (#29241)
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v4.38.1

22 Feb 00:24
@ArthurZucker ArthurZucker
v4.38.1
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v4.38.1

Fix eager attention in Gemma!

TLDR:

-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+        attn_output = attn_output.view(bsz, q_len, -1)

Contributors

sanchit-gandhi
Assets 2

v4.38: Gemma, Depth Anything, Stable LM; Static Cache, HF Quantizer, AQLM

21 Feb 13:40
@LysandreJik LysandreJik
v4.38.0
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v4.38: Gemma, Depth Anything, Stable LM; Static Cache, HF Quantizer, AQLM

New model additions

💎 Gemma 💎

Gemma is a new opensource Language Model series from Google AI that comes with a 2B and 7B variant. The release comes with the pre-trained and instruction fine-tuned versions and you can use them via AutoModelForCausalLM, GemmaForCausalLM or pipeline interface!

Read more about it in the Gemma release blogpost: https://hf.co/blog/gemma

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)

You can use the model with Flash Attention, SDPA, Static cache and quantization API for further optimizations !

  • Flash Attention 2
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")

model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2b", device_map="auto", torch_dtype=torch.float16, attn_implementation="flash_attention_2"
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
  • bitsandbytes-4bit
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")

model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2b", device_map="auto", load_in_4bit=True
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
  • Static Cache
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")

model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2b", device_map="auto"
)

model.generation_config.cache_implementation = "static"

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)

Depth Anything Model

The Depth Anything model was proposed in Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao. Depth Anything is based on the DPT architecture, trained on ~62 million images, obtaining state-of-the-art results for both relative and absolute depth estimation.

image

Stable LM

StableLM 3B 4E1T was proposed in StableLM 3B 4E1T: Technical Report by Stability AI and is the first model in a series of multi-epoch pre-trained language models.

StableLM 3B 4E1T is a decoder-only base language model pre-trained on 1 trillion tokens of diverse English and code datasets for four epochs. The model architecture is transformer-based with partial Rotary Position Embeddings, SwiGLU activation, LayerNorm, etc.

The team also provides StableLM Zephyr 3B, an instruction fine-tuned version of the model that can be used for chat-based applications.

⚡️ Static cache was introduced in the following PRs ⚡️

Static past key value cache allows LlamaForCausalLM' s forward pass to be compiled using torch.compile !
This means that (cuda) graphs can be used for inference, which speeds up the decoding step by 4x!
A forward pass of Llama2 7B takes around 10.5 ms to run with this on an A100! Equivalent to TGI performances! ⚡️

⚠️ Support for generate is not included yet. This feature is experimental and subject to changes in subsequent releases.

from transformers import AutoTokenizer, AutoModelForCausalLM, StaticCache
import torch
import os

# compilation triggers multiprocessing
os.environ["TOKENIZERS_PARALLELISM"] = "true"

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    device_map="auto",
    torch_dtype=torch.float16
)

# set up the static cache in advance of using the model
model._setup_cache(StaticCache, max_batch_size=1, max_cache_len=128)

# trigger compilation!
compiled_model = torch.compile(model, mode="reduce-overhead", fullgraph=True)

# run the model as usual
input_text = "A few facts about the universe: "
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda").input_ids
model_outputs = compiled_model(input_ids)

Quantization

🧼 HF Quantizer 🧼

HfQuantizer makes it easy for quantization method researchers and developers to add inference and / or quantization support in 🤗 transformers. If you are interested in adding the support for new methods, please refer to this documentation page: https://huggingface.co/docs/transformers/main/en/hf_quantizer

⚡️AQLM ⚡️

AQLM is a new quantization method that enables no-performance degradation in 2-bit precision. Check out this demo about how to run Mixtral in 2-bit on a free-tier Google Colab instance: https://huggingface.co/posts/ybelkada/434200761252287

🧼 Moving canonical repositories 🧼

The canonical repositories on the hugging face hub (models that did not have an organization, like bert-base-cased), have been moved under organizations.

You can find the entire list of models moved here: https://huggingface.co/collections/julien-c/canonical-models-65ae66e29d5b422218567567

Redirection has been set up so that your code continues working even if you continue calling the previous paths. We, however, still encourage you to update your code to use the new links so that it is entirely future proof.

Flax Improvements 🚀

The Mistral model was added to the library in Flax.

TensorFlow Improvements 🚀

With Keras 3 becoming the standard version of Keras in TensorFlow 2.16, we've made some internal changes to maintain compatibility. We now have full compatibility with TF 2.16 as long as the tf-keras compatibility package is installed. We've also taken the opportunity to do some cleanup - in particular, the objects like BatchEncoding that are returned by our tokenizers and processors can now be directly passed to Keras methods like model.fit(), which should simplify a lot of code and eliminate a long-standing source of annoyances.

Pre-Trained backbone weights 🚀

Enable loading in pretrained backbones in a new model, where all other weights are randomly initialized. Note: validation checks are still in place when creating a config. Passing in use_pretrained_backbone will raise an error. You can override by setting
config.use_pretrained_backbone = True after creating a config. However, it is not yet guaranteed to be fully backwards compatible.

from transformers import MaskFormerConfig, MaskFormerModel

config = MaskFormerConfig(
	use_pretrained_backbone=False, 
	backbone="microsoft/resnet-18"
)
config.use_pretrained_backbone = True
# Both models have resnet-18 backbone weights and all other weights randomly
# initialized 
model_1 = MaskFormerModel(config)
model_2 = MaskFormerModel(config)

Introduce a helper function load_backbone to load a backbone from a backbone's model config e.g. ResNetConfig, or from a model config which contains backbone information. This enables cleaner modeling files and crossloading between timm and transformers backbones.

from transformers import ResNetConfig, MaskFormerConfig
from transformers.utils.backbone_utils import load_backbone

# Resnet defines the backbone model to load
config = ResNetConfig()
backbone = load_backbone(config)

# Maskformer config defines a model which uses a resnet backbone
config = MaskFormerConfig(use_timm_backbone=True, backbone="resnet18")
backbone = load_backbone(config)

config = MaskFormerConfig(backbone_config=ResNetConfig())
backbone = load_backbone(config)
  • [Backbone] Use `load_backbone...
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Contributors

jganitkevitch, Narsil, and 102 other contributors
Assets 2

Patch release v4.37.2

29 Jan 16:11
@amyeroberts amyeroberts
v4.37.2
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Patch release v4.37.2

Selection of fixes

  • Protecting the imports for SigLIP's tokenizer if sentencepiece isn't installed
  • Fix permissions issue on windows machines when using trainer in multi-node setup
  • Allow disabling safe serialization when using Trainer. Needed for Neuron SDK
  • Fix error when loading processor from cache
  • torch < 1.13 compatible torch.load

Commits

  • [Siglip] protect from imports if sentencepiece not installed (#28737)
  • Fix weights_only (#28725)
  • Enable safetensors conversion from PyTorch to other frameworks without the torch requirement (#27599)
  • Don't fail when LocalEntryNotFoundError during processor_config.json loading (#28709)
  • Use save_safetensor to disable safe serialization for XLA (#28669)
  • Fix windows err with checkpoint race conditions (#28637)
  • [SigLIP] Only import tokenizer if sentencepiece available (#28636)
Assets 2

Patch release: v4.37.1

24 Jan 16:15
@amyeroberts amyeroberts
v4.37.1
d02d006
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Patch release: v4.37.1

A patch release to resolve import errors from removed custom types in generation utils

  • Add back in generation types #28681
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v4.37 Qwen2, Phi-2, SigLIP, ViP-LLaVA, Fast2SpeechConformer, 4-bit serialization, Whisper longform generation

22 Jan 11:20
@amyeroberts amyeroberts
v4.37.0
8e3e145
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v4.37 Qwen2, Phi-2, SigLIP, ViP-LLaVA, Fast2SpeechConformer, 4-bit serialization, Whisper longform generation

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

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Contributors

dbuos, julien-c, and 110 other contributors
Assets 2

Patch release: v4.36.2

18 Dec 18:44
@amyeroberts amyeroberts
v4.36.2
a7cab3c
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Patch release: v4.36.2

Patch release to resolve some critical issues relating to the recent cache refactor, flash attention refactor and training in the multi-gpu and multi-node settings:

  • Resolve training bug with PEFT + GC #28031
  • Resolve cache issue when going beyond context window for Mistral/Mixtral FA2 #28037
  • Re-enable passing config to from_pretrained with FA #28043
  • Fix resuming from checkpoint when using FDSP with FULL_STATE_DICT #27891
  • Resolve bug when saving a checkpoint in the multi-node setting #28078
Assets 2