huggingface/transformers v4.38.0

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

on GitHub

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.

• Add Depth Anything by @NielsRogge in #28654

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.

• Add StableLM by @jon-tow in #28810

⚡️ 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! ⚡️

• [Core generation] Adds support for static KV cache by @ArthurZucker in #27931
• [CLeanup] Revert SDPA attention changes that got in the static kv cache PR by @ArthurZucker in #29027
• Fix static generation when compiling! by @ArthurZucker in #28937
• Static Cache: load models with MQA or GQA by @gante in #28975
• Fix symbolic_trace with kv cache by @fxmarty in #28724

⚠️ 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

• HfQuantizer class for quantization-related stuff in modeling_utils.py by @poedator in #26610
• [HfQuantizer] Move it to "Developper guides" by @younesbelkada in #28768
• [HFQuantizer] Remove check_packages_compatibility logic by @younesbelkada in #28789
• [docs] HfQuantizer by @stevhliu in #28820

⚡️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

• AQLM quantizer support by @BlackSamorez in #28928
• Removed obsolete attribute setting for AQLM quantization. by @BlackSamorez in #29034

🧼 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.

• canonical repos moves by @julien-c in #28795
• Update all references to canonical models by @LysandreJik in #29001

Flax Improvements 🚀

The Mistral model was added to the library in Flax.

• Flax mistral by @kiansierra in #26943

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.

• Add tf_keras imports to prepare for Keras 3 by @Rocketknight1 in #28588
• Wrap Keras methods to support BatchEncoding by @Rocketknight1 in #28734
• Fix Keras scheduler import so it works for older versions of Keras by @Rocketknight1 in #28895

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)

• Enable instantiating model with pretrained backbone weights by @amyeroberts in #28214

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 instead of AutoBackbone.from_config by @amyeroberts in #28661
• Backbone kwargs in config by @amyeroberts in #28784

Add in API references, list supported backbones, updated examples, clarification and moving information to better reflect usage and docs

• [docs] Backbone by @stevhliu in #28739
• Improve Backbone API docs by @merveenoyan in #28666

Image Processor work 🚀

• Raise unused kwargs image processor by @molbap in #29063
• Abstract image processor arg checks by @molbap in #28843

Bugfixes and improvements 🚀

• Fix id2label assignment in run_classification.py by @jheitmann in #28590
• Add missing key to TFLayoutLM signature by @Rocketknight1 in #28640
• Avoid root logger's level being changed by @ydshieh in #28638
• Add config tip to custom model docs by @Rocketknight1 in #28601
• Fix lr_scheduler in no_trainer training scripts by @bofenghuang in #27872
• [Llava] Update convert_llava_weights_to_hf.py script by @isaac-vidas in #28617
• [GPTNeoX] Fix GPTNeoX + Flash Attention 2 issue by @younesbelkada in #28645
• Update image_processing_deformable_detr.py by @sounakdey in #28561
• [SigLIP] Only import tokenizer if sentencepiece available by @amyeroberts in #28636
• Fix phi model doc checkpoint by @amyeroberts in #28581
• get default device through PartialState().default_device as it has been officially released by @statelesshz in #27256
• integrations: fix DVCLiveCallback model logging by @dberenbaum in #28653
• Enable safetensors conversion from PyTorch to other frameworks without the torch requirement by @LysandreJik in #27599
• tensor_size - fix copy/paste error msg typo by @scruel in #28660
• Fix windows err with checkpoint race conditions by @muellerzr in #28637
• add dataloader prefetch factor in training args and trainer by @qmeeus in #28498
• Support single token decode for CodeGenTokenizer by @cmathw in #28628
• Remove deprecated eager_serving fn by @Rocketknight1 in #28665
• fix a hidden bug of GenerationConfig, now the generation_config.json can be loaded successfully by @ParadoxZW in #28604
• Update README_es.md by @vladydev3 in #28612
• Exclude the load balancing loss of padding tokens in Mixtral-8x7B by @khaimt in #28517
• Use save_safetensor to disable safe serialization for XLA by @jeffhataws in #28669
• Add back in generation types by @amyeroberts in #28681
• [docs] DeepSpeed by @stevhliu in #28542
• Improved type hinting for all attention parameters by @nakranivaibhav in #28479
• improve efficient training on CPU documentation by @faaany in #28646
• [docs] Fix doc format by @stevhliu in #28684
• [chore] Add missing space in warning by @tomaarsen in #28695
• Update question_answering.md by @yusyel in #28694
• [Vilt] align input and model dtype in the ViltPatchEmbeddings forward pass by @faaany in #28633
• [docs] Improve visualization for vertical parallelism by @petergtz in #28583
• Don't fail when LocalEntryNotFoundError during processor_config.json loading by @ydshieh in #28709
• Fix duplicate & unnecessary flash attention warnings by @fxmarty in #28557
• support PeftMixedModel signature inspect by @Facico in #28321
• fix: corrected misleading log message in save_pretrained function by @mturetskii in #28699
• [docs] Update preprocessing.md by @velaia in #28719
• Initialize _tqdm_active with hf_hub_utils.are_progress_bars_disabled(… by @ShukantPal in #28717
• Fix weights_only by @ydshieh in #28725
• Stop confusing the TF compiler with ModelOutput objects by @Rocketknight1 in #28712
• fix: suppress GatedRepoError to use cache file (fix #28558). by @scruel in #28566
• Unpin pydantic by @ydshieh in #28728
• [docs] Fix datasets in guides by @stevhliu in #28715
• [Flax] Update no init test for Flax v0.7.1 by @sanchit-gandhi in #28735
• Falcon: removed unused function by @gante in #28605
• Generate: deprecate old src imports by @gante in #28607
• [Siglip] protect from imports if sentencepiece not installed by @amyeroberts in #28737
• Add serialization logic to pytree types by @angelayi in #27871
• Fix DepthEstimationPipeline's docstring by @ydshieh in #28733
• Fix input data file extension in examples by @khipp in #28741
• [Docs] Fix Typo in English & Japanese CLIP Model Documentation (TMBD -> TMDB) by @Vinyzu in #28751
• PatchtTST and PatchTSMixer fixes by @wgifford in #28083
• Enable Gradient Checkpointing in Deformable DETR by @FoamoftheSea in #28686
• small doc update for CamemBERT by @julien-c in #28644
• Pin pytest version <8.0.0 by @amyeroberts in #28758
• Mark test_constrained_beam_search_generate as flaky by @amyeroberts in #28757
• Fix typo of Block. by @xkszltl in #28727
• [Whisper] Make tokenizer normalization public by @sanchit-gandhi in #28136
• Support saving only PEFT adapter in checkpoints when using PEFT + FSDP by @AjayP13 in #28297
• Add French translation: french README.md by @ThibaultLengagne in #28696
• Don't allow passing load_in_8bit and load_in_4bit at the same time by @osanseviero in #28266
• Move CLIP _no_split_modules to CLIPPreTrainedModel by @lz1oceani in #27841
• Use Conv1d for TDNN by @gau-nernst in #25728
• Fix transformers.utils.fx compatibility with torch<2.0 by @fxmarty in #28774
• Further pin pytest version (in a temporary way) by @ydshieh in #28780
• Task-specific pipeline init args by @amyeroberts in #28439
• Pin Torch to <2.2.0 by @Rocketknight1 in #28785
• [bnb] Fix bnb slow tests by @younesbelkada in #28788
• Prevent MLflow exception from disrupting training by @codiceSpaghetti in #28779
• don't initialize the output embeddings if we're going to tie them to input embeddings by @tom-p-reichel in #28192
• [Whisper] Refactor forced_decoder_ids & prompt ids by @patrickvonplaten in #28687
• Resolve DeepSpeed cannot resume training with PeftModel by @lh0x00 in #28746
• Wrap Keras methods to support BatchEncoding by @Rocketknight1 in #28734
• DeepSpeed: hardcode torch.arange dtype on float usage to avoid incorrect initialization by @gante in #28760
• Add artifact name in job step to maintain job / artifact correspondence by @ydshieh in #28682
• Split daily CI using 2 level matrix by @ydshieh in #28773
• [docs] Correct the statement in the docstirng of compute_transition_scores in generation/utils.py by @Ki-Seki in #28786
• Adding [T5/MT5/UMT5]ForTokenClassification by @hackyon in #28443
• Make is_torch_bf16_available_on_device more strict by @ydshieh in #28796
• Add tip on setting tokenizer attributes by @Rocketknight1 in #28764
• enable graident checkpointing in DetaObjectDetection and add tests in Swin/Donut_Swin by @SangbumChoi in #28615
• [docs] fix some bugs about parameter description by @zspo in #28806
• Add models from deit by @rajveer43 in #28302
• [Docs] Fix spelling and grammar mistakes by @khipp in #28825
• Explicitly check if token ID's are None in TFBertTokenizer constructor by @skumar951 in #28824
• Add missing None check for hf_quantizer by @jganitkevitch in #28804
• Fix issues caused by natten by @ydshieh in #28834
• fix / skip (for now) some tests before switch to torch 2.2 by @ydshieh in #28838
• Use -v for pytest on CircleCI by @ydshieh in #28840
• Reduce GPU memory usage when using FSDP+PEFT by @pacman100 in #28830
• Mark test_encoder_decoder_model_generate for vision_encoder_deocder as flaky by @amyeroberts in #28842
• Support custom scheduler in deepspeed training by @VeryLazyBoy in #26831
• [Docs] Fix bad doc: replace save with logging by @chenzizhao in #28855
• Ability to override clean_code_for_run by @w4ffl35 in #28783
• [WIP] Hard error when ignoring tensors. by @Narsil in #27484
• [Doc] update contribution guidelines by @ArthurZucker in #28858
• Correct wav2vec2-bert inputs_to_logits_ratio by @ylacombe in #28821
• Image Feature Extraction pipeline by @amyeroberts in #28216
• ClearMLCallback enhancements: support multiple runs and handle logging better by @eugen-ajechiloae-clearml in #28559
• Do not use mtime for checkpoint rotation. by @xkszltl in #28862
• Adds LlamaForQuestionAnswering class in modeling_llama.py along with AutoModel Support by @nakranivaibhav in #28777
• [Docs] Update project names and links in awesome-transformers by @khipp in #28878
• Fix LongT5ForConditionalGeneration initialization of lm_head by @eranhirs in #28873
• Raise error when using save_only_model with load_best_model_at_end for DeepSpeed/FSDP by @pacman100 in #28866
• Fix FastSpeech2ConformerModelTest and skip it on CPU by @ydshieh in #28888
• Revert "[WIP] Hard error when ignoring tensors." by @ydshieh in #28898
• unpin torch by @ydshieh in #28892
• Explicit server error on gated model by @Wauplin in #28894
• [Docs] Fix backticks in inline code and documentation links by @khipp in #28875
• Hotfix - make torchaudio get the correct version in torch_and_flax_job by @ydshieh in #28899
• [Docs] Add missing language options and fix broken links by @khipp in #28852
• fix: Fixed the documentation for logging_first_step by removing "evaluate" by @Sai-Suraj-27 in #28884
• fix Starcoder FA2 implementation by @pacman100 in #28891
• Fix Keras scheduler import so it works for older versions of Keras by @Rocketknight1 in #28895
• ⚠️ Raise Exception when trying to generate 0 tokens ⚠️ by @danielkorat in #28621
• Update the cache number by @ydshieh in #28905
• Add npu device for pipeline by @statelesshz in #28885
• [Docs] Fix placement of tilde character by @khipp in #28913
• [Docs] Revert translation of '@slow' decorator by @khipp in #28912
• Fix utf-8 yaml load for marian conversion to pytorch in Windows by @SystemPanic in #28618
• Remove dead TF loading code by @Rocketknight1 in #28926
• fix: torch.int32 instead of torch.torch.int32 by @vodkaslime in #28883
• pass kwargs in stopping criteria list by @zucchini-nlp in #28927
• Support batched input for decoder start ids by @zucchini-nlp in #28887
• [Docs] Fix broken links and syntax issues by @khipp in #28918
• Fix max_position_embeddings default value for llama2 to 4096 #28241 by @karl-hajjar in #28754
• Fix a wrong link to CONTRIBUTING.md section in PR template by @B-Step62 in #28941
• Fix type annotations on neftune_noise_alpha and fsdp_config TrainingArguments parameters by @peblair in #28942
• [i18n-de] Translate README.md to German by @khipp in #28933
• [Nougat] Fix pipeline by @NielsRogge in #28242
• [Docs] Update README and default pipelines by @NielsRogge in #28864
• Convert torch_dtype as str to actual torch data type (i.e. "float16" …to torch.float16) by @KossaiSbai in #28208
• [pipelines] updated docstring with vqa alias by @cmahmut in #28951
• Tests: tag test_save_load_fast_init_from_base as flaky by @gante in #28930
• Updated requirements for image-classification samples: datasets>=2.14.0 by @alekseyfa in #28974
• Always initialize tied output_embeddings if it has a bias term by @hackyon in #28947
• Clean up staging tmp checkpoint directory by @woshiyyya in #28848
• [Docs] Add language identifiers to fenced code blocks by @khipp in #28955
• [Docs] Add video section by @NielsRogge in #28958
• [i18n-de] Translate CONTRIBUTING.md to German by @khipp in #28954
• [NllbTokenizer] refactor with added tokens decoder by @ArthurZucker in #27717
• Add sudachi_projection option to BertJapaneseTokenizer by @hiroshi-matsuda-rit in #28503
• Update configuration_llama.py: fixed broken link by @AdityaKane2001 in #28946
• [DETR] Update the processing to adapt masks & bboxes to reflect padding by @amyeroberts in #28363
• ENH: Do not pass warning message in case quantization_config is in config but not passed as an arg by @younesbelkada in #28988
• ENH [AutoQuantizer]: enhance trainer + not supported quant methods by @younesbelkada in #28991
• Add SiglipForImageClassification and CLIPForImageClassification by @NielsRogge in #28952
• [Doc] Fix docbuilder - make BackboneMixin and BackboneConfigMixin importable from utils. by @amyeroberts in #29002
• Set the dataset format used by test_trainer to float32 by @statelesshz in #28920
• Introduce AcceleratorConfig dataclass by @muellerzr in #28664
• Fix flaky test vision encoder-decoder generate by @zucchini-nlp in #28923
• Mask Generation Task Guide by @merveenoyan in #28897
• Add tie_weights() to LM heads and set bias in set_output_embeddings() by @hackyon in #28948
• [TPU] Support PyTorch/XLA FSDP via SPMD by @alanwaketan in #28949
• FIX [Trainer / tags]: Fix trainer + tags when users do not pass "tags" to trainer.push_to_hub() by @younesbelkada in #29009
• Add cuda_custom_kernel in DETA by @SangbumChoi in #28989
• DeformableDetrModel support fp16 by @DonggeunYu in #29013
• Fix copies between DETR and DETA by @amyeroberts in #29037
• FIX: Fix error with logger.warning + inline with recent refactor by @younesbelkada in #29039
• Patch to skip failing test_save_load_low_cpu_mem_usage tests by @amyeroberts in #29043
• Fix a tiny typo in generation/utils.py::GenerateEncoderDecoderOutput's docstring by @sadra-barikbin in #29044
• add test marker to run all tests with @require_bitsandbytes by @Titus-von-Koeller in #28278
• Update important model list by @LysandreJik in #29019
• Fix max_length criteria when using inputs_embeds by @zucchini-nlp in #28994
• Support : Leverage Accelerate for object detection/segmentation models by @Tanmaypatil123 in #28312
• fix num_assistant_tokens with heuristic schedule by @jmamou in #28759
• fix failing trainer ds tests by @pacman100 in #29057
• auto_find_batch_size isn't yet supported with DeepSpeed/FSDP. Raise error accrodingly. by @pacman100 in #29058
• Honor trust_remote_code for custom tokenizers by @rl337 in #28854
• Feature: Option to set the tracking URI for MLflowCallback. by @seanswyi in #29032
• Fix trainer test wrt DeepSpeed + auto_find_bs by @muellerzr in #29061
• Add chat support to text generation pipeline by @Rocketknight1 in #28945
• [Docs] Spanish translation of task_summary.md by @aaronjimv in #28844
• [Awq] Add peft support for AWQ by @younesbelkada in #28987
• FIX [bnb / tests]: Fix currently failing bnb tests by @younesbelkada in #29092
• fix the post-processing link by @davies-w in #29091
• Fix the bert-base-cased tokenizer configuration test by @LysandreJik in #29105
• Fix a typo in examples/pytorch/text-classification/run_classification.py by @Ja1Zhou in #29072
• change version by @ArthurZucker in #29097
• [Docs] Add resources by @NielsRogge in #28705
• ENH: added new output_logits option to generate function by @mbaak in #28667
• Bnb test fix for different hardwares by @Titus-von-Koeller in #29066
• Fix two tiny typos in pipelines/base.py::Pipeline::_sanitize_parameters()'s docstring by @sadra-barikbin in #29102
• storing & logging gradient norm in trainer by @shijie-wu in #27326
• Fixed nll with label_smoothing to just nll by @nileshkokane01 in #28708
• [gradient_checkpointing] default to use it for torch 2.3 by @ArthurZucker in #28538
• Move misplaced line by @kno10 in #29117
• FEAT [Trainer / bnb]: Add RMSProp from bitsandbytes to HF Trainer by @younesbelkada in #29082
• Abstract image processor arg checks. by @molbap in #28843
• FIX [bnb / tests] Propagate the changes from #29092 to 4-bit tests by @younesbelkada in #29122
• Llama: fix batched generation by @gante in #29109
• Generate: unset GenerationConfig parameters do not raise warning by @gante in #29119
• [cuda kernels] only compile them when initializing by @ArthurZucker in #29133
• FIX [PEFT / Trainer ] Handle better peft + quantized compiled models by @younesbelkada in #29055
• [Core tokenization] add_dummy_prefix_space option to help with latest issues by @ArthurZucker in #28010
• Revert low cpu mem tie weights by @amyeroberts in #29135
• Add support for fine-tuning CLIP-like models using contrastive-image-text example by @tjs-intel in #29070
• Save (circleci) cache at the end of a job by @ydshieh in #29141
• [Phi] Add support for sdpa by @hackyon in #29108
• Generate: missing generation config eos token setting in encoder-decoder tests by @gante in #29146
• Added image_captioning version in es and included in toctree file by @gisturiz in #29104
• Fix drop path being ignored in DINOv2 by @fepegar in #29147
• [pipeline] Add pool option to image feature extraction pipeline by @amyeroberts in #28985

Significant community contributions

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

• @nakranivaibhav
  • Improved type hinting for all attention parameters (#28479)
  • Adds LlamaForQuestionAnswering class in modeling_llama.py along with AutoModel Support (#28777)
• @khipp
  • Fix input data file extension in examples (#28741)
  • [Docs] Fix spelling and grammar mistakes (#28825)
  • [Docs] Update project names and links in awesome-transformers (#28878)
  • [Docs] Fix backticks in inline code and documentation links (#28875)
  • [Docs] Add missing language options and fix broken links (#28852)
  • [Docs] Fix placement of tilde character (#28913)
  • [Docs] Revert translation of '@slow' decorator (#28912)
  • [Docs] Fix broken links and syntax issues (#28918)
  • [i18n-de] Translate README.md to German (#28933)
  • [Docs] Add language identifiers to fenced code blocks (#28955)
  • [i18n-de] Translate CONTRIBUTING.md to German (#28954)
• @ThibaultLengagne
  • Add French translation: french README.md (#28696)
• @poedator
  • HfQuantizer class for quantization-related stuff in modeling_utils.py (#26610)
• @kiansierra
  • Flax mistral (#26943)
• @hackyon
  • Adding [T5/MT5/UMT5]ForTokenClassification (#28443)
  • Always initialize tied output_embeddings if it has a bias term (#28947)
  • Add tie_weights() to LM heads and set bias in set_output_embeddings() (#28948)
  • [Phi] Add support for sdpa (#29108)
• @SangbumChoi
  • enable graident checkpointing in DetaObjectDetection and add tests in Swin/Donut_Swin (#28615)
  • Add cuda_custom_kernel in DETA (#28989)
• @rajveer43
  • Add models from deit (#28302)
• @jon-tow
  • Add StableLM (#28810)