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@dreadnode-renovate-bot dreadnode-renovate-bot bot commented Jan 28, 2026 •
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This PR contains the following updates:

Package Change Age Confidence
transformers >=4.41.0,<5.0.0 → >=5.1.0,<5.2.0 age confidence

Release Notes

huggingface/transformers (transformers)

v5.1.0: : EXAONE-MoE, PP-DocLayoutV3, Youtu-LLM, GLM-OCR

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New Model additions

EXAONE-MoE
image

K-EXAONE is a large-scale multilingual language model developed by LG AI Research. Built using a Mixture-of-Experts architecture, K-EXAONE features 236 billion total parameters, with 23 billion active during inference. Performance evaluations across various benchmarks demonstrate that K-EXAONE excels in reasoning, agentic capabilities, general knowledge, multilingual understanding, and long-context processing.

PP-DocLayoutV3
image

PP-DocLayoutV3 is a unified and high-efficiency model designed for comprehensive layout analysis. It addresses the challenges of complex physical distortions—such as skewing, curving, and adverse lighting—by integrating instance segmentation and reading order prediction into a single, end-to-end framework.

Youtu-LLM
image

Youtu-LLM is a new, small, yet powerful LLM, contains only 1.96B parameters, supports 128k long context, and has native agentic talents. On general evaluations, Youtu-LLM significantly outperforms SOTA LLMs of similar size in terms of Commonsense, STEM, Coding and Long Context capabilities; in agent-related testing, Youtu-LLM surpasses larger-sized leaders and is truly capable of completing multiple end2end agent tasks.

GlmOcr
image

GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization. The model integrates the CogViT visual encoder pre-trained on large-scale image–text data, a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder. Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance across diverse document layouts.

Breaking changes

  • 🚨 T5Gemma2 model structure (#​43633) - Makes sure that the attn implementation is set to all sub-configs. The config.encoder.text_config was not getting its attn set because we aren't passing it to PreTrainedModel.init. We can't change the model structure without breaking so I manually re-added a call to self.adjust_attn_implemetation in modeling code

  • 🚨 Generation cache preparation (#​43679) - Refactors cache initialization in generation to ensure sliding window configurations are now properly respected. Previously, some models (like Afmoe) created caches without passing the model config, causing sliding window limits to be ignored. This is breaking because models with sliding window attention will now enforce their window size limits during generation, which may change generation behavior or require adjusting sequence lengths in existing code.

  • 🚨 Delete duplicate code in backbone utils (#​43323) - This PR cleans up backbone utilities. Specifically, we have currently 5 different config attr to decide which backbone to load, most of which can be merged into one and seem redundant
    After this PR, we'll have only one config.backbone_config as a single source of truth. The models will load the backbone from_config and load pretrained weights only if the checkpoint has any weights saved. The overall idea is same as in other composite models. A few config arguments are removed as a result.

  • 🚨 Refactor DETR to updated standards (#​41549) - standardizes the DETR model to be closer to other vision models in the library.

  • 🚨Fix floating-point precision in JanusImageProcessor resize (#​43187) - replaces an int() with round(), expect light numerical differences

  • 🚨 Remove deprecated AnnotionFormat (#​42983) - removes a missnamed class in favour of AnnotationFormat.

Bugfixes and improvements

Significant community contributions

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

v5.0.0: Transformers v5

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Transformers v5 release notes

image
  • Highlights
  • Significant API changes: dynamic weight loading, tokenization
  • Backwards Incompatible Changes
  • Bugfixes and improvements

We have a migration guide that will be continuously updated available on the main branch, please check it out in case you're facing issues: migration guide.

Highlights

We are excited to announce the initial release of Transformers v5. This is the first major release in five years, and the release is significant: 1200 commits have been pushed to main since the latest minor release. This release removes a lot of long-due deprecations, introduces several refactors that significantly simplify our APIs and internals, and comes with a large number of bug fixes.

We give an overview of our focus for this release in the following blogpost. In these release notes, we'll focus directly on the refactors and new APIs coming with v5.

This release is the full V5 release. It sets in motion something bigger: going forward, starting with v5, we'll now release minor releases every week, rather than every 5 weeks. Expect v5.1 to follow next week, then v5.2 the week that follows, etc.

We're moving forward with this change to ensure you have access to models as soon as they're supported in the library, rather than a few weeks after.

In order to install this release, please do so with the following:

pip install transformers

For us to deliver the best package possible, it is imperative that we have feedback on how the toolkit is currently working for you. Please try it out, and open an issue in case you're facing something inconsistent/a bug.

Transformers version 5 is a community endeavor, and we couldn't have shipped such a massive release without the help of the entire community.

Significant API changes

Dynamic weight loading

We introduce a new weight loading API in transformers, which significantly improves on the previous API. This
weight loading API is designed to apply operations to the checkpoints loaded by transformers.

Instead of loading the checkpoint exactly as it is serialized within the model, these operations can reshape, merge,
and split the layers according to how they're defined in this new API. These operations are often a necessity when
working with quantization or parallelism algorithms.

This new API is centered around the new WeightConverter class:

class WeightConverter(WeightTransform):
    operations: list[ConversionOps]
    source_keys: Union[str, list[str]]
    target_keys: Union[str, list[str]]

The weight converter is designed to apply a list of operations on the source keys, resulting in target keys. A common
operation done on the attention layers is to fuse the query, key, values layers. Doing so with this API would amount
to defining the following conversion:

conversion = WeightConverter(
    ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"],  # The input layers
    "self_attn.qkv_proj",  # The single layer as output
    operations=[Concatenate(dim=0)],
)

In this situation, we apply the Concatenate operation, which accepts a list of layers as input and returns a single
layer.

This allows us to define a mapping from architecture to a list of weight conversions. Applying those weight conversions
can apply arbitrary transformations to the layers themselves. This significantly simplified the from_pretrained method
and helped us remove a lot of technical debt that we accumulated over the past few years.

This results in several improvements:

  • Much cleaner definition of transformations applied to the checkpoint
  • Reversible transformations, so loading and saving a checkpoint should result in the same checkpoint
  • Faster model loading thanks to scheduling of tensor materialization
  • Enables complex mix of transformations that wouldn't otherwise be possible (such as quantization + MoEs, or TP + MoEs)

Linked PR: #​41580

Tokenization

Just as we moved towards a single backend library for model definition, we want our tokenizers, and the Tokenizer object to be a lot more intuitive. With v5, tokenizer definition is much simpler; one can now initialize an empty LlamaTokenizer and train it directly on your corpus.

Defining a new tokenizer object should be as simple as this:

from transformers import TokenizersBackend, generate_merges
from tokenizers import pre_tokenizers, Tokenizer
from tokenizers.model import BPE

class Llama5Tokenizer(TokenizersBackend):
    def __init__(self, unk_token="<unk>",bos_token="<s>", eos_token="</s>", vocab=None, merges=None ):
        if vocab is None:
            self._vocab = {
                str(unk_token): 0,
                str(bos_token): 1,
                str(eos_token): 2,
            }

        else:
            self._vocab = vocab

            self._merges = merges

        self._tokenizer = Tokenizer(
            BPE(vocab=self._vocab, merges=self._merges, fuse_unk=True)
        )
        self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(
            replacement="▁", prepend_scheme=_get_prepend_scheme(self.add_prefix_space, self), split=False
        )
        super().__init__(
            tokenizer_object=self._tokenizer,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
        )

Once the tokenizer is defined as above, you can load it with the following: Llama5Tokenizer(). Doing this returns you an empty, trainable tokenizer that follows the definition of the authors of Llama5 (it does not exist yet 😉).

The above is the main motivation towards refactoring tokenization: we want tokenizers to behave similarly to models: trained or empty, and with exactly what is defined in their class definition.

Backend Architecture Changes: moving away from the slow/fast tokenizer separation

Up to now, transformers maintained two parallel implementations for many tokenizers:

  • "Slow" tokenizers (tokenization_<model>.py) - Python-based implementations, often using SentencePiece as the backend.
  • "Fast" tokenizers (tokenization_<model>_fast.py) - Rust-based implementations using the 🤗 tokenizers library.

In v5, we consolidate to a single tokenizer file per model: tokenization_<model>.py. This file will use the most appropriate backend available:

  1. TokenizersBackend (preferred): Rust-based tokenizers from the 🤗 tokenizers library. In general it provides optimal performance, but it also offers a lot more features that are commonly adopted across the ecosystem:
  • handling additional tokens
  • a full python API for setting and updating
  • automatic parallelization,
  • automatic offsets
  • customization
  • training
  1. SentencePieceBackend: for tokenizers requiring the sentencepiece library. It inherits from PythonBackend.
  2. PythonBackend: a Python implementations of the features provided by tokenizers. Basically allows adding tokens.
  3. MistralCommonBackend: relies on MistralCommon's tokenization library. (Previously known as the MistralCommonTokenizer)

The AutoTokenizer automatically selects the appropriate backend based on available files and dependencies. This is transparent, you continue to use AutoTokenizer.from_pretrained() as before. This allows transformers to be future-proof and modular to easily support future backends.

Defining a tokenizers outside of the existing backends

We enable users and tokenizer builders to define their own tokenizers from top to bottom. Tokenizers are usually defined using a backend such as tokenizers, sentencepiece or mistral-common, but we offer the possibility to design the tokenizer at a higher-level, without relying on those backends.

To do so, you can import the PythonBackend (which was previously known as PreTrainedTokenizer). This class encapsulates all the logic related to added tokens, encoding, and decoding.

If you want something even higher up the stack, then PreTrainedTokenizerBase is what PythonBackend inherits from. It contains the very basic tokenizer API features:

  • encode
  • decode
  • vocab_size
  • get_vocab
  • convert_tokens_to_ids
  • convert_ids_to_tokens
  • from_pretrained
  • save_pretrained
  • among a few others
API Changes
1. Direct tokenizer initialization with vocab and merges

Starting with v5, we now enable initializing blank, untrained tokenizers-backed tokenizers:

from transformers import LlamaTokenizer

tokenizer = LlamaTokenizer()

This tokenizer will therefore follow the definition of the LlamaTokenizer as defined in its class definition. It can then be trained on a corpus as can be seen in the tokenizers documentation.

These tokenizers can also be initialized from vocab and merges (if necessary), like the previous "slow" tokenizers:

from transformers import LlamaTokenizer

vocab = {"<unk>": 0, "<s>": 1, "</s>": 2, "hello": 3, "world": 4}
merges = [("h", "e"), ("l", "l"), ("o", " ")]

tokenizer = LlamaTokenizer(vocab=vocab, merges=merges)

This tokenizer will behave as a Llama-like tokenizer, with an updated vocabulary. This allows comparing different tokenizer classes with the same vocab; therefore enabling the comparison of different pre-tokenizers, normalizers, etc.

⚠️ The vocab_file (as in, a path towards a file containing the vocabulary) cannot be used to initialize the LlamaTokenizer as loading from files is reserved to the from_pretrained method.

2. Simplified decoding API

The batch_decode and decode methods have been unified to reflect behavior of the encode method. Both single and batch decoding now use the same decode method. See an example of the new behavior below:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("t5-small") 
inputs = ["hey how are you?", "fine"]
tokenizer.decode(tokenizer.encode(inputs))

Gives:

- 'hey how are you?</s> fine</s>'
+ ['hey how are you?</s>', 'fine</s>']

We expect encode and decode to behave, as two sides of the same coin: encode, process, decode, should work.

[!NOTE]
A common use-case would be: encode, model.generate, decode. However, using generate would return list[list[int]], which would then be incompatible with decode.

3. Unified encoding API

The encode_plus method is deprecated in favor of the single __call__ method.

4. apply_chat_template returns BatchEncoding

Previously, apply_chat_template returned input_ids for backward compatibility. Starting with v5, it now consistently returns a BatchEncoding dict like other tokenizer methods.

# v5
messages = [
    {"role": "user", "content": "Hello!"},
    {"role": "assistant", "content": "Hi there!"}
]

# Now returns BatchEncoding with input_ids, attention_mask, etc.
outputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
print(outputs.keys())  # dict_keys(['input_ids', 'attention_mask'])
5. Removed legacy configuration file saving:

We simplify the serialization of tokenization attributes:

  • special_tokens_map.json - special tokens are now stored in tokenizer_config.json.
  • added_tokens.json - added tokens are now stored in tokenizer.json.
  • added_tokens_decoder is only stored when there is no tokenizer.json.

When loading older tokenizers, these files are still read for backward compatibility, but new saves use the consolidated format. We're gradually moving towards consolidating attributes to fewer files so that other libraries and implementations may depend on them more reliably.

6. Model-Specific Changes

Several models that had identical tokenizers now import from their base implementation:

  • LayoutLM → uses BertTokenizer
  • LED → uses BartTokenizer
  • Longformer → uses RobertaTokenizer
  • LXMert → uses BertTokenizer
  • MT5 → uses T5Tokenizer
  • MVP → uses BartTokenizer

These modules will eventually be removed altogether.

Removed T5-specific workarounds

The internal _eventually_correct_t5_max_length method has been removed. T5 tokenizers now handle max length consistently with other models.

Testing Changes

A few testing changes specific to tokenizers have been applied:

  • Model-specific tokenization test files now focus on integration tests.
  • Common tokenization API tests (e.g., add_tokens, encode, decode) are now centralized and automatically applied across all tokenizers. This reduces test duplication and ensures consistent behavior

For legacy implementations, the original BERT Python tokenizer code (including WhitespaceTokenizer, BasicTokenizer, etc.) is preserved in bert_legacy.py for reference purposes.

7. Deprecated / Modified Features

Special Tokens Structure:

  • SpecialTokensMixin: Merged into PreTrainedTokenizerBase to simplify the tokenizer architecture.
  • special_tokens_map: Now only stores named special token attributes (e.g., bos_token, eos_token). Use extra_special_tokens for additional special tokens (formerly additional_special_tokens). all_special_tokens includes both named and extra tokens.
# v4
tokenizer.special_tokens_map  # Included 'additional_special_tokens'

# v5
tokenizer.special_tokens_map  # Only named tokens
tokenizer.extra_special_tokens  # Additional tokens
  • special_tokens_map_extended and all_special_tokens_extended: Removed. Access AddedToken objects directly from _special_tokens_map or _extra_special_tokens if needed.
  • additional_special_tokens: Still accepted for backward compatibility but is automatically converted to extra_special_tokens.

Deprecated Methods:

  • sanitize_special_tokens(): Already deprecated in v4, removed in v5.
  • prepare_seq2seq_batch(): Deprecated; use __call__() with text_target parameter instead.
# v4
model_inputs = tokenizer.prepare_seq2seq_batch(src_texts, tgt_texts, max_length=128)

# v5
model_inputs = tokenizer(src_texts, text_target=tgt_texts, max_length=128, return_tensors="pt")
model_inputs["labels"] = model_inputs.pop("input_ids_target")
  • BatchEncoding.words(): Deprecated; use word_ids() instead.

Removed Methods:

  • create_token_type_ids_from_sequences(): Removed from base class. Subclasses that need custom token type ID creation should implement this method directly.
  • prepare_for_model(), build_inputs_with_special_tokens(), truncate_sequences(): Moved from tokenization_utils_base.py to tokenization_python.py for PythonBackend tokenizers. TokenizersBackend provides model-ready input via tokenize() and encode(), so these methods are no longer needed in the base class.
  • _switch_to_input_mode(), _switch_to_target_mode(), as_target_tokenizer(): Removed from base class. Use __call__() with text_target parameter instead.
# v4
with tokenizer.as_target

</details>

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@dreadnode-renovate-bot dreadnode-renovate-bot bot added area/python Changes to Python package configuration and dependencies type/digest Dependency digest updates labels Jan 28, 2026
@dreadnode-renovate-bot
fix(deps): update dependency transformers to v5
157a706 
| datasource | package      | from   | to    |
| ---------- | ------------ | ------ | ----- |
| pypi       | transformers | 4.57.1 | 5.1.0 |
@dreadnode-renovate-bot dreadnode-renovate-bot bot force-pushed the renovate/transformers-5.x branch from 9f77e00 to 157a706 Compare February 11, 2026 00:31
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