GitLab

* Rename sanity_evaluation to eval_on_start

* move arg back to last

fix typo

* fix the get_size_with_aspect_ratio in max_size situation

* make fix-up

* add more general solution

* consider when max_size is not defined

* fix typo

* fix typo

* simple fix

* fix error

* fix if else error

* fix error of size overwrite

* fix yolos image processing

* fix detr image processing

* make

* add longest related test script

* Update src/transformers/models/yolos/image_processing_yolos.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add more test

* add test script about longest size

* remove deprecated

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add qwen2 gguf support

* Update docs

* fix qwen2 tokenizer

* add qwen2 gguf test

* fix typo in qwen2 gguf test

* format code

* Remove mistral, clarify the error message

* format code

* add typing and update docstring

* fix

* fix

* fix

* fix

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Update MLP

* tmp commit

* sliding window with fewer differences

* make fixup + rebase

* missing overwrite

* update non-causal mask for sdpa

* add test

* update docstrings

* add one more test

* fix cross attention bug

* gentler atol/rtol

While running the model.prepare_tf_dataset() method,
it raises the error below:
```
TypeError: Cannot convert [array([322.,   1.])] to EagerTensor of dtype int64
```

This happens, in  "DataCollatorForSeq2Seq" function when we are try
to convert the labels to tensors. While converting the labels to tensors,
the labels can be in the format of list of list or list of ndarrays.
There is no problem converting the list of list lables. There is a problem
when the list of ndarrays are float values(like below).

```
[array([322.,   1.])]
```

so the exception raises while trying to convert this label to tensors using
below code.

```
batch["labels"] = tf.constant(batch["labels"], dtype=tf.int64)
```

The labels are always integer values, so this got converted to float
values in the label padding operation below.
```
batch["labels"] = [
                    call(label)
                    if padding_side == "right"
    ...

* fixes

* fix-copies

* token healing impl + trie with extensions

* make fixup

* prefix-robust space tokenization

* examples readme and requirements

* make fixup

* allow input prompt and model

* redundant defaults

* Specialized Trie

* make fixup

* updated tests with new inherited Tree

* input ids to auto device_map

* rm unused import

* Update src/transformers/generation/utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* naming convention

* Revert "naming convention"

This reverts commit dd39d9c5b7a969e2d8a8d2a8e54f121b82dc44f0.

* naming convention

* last -hopefully- changes

---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Remove copied froms for deprecated models

* Remove automatically in script

Corrected a typo in security.md. Changed `use_safetenstors` to `use_safetensors` in the section discussing the usage of safe formats for loading models to prevent arbitrary code execution.

* current working example!

* commit regex and result file

* update

* nit

* push the conversion file

* oups

* roadmap and nits

* attempt diffs for 3 files

* persimmon

* nit

* add diff file that is the same as the modeling_llama.py

* fix rope nits

* updates

* updates with converted versions

* give some breathing space to the code

* delete

* update

* update

* push the actual result

* update regex patterns

* update regex patterns

* fix some issues

* fix some issues

* fix some issues

* updates

* updates

* updates

* updates

* updates

* revert changes done to llama

* updates

* update gemma

* updates

* oups

* current state

* current state

* update

* ouiiii

* nit

* clear diffs

* nit

* fixup

* update

* doc 🚀

* 🔥

* for now use gemma

* deal with comments

* style

* handle funtions

* deal with assigns

* todos

* process inheritage

*...

* Description of quantization_config

Added missing description about quantization_config in replace_with_bnb_linear for better readability.

* Removed trailing spaces

* Initial setup

* Metrics

* Overfit on two batches

* Train 40 epochs

* Memory leak debugging

* Trainer fine-tuning

* Draft

* Fixup

* Trained end-to-end

* Add requirements

* Rewrite evaluator

* nits

* Add readme

* Add instance-segmentation to the table

* Support void masks

* Remove sh

* Update docs

* Add pytorch test

* Add accelerate test

* Update examples/pytorch/instance-segmentation/README.md

* Update examples/pytorch/instance-segmentation/run_instance_segmentation.py

* Update examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py

* Update examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py

* Update examples/pytorch/instance-segmentation/run_instance_segmentation.py

* Fix consistency oneformer

* Fix imports

* Fix imports sort

* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update examples/pytorch/instance-segmentation/run_instance_segmentation.py
Co-authored-by: Sangbum Daniel Choi <34004152+SangbumChoi@users.noreply.github.com>

* Add resources to docs

* Update examples/pytorch/instance-segmentation/README.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update examples/pytorch/instance-segmentation/README.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Remove explicit model_type argument

* Fix tests

* Update readme

* Note about other models

---------
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Sangbum Daniel Choi <34004152+SangbumChoi@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Implement streaming run in ReAct agents
* Allow additional imports in code agents
* Python interpreter: support classes and exceptions, fixes

* add sanity evaluation

* fix

* Apply suggestions from code review
Co-authored-by: Zach Mueller <muellerzr@gmail.com>

* fix

---------
Co-authored-by: Zach Mueller <muellerzr@gmail.com>

enhance error message

`mask` variable is not defined. probably a writing mistake. it should be `segmentation_map`. `segmentation_map` should be a `1` channel image rather than `RGB`.
[on a different note, the `mask_url` is the same as `raw_image`. could provide a better example.

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* helper

* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* updates

* more doc

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

remove `IS_GITHUB_CI`

Replace all occurences of `load_in_8bit` with bnb config

fix get_scheduler args

add validation for bnb config

* remove

* build

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Modified test

* Added on_optimizer_step to callbacks

* Move callback after step is called

* Added on optimizer step callback

* add Raushan

* add Raushan

Update overview.md

* fix

* fix

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Fix env.py in cases where torch is not present

* Simplify the fix (and avoid some issues)

* Improve `transformers-cli env` reporting

* move the line `"Using GPU in script?": "<fill in>"` to in if conditional
statement

* same option for npu

* Fix has_file in offline mode

* harmonize env variable for offline mode

* Switch to HF_HUB_OFFLINE

* fix test

* revert test_offline to test TRANSFORMERS_OFFLINE

* Add new offline test

* merge conflicts

* docs

* add mistral v3 conversion script

* Update src/transformers/models/mistral/convert_mistral_weights_to_hf.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fixup

---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* quanto latest version was refactored

* add error msg

* incorrect compare sign

* Update src/transformers/cache_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Deprecate models
- graphormer
- time_series_transformer
- xlm_prophetnet
- qdqbert
- nat
- ernie_m
- tvlt
- nezha
- mega
- jukebox
- vit_hybrid
- x_clip
- deta
- speech_to_text_2
- efficientformer
- realm
- gptsan_japanese

* Fix up

* Fix speech2text2 imports

* Make sure message isn't indented

* Fix docstrings

* Correctly map for deprecated models from model_type

* Uncomment out

* Add back time series transformer and x-clip

* Import fix and fix-up

* Fix up with updated ruff