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} | `ipex.optimize` optimizations are mostly upstreamed into PyTorch, and new optimizations will directly upstream to PyTorch, we don't need explicitly all `ipex.optimize` to make it, retiring `ipex.optimize` usage from `transformers` | {
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} | # What does this PR do?
This PR updates the model-card for the Canine model, as described in https://github.com/huggingface/transformers/issues/36979, in an attempt to standardize all model-cards.
<!--
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## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@stevhliu
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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} | # What does this PR do?
This PR updates the model-card for the Canine model, as described in https://github.com/huggingface/transformers/issues/36979, in an attempt to standardize all model-cards.
<!--
Congratulations! You've made it this far! You're not quite done yet though.
Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->
<!-- Remove if not applicable -->
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@stevhliu
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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Testing the live preview, please ignore this
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Fixes # (issue)
## Before submitting
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## Who can review?
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} | # What does this PR do?
Fixes behavior introduced in 4.52 VLM refacto #37033 that renames VLM model weights but forgot about adapters.
(Theoretically, it seems the problem was just the non-propagation of keymapping to peft but this is made a lot more salient by recent big changes in VLM naming, which deprecates all existing adapters basically)
@zucchini-nlp
Note that this is just for loading, not sure if we want to generalize this to saving... I kind of feel we might just want people to save new adapters in the correct format going forward (and slowly convert all models to the new format while ensuring backcompat) | {
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https://api.github.com/repos/huggingface/transformers/issues/38626 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38626/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38626/comments | https://api.github.com/repos/huggingface/transformers/issues/38626/events | https://github.com/huggingface/transformers/pull/38626 | 3,122,967,408 | PR_kwDOCUB6oc6ZS3AQ | 38,626 | fix: Added code to match interpolation of Google's ViT implementation… | {
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} | Fixes #28180
# What does this PR do?
Fixes the interpolation method in ViT image processors to match the original Google ViT implementation. Changes the default resampling from BILINEAR to BICUBIC interpolation.
## Implementation Notes
This implementation follows @NielsRogge's comments from #28180:
- Added pixel value verification using `torch.allclose` similar to the DINOv2 conversion
- Verification ensures HuggingFace preprocessing matches the reference implementation
- DeiT verification is currently skipped with `pass` - can be updated in a follow-up PR if needed
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@NielsRogge @amyeroberts @qubvel
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Companion report to #38609 to modify the docstring within the code for Wav2Vec2Processor and Wav2Vec2ProcessorWithLM
### Who can help?
@eustlb
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
None. Refer to #38609 for details.
### Expected behavior
Docstring should not refer to as_target_processor so users will migrate to new method. | {
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} | # What does this PR do?
This PR updates the RagRetriever Class retrieve function signature so that it shows the correct returned items.
This PR also removes the requirement of the faiss dependency when using the RagRetriever Class.
<!--
Congratulations! You've made it this far! You're not quite done yet though.
Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->
<!-- Remove if not applicable -->
Fixes https://github.com/huggingface/transformers/issues/38623
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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https://api.github.com/repos/huggingface/transformers/issues/38623 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38623/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38623/comments | https://api.github.com/repos/huggingface/transformers/issues/38623/events | https://github.com/huggingface/transformers/issues/38623 | 3,122,828,964 | I_kwDOCUB6oc66Ipak | 38,623 | RagRetriever retrieve function mismatch in signature and return, unneeded requirement for faiss | {
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] | closed | false | null | [] | null | [] | 2025-06-05T22:08:34 | 2025-06-09T15:17:35 | 2025-06-09T15:17:35 | CONTRIBUTOR | null | null | null | null | ### System Info
When creating a custom RagRetriever based on the RagRetriever Class it became apparent when using linting tools that the signature and returned items in the [retrieve](https://github.com/huggingface/transformers/blob/main/src/transformers/models/rag/retrieval_rag.py#L560-L580) function do not match.
```
def retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, List[dict]]:
.......
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(doc_ids)
```
In using the RagRetriever functionality it also became clear that I needed to install faiss-cpu library, even though it is not required for the work that I am doing. Therefore it should be optional.
```
File /opt/app-root/lib64/python3.11/site-packages/transformers/models/rag/retrieval_rag.py:400, in RagRetriever.__init__(self, config, question_encoder_tokenizer, generator_tokenizer, index, init_retrieval)
398 def __init__(self, config, question_encoder_tokenizer, generator_tokenizer, index=None, init_retrieval=True):
399 self._init_retrieval = init_retrieval
--> 400 requires_backends(self, ["datasets", "faiss"])
401 super().__init__()
402 self.index = index or self._build_index(config)
File /opt/app-root/lib64/python3.11/site-packages/transformers/utils/import_utils.py:1871, in requires_backends(obj, backends)
1868 failed.append(msg.format(name))
1870 if failed:
-> 1871 raise ImportError("".join(failed))
ImportError:
CustomRAGRetriever requires the faiss library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones
that match your environment. Please note that you may need to restart your runtime after installation.
```
I will submit a PR that will hopefully fix these issues.
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Steps to reproduce the errors:
retrieve function signature mismatch with returned items:
Run mypy linting on code inheriting the base RagRetriever class where your custom retrieve function signature mentions returning 3 items and you will receive an error that this does not match
```
error: Signature of "retrieve" incompatible with supertype "RagRetriever" [override]
```
faiss dependency requirement:
Create a custom rag retriever and try to use it without importing faiss in your environment and you will get this error:
```
CustomRagRetriever requires the faiss library but it was not found in your environment. Checkout the instructions on the
installation page of its repo...
```
Note: I am not using faiss, as it is not required, therefore a ragretriever can be used without this dependency.
### Expected behavior
I would expect the function signature to match the number of items returned by the function.
I would expect to be able to inherit from the RagRetriever without needing the faiss dependency. | {
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} | # What does this PR do?
This PR fixes a bug as MODEL_FOR_MASK_GENERATION_MAPPING_NAMES was defined twice.
Reported here: https://github.com/NielsRogge/Transformers-Tutorials/issues/497 | {
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https://api.github.com/repos/huggingface/transformers/issues/38621 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38621/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38621/comments | https://api.github.com/repos/huggingface/transformers/issues/38621/events | https://github.com/huggingface/transformers/pull/38621 | 3,122,480,714 | PR_kwDOCUB6oc6ZRKY9 | 38,621 | Add Arcee model support | {
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} | ## Summary
This PR adds support for the Arcee model architecture, laying the groundwork for the upcoming Arcee Foundation Model (AFM) release. Arcee is a decoder-only transformer model based on the Llama architecture with a key modification: it uses ReLU² (ReLU-squared) activation in the MLP blocks instead of SiLU, following recent research showing improved training efficiency with squared activations.
## Model Description
Arcee is architecturally similar to Llama but with the following distinctions:
- **ReLU² activation**: Uses `x * relu(x)` in MLP layers for improved gradient flow
- **Optimized for efficiency**: Designed with training and inference efficiency in mind
- **Extended context**: Supports extended context with RoPE scaling
## Implementation Details
- Modular implementation inheriting from Llama components where applicable
- Custom ArceeMLP class implementing the ReLU² activation
- Full support for all standard transformers features:
- Flash Attention 2, SDPA, and other attention backends
- Gradient checkpointing
- Quantization support (including quantized caches)
- All standard model variants (CausalLM, SequenceClassification, QuestionAnswering, TokenClassification)
## Testing
- Added comprehensive test suite following standard transformers test patterns
- Tests for all model variants and core functionality
- Specific test for ReLU² activation verification
- RoPE scaling tests including YARN support
- Tested model forward and backward passes
- Verified compatibility with existing architecture
- Model loading and forward passes verified
- Compatibility with existing infrastructure confirmed | {
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https://api.github.com/repos/huggingface/transformers/issues/38620 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38620/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38620/comments | https://api.github.com/repos/huggingface/transformers/issues/38620/events | https://github.com/huggingface/transformers/pull/38620 | 3,122,379,192 | PR_kwDOCUB6oc6ZQzjl | 38,620 | Use torch 2.7.1 on daily CI | {
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} | # What does this PR do?
I separate this from #37856 as that one is prepared 1 month ago + I would merge this PR on this Saturday evening instead. | {
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https://api.github.com/repos/huggingface/transformers/issues/38618 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38618/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38618/comments | https://api.github.com/repos/huggingface/transformers/issues/38618/events | https://github.com/huggingface/transformers/pull/38618 | 3,122,156,010 | PR_kwDOCUB6oc6ZQBgC | 38,618 | Fix typo in LLaVa documentation | {
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} | In exactly one section, LlavaImageProcessor had documentation errors, which threw the entire thing off.
# What does this PR do?
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## Before submitting
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https://api.github.com/repos/huggingface/transformers/issues/38617 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38617/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38617/comments | https://api.github.com/repos/huggingface/transformers/issues/38617/events | https://github.com/huggingface/transformers/issues/38617 | 3,121,797,099 | I_kwDOCUB6oc66Etfr | 38,617 | ImportError: cannot import name 'layer_type_validation' from 'transformers.configuration_utils' | {
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] | closed | false | null | [] | null | [] | 2025-06-05T16:09:03 | 2025-06-15T07:56:37 | 2025-06-15T07:56:37 | NONE | null | null | null | null | ### System Info
env:
Name: transformers
Version: 4.53.0.dev0
whe i called hte code bellowed:
`model = AutoModelForImageTextToText.from_pretrained(model_id, local_files_only=True, **model_kwargs)`
the model_id is medgemma model that from https://huggingface.co/models?other=medgemma.
the ImportError: cannot import name 'layer_type_validation' from 'transformers.configuration_utils (/usr/local/lib/python3.11/dist-packages/transformers/configuration_utils.py' errror occurred.
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
model = AutoModelForImageTextToText.from_pretrained(model_id, local_files_only=True, **model_kwargs)
#note model_id visit on https://huggingface.co/models?other=medgemma
### Expected behavior
run without error. | {
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https://api.github.com/repos/huggingface/transformers/issues/38616 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38616/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38616/comments | https://api.github.com/repos/huggingface/transformers/issues/38616/events | https://github.com/huggingface/transformers/pull/38616 | 3,121,775,743 | PR_kwDOCUB6oc6ZOs8r | 38,616 | Remove `isort` from dependencies | {
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} | [] | closed | false | null | [] | null | [] | 2025-06-05T16:01:04 | 2025-06-05T16:43:32 | 2025-06-05T16:42:50 | CONTRIBUTOR | null | null | false | {
"url": "https://api.github.com/repos/huggingface/transformers/pulls/38616",
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"merged_at": "2025-06-05T16:42:50"
} | # What does this PR do?
`isort` is no longer being used anywhere, it is replaced with ruff. So, I think it can be removed from dependencies.
References: https://docs.astral.sh/ruff/linter/#action-comments
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@ydshieh @ArthurZucker | {
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https://api.github.com/repos/huggingface/transformers/issues/38615 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38615/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38615/comments | https://api.github.com/repos/huggingface/transformers/issues/38615/events | https://github.com/huggingface/transformers/pull/38615 | 3,121,595,827 | PR_kwDOCUB6oc6ZOFdg | 38,615 | Don't run `AriaForConditionalGenerationModelTest` on CircleCI | {
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} | [] | closed | false | null | [] | null | [] | 2025-06-05T14:59:01 | 2025-06-06T09:30:32 | 2025-06-06T09:30:31 | COLLABORATOR | null | null | false | {
"url": "https://api.github.com/repos/huggingface/transformers/pulls/38615",
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"merged_at": "2025-06-06T09:30:31"
} | # What does this PR do?
Let's just simply get rid of this model tests on CircleCI. Cause the slowest job to finish in `13m 25s`.
We still have it on dailly CI
```bash
148.80s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_model_outputs_equivalence
120.40s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_model_outputs_equivalence
55.83s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_resize_tokens_embeddings
36.21s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_attention_outputs
28.76s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_resize_embeddings_untied
23.11s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_training
22.56s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_retain_grad_hidden_states_attentions
22.27s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_hidden_states_output
22.15s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_determinism
14.41s call tests/models/aria/test_modeling_aria.py::AriaForConditionalGenerationModelTest::test_save_load
``` | {
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https://api.github.com/repos/huggingface/transformers/issues/38613 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38613/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38613/comments | https://api.github.com/repos/huggingface/transformers/issues/38613/events | https://github.com/huggingface/transformers/issues/38613 | 3,121,451,362 | I_kwDOCUB6oc66DZFi | 38,613 | MDX Errors | {
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Ubuntu 24.04.2 LTS, CPython 3.11.12, transformers==4.53.0.dev0
@stevhliu I'm trying to contribute to the model cards. I forked the latest transformers and I ran the scripts, from the home page and then I want to the documents page. I'm having issues with the doc builder. I keep receiving the errors "ValueError: There was an error when converting docs/source/en/internal/generation_utils.md to the MDX format.
Unable to find generation.TFGreedySearchEncoderDecoderOutput in transformers. Make sure the path to that object is correct." And Unable to find image_processing_utils_fast.BaseImageProcessorFast in transformers. Make sure the path to that object is correct.
I ran the " pip install -e ".[docs]" and saw this after installing everything: "warning: The package `transformers @ file://s` does not have an extra named `docs`"
I ran the doc builder and that ran as expected until I ran the doc-builder command "doc-builder build transformers docs/source/en/ --build_dir ~/tmp/test-build"
Is there something that I'm misunderstanding? Is there a workaround for me to write the markdown of the card that I have been assigned without having to run those scripts instead, in the meantime.. Thank you!
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Ran install scripts on the Documents folder
### Expected behavior
To generate the docs | {
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} | # What does this PR do?
They never pass on T4 runners ... | {
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https://api.github.com/repos/huggingface/transformers/issues/38610 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38610/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38610/comments | https://api.github.com/repos/huggingface/transformers/issues/38610/events | https://github.com/huggingface/transformers/pull/38610 | 3,121,294,949 | PR_kwDOCUB6oc6ZNJ2- | 38,610 | [Nit] Add Note on SigOpt being in Public Archive Mode | {
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"url": "https://api.github.com/repos/huggingface/transformers/pulls/38610",
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} | # What does this PR do?
As discussed in the issue, I've added a note about the sigopt project being in public archive mode. Could you please review?
Fixes #38593
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
## Who can review?
@stevhliu | {
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https://api.github.com/repos/huggingface/transformers/issues/38609 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38609/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38609/comments | https://api.github.com/repos/huggingface/transformers/issues/38609/events | https://github.com/huggingface/transformers/issues/38609 | 3,121,259,283 | I_kwDOCUB6oc66CqMT | 38,609 | Wav2Vec2Processor/Wav2Vec2ProcessorWithLM documentation still refers to 'as_target_processor' | {
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] | closed | false | null | [] | null | [] | 2025-06-05T13:30:05 | 2025-06-09T19:26:10 | 2025-06-09T19:26:10 | CONTRIBUTOR | null | null | null | null | ### System Info
"as_target_processor()" is to be deprecated since using it will throw a warning:
```
UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.
```
This warning has been around since at least transformers 4.49, however the documentation still refers to the use of "as_target_processor" in Wav2Vec2Processor/Wav2Vec2ProcessorWithLM.
While HuggingFace task recipes have been updated, many 3rd-party examples still use as_target_processor() and the documentation is confusing since you no longer need to call the processor twice (once outside the 'with' block and once inside), but can add both the speech array and the transcription text into one single call to processor.
This is important because tnt documentation still carries the impression that processor should be called twice, and in fact simply calling the processor twice with no "with" block leads to hard to understand errors.
### Who can help?
@stevhliu
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
See
1. https://huggingface.co/docs/transformers/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor
2. https://huggingface.co/docs/transformers/en/model_doc/wav2vec2#transformers.Wav2Vec2ProcessorWithLM
### Expected behavior
Updated documentation to match the task recipes. | {
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} | Hey team! Fixed errors in `test_modeling_mllama.py` / `test_modeling_video_llava.py` / `test_processing_common.py`
`dirrectly` - `directly` x3
`successfull` - `successful`
`suports` - `supports` | {
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https://api.github.com/repos/huggingface/transformers/issues/38607 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38607/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38607/comments | https://api.github.com/repos/huggingface/transformers/issues/38607/events | https://github.com/huggingface/transformers/pull/38607 | 3,120,757,044 | PR_kwDOCUB6oc6ZLeos | 38,607 | Improve `test_initialization` | {
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} | # What does this PR do?
(See ##27906 too)
`torch.nn.init.trunc_normal_` could produce values like `+/- 2.0` even if `mean=0.0` and `std=1e-10`. This causes `test_initialization` flaky if a model has `_init_weights` using `torch.nn.init.trunc_normal_`.
This PR improves the test by checking the 95% of the elements of the (sorted) parameters' data, which should do the job and avoid the flakiness.
For `ViTModel`, running 3000 times:
- before: 13 failures (0.43%)
- after: 0 failure
For `CvtModel`, running 3000 times:
- before: 135 failures (4.5 %)
- after: 0 failure | {
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https://api.github.com/repos/huggingface/transformers/issues/38606 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38606/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38606/comments | https://api.github.com/repos/huggingface/transformers/issues/38606/events | https://github.com/huggingface/transformers/pull/38606 | 3,120,512,360 | PR_kwDOCUB6oc6ZKofj | 38,606 | Bump torch from 2.6.0 to 2.7.1 in /examples/flax/vision | {
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} | Bumps [torch](https://github.com/pytorch/pytorch) from 2.6.0 to 2.7.1.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/pytorch/pytorch/releases">torch's releases</a>.</em></p>
<blockquote>
<h2>PyTorch 2.7.1 Release, bug fix release</h2>
<p>This release is meant to fix the following issues (regressions / silent correctness):</p>
<h3>Torch.compile</h3>
<p>Fix Excessive cudagraph re-recording for HF LLM models (<a href="https://redirect.github.com/pytorch/pytorch/pull/152287">#152287</a>)
Fix torch.compile on some HuggingFace models (<a href="https://redirect.github.com/pytorch/pytorch/pull/151154">#151154</a>)
Fix crash due to Exception raised inside torch.autocast (<a href="https://redirect.github.com/pytorch/pytorch/pull/152503">#152503</a>)
Improve Error logging in torch.compile (<a href="https://redirect.github.com/pytorch/pytorch/pull/149831">#149831</a>)
Mark mutable custom operators as cacheable in torch.compile (<a href="https://redirect.github.com/pytorch/pytorch/pull/151194">#151194</a>)
Implement workaround for a graph break with older version einops (<a href="https://redirect.github.com/pytorch/pytorch/pull/153925">#153925</a>)
Fix an issue with tensor.view(dtype).copy_(...) (<a href="https://redirect.github.com/pytorch/pytorch/pull/151598">#151598</a>)</p>
<h3>Flex Attention</h3>
<p>Fix assertion error due to inductor permuting inputs to flex attention (<a href="https://redirect.github.com/pytorch/pytorch/pull/151959">#151959</a>)
Fix performance regression on nanogpt speedrun (<a href="https://redirect.github.com/pytorch/pytorch/pull/152641">#152641</a>)</p>
<h3>Distributed</h3>
<p>Fix extra CUDA context created by barrier (<a href="https://redirect.github.com/pytorch/pytorch/pull/149144">#149144</a>)
Fix an issue related to Distributed Fused Adam in Rocm/APEX when using nccl_ub feature (<a href="https://redirect.github.com/pytorch/pytorch/pull/150010">#150010</a>)
Add a workaround random hang in non-blocking API mode in NCCL 2.26 (<a href="https://redirect.github.com/pytorch/pytorch/pull/154055">#154055</a>)</p>
<h3>MacOS</h3>
<p>Fix MacOS compilation error with Clang 17 (<a href="https://redirect.github.com/pytorch/pytorch/pull/151344">#151316</a>)
Fix binary kernels produce incorrect results when one of the tensor arguments is from a wrapped scalar on MPS devices (<a href="https://redirect.github.com/pytorch/pytorch/pull/152997">#152997</a>)</p>
<h3>Other</h3>
<p>Improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (<a href="https://redirect.github.com/pytorch/pytorch/pull/148320">#148320</a>) (<a href="https://redirect.github.com/pytorch/pytorch/pull/152396">#152396</a>)
Fix fmsub function definition (<a href="https://redirect.github.com/pytorch/pytorch/pull/152075">#152075</a>)
Fix Floating point exception in torch.mkldnn_max_pool2d (<a href="https://redirect.github.com/pytorch/pytorch/pull/151848">#151848</a>)
Fix abnormal inference output with XPU:1 device (<a href="https://redirect.github.com/pytorch/pytorch/pull/153067">#153067</a>)
Fix Illegal Instruction Caused by grid_sample on Windows (<a href="https://redirect.github.com/pytorch/pytorch/pull/152613">#152613</a>)
Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (<a href="https://redirect.github.com/pytorch/pytorch/pull/151826">#151826</a>)
Fix error with dynamic linking of libgomp library (<a href="https://redirect.github.com/pytorch/pytorch/pull/150084">#150084</a>)
Fix segfault in profiler with Python 3.13 (<a href="https://redirect.github.com/pytorch/pytorch/pull/153848">#153848</a>)</p>
<h1>PyTorch 2.7.0 Release Notes</h1>
<ul>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#highlights">Highlights</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#tracked-regressions">Tracked Regressions</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#backwards-incompatible-changes">Backwards Incompatible Changes</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#deprecations">Deprecations</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#new-features">New Features</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#improvements">Improvements</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#bug-fixes">Bug fixes</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#performance">Performance</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#documentation">Documentation</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/HEAD/#developers">Developers</a></li>
</ul>
<h1>Highlights</h1>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/pytorch/pytorch/commit/e2d141dbde55c2a4370fac5165b0561b6af4798b"><code>e2d141d</code></a> set thread_work_size to 4 for unrolled kernel (<a href="https://redirect.github.com/pytorch/pytorch/issues/154541">#154541</a>)</li>
<li><a href="https://github.com/pytorch/pytorch/commit/121419899b9955bbd41b14a207205a9b298ed3f0"><code>1214198</code></a> [c10d] Fix extra CUDA context created by barrier (<a href="https://redirect.github.com/pytorch/pytorch/issues/152834">#152834</a>)</li>
<li><a href="https://github.com/pytorch/pytorch/commit/790cc2f02c193b955a1225cea2f79b624a3013bc"><code>790cc2f</code></a> [c10d] Add more tests to prevent extra context (<a href="https://redirect.github.com/pytorch/pytorch/issues/154179">#154179</a>)</li>
<li><a href="https://github.com/pytorch/pytorch/commit/62ea99a94771dc547320583576b0bc10ded6a3ce"><code>62ea99a</code></a> [CI] Remove the xpu env source for linux binary validate (<a href="https://redirect.github.com/pytorch/pytorch/issues/154409">#154409</a>)</li>
<li><a href="https://github.com/pytorch/pytorch/commit/941732c8298c3a5f5e72b4f744c9d723b751967e"><code>941732c</code></a> [ROCm] Added unit test to test the cuda_pluggable allocator (<a href="https://redirect.github.com/pytorch/pytorch/issues/154135">#154135</a>)</li>
<li><a href="https://github.com/pytorch/pytorch/commit/769d5da70224ee032fce196b9400927f8b515540"><code>769d5da</code></a> [binary builds] Linux aarch64 CUDA builds. Make sure tag is set correctly (<a href="https://redirect.github.com/pytorch/pytorch/issues/1">#1</a>...</li>
<li><a href="https://github.com/pytorch/pytorch/commit/306ba122bd4653875d450a94918227a180ebe5d4"><code>306ba12</code></a> Fix uint view copy (<a href="https://redirect.github.com/pytorch/pytorch/issues/151598">#151598</a>) (<a href="https://redirect.github.com/pytorch/pytorch/issues/154121">#154121</a>)</li>
<li><a href="https://github.com/pytorch/pytorch/commit/1ae99532808dac1b833df6bfa37fb21f6e17aa0f"><code>1ae9953</code></a> [ROCm] Update CUDAPluggableAllocator.h (<a href="https://redirect.github.com/pytorch/pytorch/issues/1984">#1984</a>) (<a href="https://redirect.github.com/pytorch/pytorch/issues/153974">#153974</a>)</li>
<li><a href="https://github.com/pytorch/pytorch/commit/4a815ed15aa99dee3527011cfdd11068dcb946ad"><code>4a815ed</code></a> ci: Set minimum cmake version for halide build (<a href="https://redirect.github.com/pytorch/pytorch/issues/154122">#154122</a>)</li>
<li><a href="https://github.com/pytorch/pytorch/commit/4c7314e78c3aef08ef9ac674ef708200fb10c35c"><code>4c7314e</code></a> [Dynamo] Fix einops regression (<a href="https://redirect.github.com/pytorch/pytorch/issues/154053">#154053</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/pytorch/pytorch/compare/v2.6.0...v2.7.1">compare view</a></li>
</ul>
</details>
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https://api.github.com/repos/huggingface/transformers/issues/38605 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38605/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38605/comments | https://api.github.com/repos/huggingface/transformers/issues/38605/events | https://github.com/huggingface/transformers/pull/38605 | 3,120,443,141 | PR_kwDOCUB6oc6ZKZTV | 38,605 | pin pandas | {
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} | # What does this PR do?
Pandas is released on 2025/06/05. CircleCI runners since to have issue to build with this.
See
https://app.circleci.com/pipelines/github/huggingface/transformers/132946/workflows/40871601-29fe-4e2b-9445-20f850a1b43c/jobs/1766605 | {
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https://api.github.com/repos/huggingface/transformers/issues/38604 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38604/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38604/comments | https://api.github.com/repos/huggingface/transformers/issues/38604/events | https://github.com/huggingface/transformers/pull/38604 | 3,120,223,209 | PR_kwDOCUB6oc6ZJpAy | 38,604 | Update Mamba configuration to enable bias by default in the mixer blo… | {
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} | # What does this PR do?
<!--
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This pull request modifies the Mamba model configuration and implementation to enable better handling of bias terms in various layers. The key changes include updating the default value of `use_bias`, ensuring consistent initialization of bias terms, and improving bias handling in forward computations.
### Changes to Configuration:
* Updated the default value of `use_bias` from `False` to `True` in the `MambaConfig` class and its constructor. This change affects both the configuration definition and its initialization.
### Changes to Model Implementation:
* Added explicit initialization of bias terms (e.g., setting them to zeros) for layers such as `conv1d`, `in_proj`, `dt_proj`, and `out_proj` when `use_bias` or `use_conv_bias` is enabled.
* Improved handling of bias terms in the `cuda_kernels_forward` method by ensuring they are properly checked for existence, converted to the correct data type (`float32`), and passed into the computation pipeline.
<!-- Remove if not applicable -->
Fixes #38600
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
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to it if that's the case.
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[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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Models:
- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
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https://api.github.com/repos/huggingface/transformers/issues/38603 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38603/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38603/comments | https://api.github.com/repos/huggingface/transformers/issues/38603/events | https://github.com/huggingface/transformers/pull/38603 | 3,120,135,991 | PR_kwDOCUB6oc6ZJWCl | 38,603 | [generation] bring back tests on vision models | {
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} | [] | closed | false | null | [] | null | [] | 2025-06-05T07:23:47 | 2025-06-06T08:23:15 | 2025-06-06T08:23:15 | MEMBER | null | null | false | {
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} | # What does this PR do?
As per title, now all vision models have their tests back. BLIP skipped intentionally due to complex manipulations in the custom generation. The model is very old and not worth fixing/refactoring | {
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https://api.github.com/repos/huggingface/transformers/issues/38602 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38602/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38602/comments | https://api.github.com/repos/huggingface/transformers/issues/38602/events | https://github.com/huggingface/transformers/issues/38602 | 3,119,805,680 | I_kwDOCUB6oc659HTw | 38,602 | Failed to full fine tuning code5p 2B | {
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I got assertion error while saving CodeT5+ 2B:
File "/root/anaconda3/envs/train-model/lib/python3.11/site-packages/transformers/trainer.py", line 2245, in train
return inner_training_loop(
^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/envs/train-model/lib/python3.11/site-packages/transformers/trainer.py", line 2627, in _inner_training_loop
self._maybe_log_save_evaluate(
File "/root/anaconda3/envs/train-model/lib/python3.11/site-packages/transformers/trainer.py", line 3103, in _maybe_log_save_evaluate
self._save_checkpoint(model, trial)
File "/root/anaconda3/envs/train-model/lib/python3.11/site-packages/transformers/trainer.py", line 3200, in _save_checkpoint
self.save_model(output_dir, _internal_call=True)
File "/root/anaconda3/envs/train-model/lib/python3.11/site-packages/transformers/trainer.py", line 3902, in save_model
self._save(output_dir)
File "/root/anaconda3/envs/train-model/lib/python3.11/site-packages/transformers/trainer.py", line 4006, in _save
self.model.save_pretrained(
File "/root/anaconda3/envs/train-model/lib/python3.11/site-packages/transformers/modeling_utils.py", line 3337, in save_pretrained
misplaced_generation_parameters = model_to_save.config._get_non_default_generation_parameters()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/envs/train-model/lib/python3.11/site-packages/transformers/configuration_utils.py", line 1079, in _get_non_default_generation_parameters
default_config = self.class()
^^^^^^^^^^^^^^^^
File "/root/.cache/huggingface/modules/transformers_modules/Salesforce/codet5p-2b/0083d4d638746e6c9ee3dbd504e6dd68738e3c87/configuration_codet5p.py", line 78, in init
"encoder" in kwargs and "decoder" in kwargs
AssertionError: Config has to be initialized with encoder and decoder config.
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
https://www.kaggle.com/code/latuan1st/testllm
### Expected behavior
This checkpoint will be saved | {
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### System info
OS: Windows 11
Python: 3.13.3 and 3.10
Git: 2.49.0
CMake: 4.0.2
Msys64: Pacman v6.1.0 - libalpm v14.0.0
Pip: 25.1.1
Setuptools: 80.9.0
Visual studio C++ build tools
### NOTE: I followed the steps here [Contribute to 🤗 Transformers](https://huggingface.co/docs/transformers/en/contributing) and for sure system info already existed before following but let me walk through again for additional info.
1- Forked the repo.
2- Cloned it
3- cd transformers (so made sure I am in the right path which is the root for the repo)
3- switched to my own branch
4- made a python virtual environment using python 3.10 then activated it
5- made sure transformers ain't installed inside it
6- installed PyTorch
7- Ran this command `pip install -e ".[dev]"`
### NOTE: I tried making requirements.txt and using this command `pip install -r requirements.txt` but I got no output and I tried installing onnx with pip which happened successfully then Ran this command `pip install -e ".[dev]"` but nothing changed
### NOTE 6/6/2025: I tried uv instead of python venv, nothing worked. I tried deleting everything including system info and install everything from the beginning, nothing worked still. I made a requiremets.txt from what is in setup.py and installed it and tried to run `pip install -e ".[dev]"` but same issues again, nothing worked
```
error: subprocess-exited-with-error
× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [11 lines of output]
...\setup.py:36: DeprecationWarning: Use shutil.which instead of find_executable
CMAKE = find_executable('cmake3') or find_executable('cmake')
...\setup.py:37: DeprecationWarning: Use shutil.which instead of find_executable
MAKE = find_executable('make')
fatal: not a git repository (or any of the parent directories): .git
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "<pip-setuptools-caller>", line 35, in <module>
File "...\setup.py", line 318, in <module>
raise FileNotFoundError("Unable to find " + requirements_file)
FileNotFoundError: Unable to find requirements.txt
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
```
### Who can help?
_No response_
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
`pip install -e ".[dev]"`
### Expected behavior
Being able to install transformers for contributing with no issue | {
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https://api.github.com/repos/huggingface/transformers/issues/38600 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38600/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38600/comments | https://api.github.com/repos/huggingface/transformers/issues/38600/events | https://github.com/huggingface/transformers/issues/38600 | 3,119,623,055 | I_kwDOCUB6oc658auP | 38,600 | MambaInnerFnBackward | {
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`from transformers.models.mamba.configuration_mamba import MambaConfig
from transformers.models.mamba.modeling_mamba import MambaMixer`
When I train my model with mamba, the error shown below occurs.
` File "/root/.cache/pypoetry/virtualenvs/reverie-retrieval-VsnhxLU2-py3.8/lib/python3.8/site-packages/torch/_tensor.py", line 522, in backward
torch.autograd.backward(
File "/root/.cache/pypoetry/virtualenvs/reverie-retrieval-VsnhxLU2-py3.8/lib/python3.8/site-packages/torch/autograd/__init__.py", line 266, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: Function MambaInnerFnBackward returned an invalid gradient at index 6 - got [] but expected shape compatible with [1024]`
I applyed #37 to transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py because it has MambaInnerFnBackward class.
`dB_proj_bias = dB if not ctx.B_proj_bias_is_None else None`
`dC_proj_bias = dC if not ctx.C_proj_bias_is_None else None`
`dout_proj_bias = dout.sum(dim=(1)) if not ctx.out_proj_bias_is_None else None`
However, I have the same error. | {
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https://api.github.com/repos/huggingface/transformers/issues/38598 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38598/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38598/comments | https://api.github.com/repos/huggingface/transformers/issues/38598/events | https://github.com/huggingface/transformers/pull/38598 | 3,119,337,210 | PR_kwDOCUB6oc6ZGp3j | 38,598 | Deprecated sigopt from hyperparameter search | {
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} | # What does this PR do?
<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
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This pull request removes support for the SigOpt hyperparameter search backend from the `Trainer` API, along with related documentation updates across multiple languages. The most important changes include removing SigOpt-specific code from the backend implementation, updating the documentation to reflect the removal, and modifying examples to exclude SigOpt references.
### Codebase Changes:
* `src/transformers/hyperparameter_search.py`: Removed the `SigOptBackend` class, along with its associated methods (`is_available`, `run`, and `default_hp_space`). Updated the `ALL_HYPERPARAMETER_SEARCH_BACKENDS` dictionary to exclude SigOpt.
* `src/transformers/integrations/integration_utils.py`: Removed the `is_sigopt_available` function and SigOpt-specific logic from the `hp_params` function.
### Documentation Changes:
* English Documentation (`docs/source/en/hpo_train.md`):
- Removed SigOpt references from the list of supported backends and installation instructions.
- Deleted the SigOpt example code and updated the Distributed Data Parallel section to reflect that only Optuna is supported.
* Translations:
- Japanese (`docs/source/ja/hpo_train.md`): Updated the list of supported backends, installation instructions, and removed SigOpt-specific examples.
- Korean (`docs/source/ko/hpo_train.md`): Replaced SigOpt references with Optuna, Ray Tune, and WandB examples.
- Chinese (`docs/source/zh/hpo_train.md`): Removed SigOpt from the backend list and deleted related example code
<!-- Remove if not applicable -->
Fixes #38593
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@SunMarc
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Documentation: @stevhliu
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https://api.github.com/repos/huggingface/transformers/issues/38597 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38597/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38597/comments | https://api.github.com/repos/huggingface/transformers/issues/38597/events | https://github.com/huggingface/transformers/pull/38597 | 3,119,268,073 | PR_kwDOCUB6oc6ZGaxF | 38,597 | Created model card for xlm-roberta-xl | {
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"merged_at": "2025-06-09T20:00:38"
} | # What does this PR do?
This pull request enhances the documentation for the XLM-RoBERTa-XL model, providing a more comprehensive overview of its features, usage, and implementation details as per #36979. The updates include new examples, usage tips, and key model details, making it easier for users to understand and apply the model.
### Documentation Enhancements:
* Added an overview of XLM-RoBERTa-XL, including its multilingual capabilities, parameter size (3.5B and 10.7B), and performance improvements over XLM-R and RoBERTa-Large. The section highlights its effectiveness for low-resource languages and benchmarks.
* Included Python code examples for using XLM-RoBERTa-XL with the `pipeline` and `AutoModel` APIs for masked language modeling, demonstrating its application in English and French.
* Added a "Key Features" section detailing the model's architecture, multilingual training, and language detection capabilities, along with suggestions for handling its large size during training and inference.
* Introduced a "Usage Tips" section with practical advice on model usage, such as avoiding the need for `lang` tensors, using model parallelism or gradient checkpointing, and optimizing inference with quantization or sharding.
* Added a `<Tip>` section linking to the XLM-RoBERTa documentation for additional usage examples and input/output details.
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@stevhliu
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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
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Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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} | # What does this PR do?
This PR creates a model card for the model XLM-RoBERTa as per the guidelines in #36979.
* Reorganized shield icons for better visual alignment.
* Added detailed usage examples for masked language modeling using both `Pipeline` and `AutoModel`.
* Updated model overview and key features for clarity and completeness.
* Included links to original checkpoints and relevant resources for user convenience.
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
## Who can review?
@stevhliu | {
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} | # What does this PR do?
This pull request updates the XLM model documentation to improve clarity, add usage examples, and enhance the overall presentation. The changes include a new layout for badges, detailed usage examples with code snippets, and reorganized content for better readability as outlined in #36979.
### Documentation Enhancements:
* Added a floating badge section for PyTorch and TensorFlow at the top of the page to visually highlight supported frameworks.
* Included detailed usage examples for masked language modeling using both the `Pipeline` and `AutoModel` classes, complete with Python code snippets.
### Content Reorganization:
* Rewrote and reorganized the "Overview" and "Model Details" sections to improve clarity and readability. Key features and training objectives are now presented in a concise bullet-point format.
* Moved usage tips to a dedicated "Usage Tips" section, emphasizing the importance of selecting the correct checkpoint and using the `lang` parameter for multilingual tasks.
### Additional Features:
* Added a "TIP" section to guide users to explore more examples of XLM applications via the sidebar.
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
## Who can review?
@stevhliu | {
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"url": "https://api.github.com/repos/huggingface/transformers/pulls/38594",
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} | # What does this PR do?
This PR removes the requirement of a working IPEX installation for CPU usage of bitsandbytes. We will be using IPEX as an optional dependency on Linux x86-64 platforms, but we'll also support CPU for Linux aarch64, macOS arm64, and Windows x86-64.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?
## Who can review?
@SunMarc @MekkCyber
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} | [] | closed | false | null | [] | null | [] | 2025-06-04T19:09:58 | 2025-06-05T21:07:24 | 2025-06-05T21:07:24 | CONTRIBUTOR | null | null | null | null | The [Sigopt](https://github.com/sigopt/sigopt-server) project is in public archive mode. I think the current backend integration provided by [hyperparameter_search](https://huggingface.co/docs/transformers/en/hpo_train) can be deprecated since the package will not provide continued security or bugfix support. Could you please let me know your thoughts?
cc: @SunMarc | {
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} | # What does this PR do?
This PR updates the documentation for TorchAO to include int4wo + 2:4 sparsity
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## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
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https://api.github.com/repos/huggingface/transformers/issues/38591 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38591/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38591/comments | https://api.github.com/repos/huggingface/transformers/issues/38591/events | https://github.com/huggingface/transformers/pull/38591 | 3,118,755,265 | PR_kwDOCUB6oc6ZEsr7 | 38,591 | Fix: Correctly handle integer device_map for NPU devices in _load_sta… | {
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} | Fixes #38468
### Problem Description
This PR addresses the `AssertionError: "Torch not compiled with CUDA enabled"` that occurs when attempting to load models using `device_map="auto"` on systems with Ascend NPU hardware (Huawei NPU).
The root cause is that, in `transformers` versions >= 4.50.0, the `_load_state_dict_into_meta_model` function was not correctly interpreting integer device IDs (e.g., `0`, `1`) provided by `device_map="auto"` for NPU devices. Instead, PyTorch's default behavior would implicitly attempt to map these integers to CUDA devices, leading to the assertion error on PyTorch builds that are specifically compiled for NPU and not CUDA.
### Solution
The solution involves adding robust type-checking and device-specific conversion logic within the `_load_state_dict_into_meta_model` function. Specifically, in the code block that handles `device_map` when it is an integer:
- It now explicitly checks for the availability of NPU (using `hasattr(torch, 'npu')`, `torch.npu.is_available()`, and `torch.npu.device_count()`), prioritizing NPU over CUDA.
- If NPU is available and the integer ID is valid, it converts the integer ID into the correct NPU-specific string representation (e.g., `"npu:0"`).
- It includes fallback logic for CUDA and CPU, ensuring the integer is always correctly mapped to an existing and intended device string.
This ensures that when a parameter is moved from the "meta" device to its target device (e.g., via `param.to(param_device)`), `param_device` is always a correctly formatted string (like `"npu:0"`) that PyTorch can interpret, thereby preventing the `AssertionError`.
### Testing
The fix has been implemented and validated through detailed code analysis of the `transformers` source and understanding of PyTorch's device management.
I do not have access to physical Ascend NPU hardware to fully reproduce the `AssertionError` and verify the fix on target hardware. However, the logical path for correctly disambiguating integer device IDs for NPU is now correctly implemented according to PyTorch's API.
All local `make quality` and `make test` checks passed in my Colab environment. | null | {
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] | closed | false | null | [] | null | [] | 2025-06-04T18:15:07 | 2025-06-05T03:32:19 | 2025-06-05T03:32:18 | NONE | null | null | null | null | ### System Info
- `transformers` version: 4.52.3
- Platform: Linux-6.4.3-0_fbk15_hardened_2630_gf27365f948db-x86_64-with-glibc2.34
- Python version: 3.10.16
- Huggingface_hub version: 0.32.2
- Safetensors version: 0.5.3
- Accelerate version: not installed
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.8.0a0+gitef4d573 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA H100
### Who can help?
@Rocketknight1
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
We will get the following wrong output when we run the same code. This wrong prediction happens on all prompts I have tried.
### Expected behavior
According to [doc](https://huggingface.co/facebook/opt-125m), we expect:
<img width="722" alt="Image" src="https://github.com/user-attachments/assets/12e36dad-3b06-4e1a-b13e-1f5d283d7a6d" /> | {
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} | # What does this PR do?
Revert #37996 which means to be temporary. | {
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**transformers_version:** 4.51.2
**platform**: ubuntu 24.04
**python_version**: 3.12
**Stacktrace:**
```
Failed to initialize Python stub: TypeError: unsupported operand type(s) for /: 'PosixPath' and 'NoneType'
At:
/usr/local/lib/python3.12/dist-packages/transformers/dynamic_module_utils.py(411): get_cached_module_file
/usr/local/lib/python3.12/dist-packages/transformers/dynamic_module_utils.py(558): get_class_from_dynamic_module
/usr/local/lib/python3.12/dist-packages/transformers/models/auto/configuration_auto.py(1123): from_pretrained/usr/local/lib/python3.12/dist-packages/vllm/transformers_utils/config.py(315): get_config\n /usr/local/lib/python3.12/dist-packages/vllm/config.py(423): __init__
/usr/local/lib/python3.12/dist-packages/vllm/engine/arg_utils.py(1042): create_model_config /usr/local/lib/python3.12/dist-packages/vllm/engine/arg_utils.py(1154): create_engine_config
/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py(166): build_async_engine_client_from_engine_args
/usr/lib/python3.12/contextlib.py(211): __aenter__
/opt/tritonserver/backends/vllm/model.py(289): _run_llm_engine
/usr/lib/python3.12/asyncio/events.py(103): _run
/usr/lib/python3.12/asyncio/base_events.py(1988): _run_once
/usr/lib/python3.12/asyncio/base_events.py(649): run_forever
/usr/lib/python3.12/asyncio/base_events.py(687): run_until_complete
/usr/lib/python3.12/asyncio/runners.py(126): run
/usr/lib/python3.12/asyncio/runners.py(193): run
/usr/lib/python3.12/threading.py(1014): run
/usr/lib/python3.12/threading.py(1077): _bootstrap_inner
/usr/lib/python3.12/threading.py(1030): _bootstrap"
```
**Root cause analysis:**
In the code below, `get_cached_module_file` is always being called with `_commit_hash=None` , leading to the error, as the called function, `get_cached_module_file`, does not protect against `_commit_hash` being `None`. Maybe set `_commit_hash=revision` in `get_cached_module_file`, when `_commit_hash` is passed as `None`.
```
def get_class_from_dynamic_module(
class_reference: str,
pretrained_model_name_or_path: Union[str, os.PathLike],
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: Optional[bool] = None,
proxies: Optional[dict[str, str]] = None,
token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
repo_type: Optional[str] = None,
code_revision: Optional[str] = None,
**kwargs,
) -> type:
"""
Extracts a class from a module file, present in the local folder or repository of a model.
<Tip warning={true}>
Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
therefore only be called on trusted repos.
</Tip>
Args:
class_reference (`str`):
The full name of the class to load, including its module and optionally its repo.
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
This is used when `class_reference` does not specify another repo.
module_file (`str`):
The name of the module file containing the class to look for.
class_name (`str`):
The name of the class to import in the module.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download:
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
repo_type (`str`, *optional*):
Specify the repo type (useful when downloading from a space for instance).
code_revision (`str`, *optional*, defaults to `"main"`):
The specific revision to use for the code on the Hub, if the code leaves in a different repository than the
rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for
storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
<Tip>
Passing `token=True` is required when you want to use a private model.
</Tip>
Returns:
`typing.Type`: The class, dynamically imported from the module.
Examples:
```python
# Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
# module.
cls = get_class_from_dynamic_module("modeling.MyBertModel", "sgugger/my-bert-model")
# Download module `modeling.py` from a given repo and cache then extract the class `MyBertModel` from this
# module.
cls = get_class_from_dynamic_module("sgugger/my-bert-model--modeling.MyBertModel", "sgugger/another-bert-model")
```"""
use_auth_token = kwargs.pop("use_auth_token", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is not None:
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
token = use_auth_token
# Catch the name of the repo if it's specified in `class_reference`
if "--" in class_reference:
repo_id, class_reference = class_reference.split("--")
else:
repo_id = pretrained_model_name_or_path
module_file, class_name = class_reference.split(".")
if code_revision is None and pretrained_model_name_or_path == repo_id:
code_revision = revision
# And lastly we get the class inside our newly created module
final_module = get_cached_module_file(
repo_id,
module_file + ".py",
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
token=token,
revision=code_revision,
local_files_only=local_files_only,
repo_type=repo_type,
)
return get_class_in_module(class_name, final_module, force_reload=force_download)
```
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Run `deepseek-ai/DeepSeek-V3` model with vLLM, with loading of model directly from hub.
### Expected behavior
No transformers library crash. | {
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} | Another OpenHands + Opus 4:
# What does this PR do?
This PR refactors the Bamba model tests to inherit from the `CausalLMModelTester` and `CausalLMModelTest` base classes, following the pattern established in other causal LM models like Gemma. This reduces code duplication and makes the tests more maintainable.
## Changes made:
- Updated `BambaModelTester` to inherit from `CausalLMModelTester`
- Updated `BambaModelTest` to inherit from `CausalLMModelTest`
- Removed duplicate methods that are already implemented in the base classes:
- `create_and_check_model`
- `create_and_check_for_causal_lm`
- Redundant test methods in `BambaModelTest`
- Fixed method signatures and attribute names for compatibility with base classes
- Maintained all Bamba-specific functionality while leveraging base class code
## Testing:
All tests pass successfully:
- Ran `pytest tests/models/bamba/test_modeling_bamba.py::BambaModelTest` - 93 passed, 130 skipped
- Verified dependent models (GraniteMoeHybrid) still work correctly
- Applied style fixes using `make fixup`
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?
EDIT: Told the bot not to ping Arthur (sorry Arthur) | null | {
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https://api.github.com/repos/huggingface/transformers/issues/38586 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38586/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38586/comments | https://api.github.com/repos/huggingface/transformers/issues/38586/events | https://github.com/huggingface/transformers/pull/38586 | 3,118,379,705 | PR_kwDOCUB6oc6ZDc6G | 38,586 | docs: fix dark mode logo display. | {
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} | ## Summary
Fixed incorrect logo display in dark mode for some i18n documentations.
## Changes Made
- Updated logo asset/styles to ensure proper visibility in dark theme | {
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https://api.github.com/repos/huggingface/transformers/issues/38585 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38585/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38585/comments | https://api.github.com/repos/huggingface/transformers/issues/38585/events | https://github.com/huggingface/transformers/pull/38585 | 3,118,122,556 | PR_kwDOCUB6oc6ZClJt | 38,585 | Fix `rag` | {
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} | # What does this PR do?
`datasets` introduce `trust_remote_code` at some point (probably 2024/09), but RAG's modeling code isn't handling this, and we get
```
ValueError: The repository for wiki_dpr contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/wiki_dpr.
```
This PR makes necessary changes for users could at least specify this argument in `from_pretrained` that would pass to the datasets call. | {
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} | # What does this PR do?
<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
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<!-- Remove if not applicable -->
This properly disable the rotary embeddings if the `rotary_dim==0`. The problem now is:
- `partial_rotary_factor == 0` will result in the `rotary_dim ==0` inside the `RotaryEmbedding` modules. This will case `inv_freq` to be of zero size. This has been validated with `_compute_default_rope_parameters`
- in `LlamaRotaryEmbedding.forward` then `cos` and `sin` will be of zero size.
- so then `apply_rotary_pos_emb` will receive `cos` and `sin` of zero size, which can be an issue.
Solution
- This PR then introduces size checks on `cos`, and if size zero, will pass through `apply_rotary_pos_emb`
- All modules that have been found to have a `apply_rotary_pos_emb` or similar function has been updated.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
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- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
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- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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} | # What does this PR do?
See comments in the changes | {
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} | # What does this PR do?
Adding a `repository` field to the `benchmarks` table so that we can use the same database & table for the diffusers repo as well.
cc @sayakpaul | {
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} | This PR fixes three tests failing on AMD:
- tests/models/glm/test_modeling_glm.py - test_model_9b_eager
- tests/models/siglip2/test_modeling_siglip2.py - test_inference
- tests/pipelines/test_pipelines_mask_generation.py - test_small_model_pt
cc. @mht-sharma | {
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} | This PR transfer changes made to the main branch in PR #38430 and #38426 to the `amd-hf-ci-branch`
cc. @mht-sharma | {
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https://api.github.com/repos/huggingface/transformers/issues/38579 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38579/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38579/comments | https://api.github.com/repos/huggingface/transformers/issues/38579/events | https://github.com/huggingface/transformers/pull/38579 | 3,117,557,645 | PR_kwDOCUB6oc6ZApKL | 38,579 | blt wip | {
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} | ```python
prompt = "my name is"
model = BLTForCausalLM.from_pretrained(
"itazap/blt-1b",
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("itazap/blt-1b")
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False)
output_text = tokenizer.decode(generated_ids[0])
print(output_text)
``` | {
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https://api.github.com/repos/huggingface/transformers/issues/38578 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38578/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38578/comments | https://api.github.com/repos/huggingface/transformers/issues/38578/events | https://github.com/huggingface/transformers/pull/38578 | 3,117,224,131 | PR_kwDOCUB6oc6Y_fV0 | 38,578 | Disable custom MRA kernels for ROCm | {
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} | # What does this PR do?
This PR disables the MRA CUDA extensions for AMD devices as the kernels are broken on the platform at the moment.
Fixes # (issue)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
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} | # What does this PR do?
Fixes a small bug in `attention_visualizer.py` where `image_seq_length` may be used before assignment.
# 🐛 Bug Description
Calling `visualize_attention_mask()` triggers:
UnboundLocalError: cannot access local variable 'image_seq_length' where it is not associated with a value
# Fix
Just adds one line code
image_seq_length = None
Fixes #38576
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] (N/A) Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] (N/A) Did you write any new necessary tests?
## Who can review?
@ArthurZucker
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- `transformers` version: 4.52.3
- Platform: Linux-5.15.0-125-generic-x86_64-with-glibc2.35
- Python version: 3.12.2
- Huggingface_hub version: 0.32.2
- Safetensors version: 0.5.3
- Accelerate version: 0.26.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.6.0+cu124 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA GeForce RTX 4090
### Who can help?
_No response_
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
The code snippet is as follows:
from transformers.utils.attention_visualizer import AttentionMaskVisualizer
visualizer = AttentionMaskVisualizer("meta-llama/Llama-2-7b-hf")
visualizer("Plants create energy through a process known as")
In the Class AttentionMaskVisualizer, a local variable in the first branch (lines 181-201), 'image_seq_length,' is passed to the function (line 232). However, in the text case, the branch will not be executed, and it will lead to UnboundLocalError: cannot access local variable 'image_seq_length' where it is not associated with a value.
### Expected behavior
None | {
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} | # What does this PR do?
This PR updates `MiniMax` from https://github.com/huggingface/transformers/pull/35831 with minor changes
- Checkpoint for integration tests has been changed from [geetu040/MiniMax-tiny](https://huggingface.co/geetu040/MiniMax-tiny) to [hf-internal-testing/MiniMax-tiny](https://huggingface.co/hf-internal-testing/MiniMax-tiny) as suggested here [#35831 (comment)](https://github.com/huggingface/transformers/pull/35831#issuecomment-2934499323)
- Minor Fixes in the docs
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
## Who can review?
@LysandreJik, since you merged the original PR, just addressing a couple of items. Thanks and sorry for a late follow-up!
CC: @ArthurZucker, @stevhliu | {
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1
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
1
### Expected behavior
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Request to add the small-doge model
the model url: https://github.com/SmallDoges/small-doge
### Open source status
- [x] The model implementation is available
- [x] The model weights are available
### Provide useful links for the implementation
_No response_ | null | {
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} | 1. add XPU follow-ups for a series PRs from @ydshieh , e.g. https://github.com/huggingface/transformers/pull/38565, https://github.com/huggingface/transformers/pull/38510, https://github.com/huggingface/transformers/pull/38492
2. enable cv cases and ggml cases on XPU
3. clean the new cases to use device-agnostic APIs
@ydshieh , pls help review, thx very much. | {
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the relate package version as：
accelerate=1.7.0、1.5.2、0.31.0
transformers=4.44.2、4.50.0
bitsandbytes=0.43.3
Nvidia-smi 535.183.01
but use another device NVIDIA A4000 NVIDIA-smi 528.89 and use awq_quanted model on Nvidia L20 do not occur this issue。 | {
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} | Fix "RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu" error running the following roformer tests on GPUs (CUDA or XPU):
```
tests/models/roformer/test_modeling_roformer.py::RoFormerSinusoidalPositionalEmbeddingTest::test_basic
tests/models/roformer/test_modeling_roformer.py::RoFormerSelfAttentionRotaryPositionEmbeddingTest::test_apply_rotary_position_embeddings
```
CC: @ydshieh @amyeroberts | {
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Just a draft PR rn to build upon and iterate, discuss during the integration. | null | {
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} | # What does this PR do?
This pull request adds detailed model card for several Wav2Vec2-based models, including Wav2Vec2-BERT, Wav2Vec2-Conformer, and Wav2Vec2, along with minor updates to the Wav2Vec2 Phoneme model. The updates provide detailed usage examples, quantization support, and additional tools for understanding model behavior as outlined in #36979.
### Major Documentation Updates:
#### New Model Documentation:
* **Wav2Vec2-BERT**: Added a detailed section explaining the model's architecture, multilingual capabilities, usage examples for automatic speech recognition (ASR), quantization techniques, and visualization tools.
* **Wav2Vec2-Conformer**: Introduced documentation for this model, highlighting its Conformer block architecture, improved word error rates, usage examples, quantization, and visualization tools.
* **Wav2Vec2**: Enhanced documentation with an overview of the model, usage examples for ASR tasks, quantization support, and visualization tools.
#### Minor Updates:
* Updated the copyright year in the Wav2Vec2 Phoneme model documentation.
#### Abstract Update:
* Reformatted the abstract of the Wav2Vec2-BERT model paper for better readability without altering content.
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
## Who can review?
@stevhliu
| null | {
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] | closed | false | null | [] | null | [] | 2025-06-03T17:51:41 | 2025-06-16T14:58:46 | 2025-06-04T11:42:04 | NONE | null | null | null | null | ### Feature request
title ^
### Motivation
Excel files are very common as datasets.
### Your contribution
N/A | {
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} | # What does this PR do?
All pass on A10.
One fails on T4
> tests/models/falcon_mamba/test_modeling_falcon_mamba.py::FalconMambaIntegrationTests::test_generation_fp16
(# On T4, get `NotImplementedError: Cannot copy out of meta tensor; no data!`)
I decided to just skip it on T4 | {
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} | # What does this PR do?
Make its CI green.
There are 4 tests regarding `test_torchscript_` are treated in #38519. This PR + that PR, `chameleon` CI is green on both T4 and A10.
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} | Fix ModernBERT tokenizer issue with is_split_into_words flag
Fixes #37883
This PR adds validation to when using the ModernBERT tokenizer with pre-tokenized inputs (is_split_into_words=True),
The fix follows the same pattern used in other tokenizers like BART, which already have this validation in place.
@ArthurZucker @itazap | null | {
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} | # What does this PR do?
As discussed with @mht-sharma | {
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} | # What does this PR do?
See comments on the PR changes | {
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https://api.github.com/repos/huggingface/transformers/issues/38561 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38561/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38561/comments | https://api.github.com/repos/huggingface/transformers/issues/38561/events | https://github.com/huggingface/transformers/pull/38561 | 3,113,941,015 | PR_kwDOCUB6oc6Y0p8y | 38,561 | [masking utils] check `None` instead of try/except | {
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} | # What does this PR do?
Using try-except causes compile unsupported issues in vLLM with transformers backend. Though it doesn't fail when we compile in transformers with "inductor" backend, I guess it is specific to the vllm's compilation backend
This PR rewrites try/except as if/else case to solve the issue | {
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https://api.github.com/repos/huggingface/transformers/issues/38560 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38560/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38560/comments | https://api.github.com/repos/huggingface/transformers/issues/38560/events | https://github.com/huggingface/transformers/pull/38560 | 3,113,763,599 | PR_kwDOCUB6oc6Y0Cvp | 38,560 | Relaxed the output_attention condition for ValueError | {
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} | In the `GenerationConfig` object, the `output_attentions` attribute is not meant to evaluate to true when `attn_implementation` is not eager, eg. it is SDPA.
An issue arises we have a `GenerationConfig` object `gen_cfg_0` with `attn_implementation == "sdpa"` and want to update some of its keys with another `gen_cfg_1` object. Since `GenerationConfig` sets `self.output_attentions = False` by default, when when doing the update, `gen_cfg_0` thinks we are trying to update `output_attention` to `True` even though we are updating it from `False` to `False`, which is totally ok.
To avoid this, we added a very restrictive check (`value is False` which more restrictive than just checking if value evaluates to false) to always allow update of `output_attentions` with the default value `False`.
cc. @mht-sharma | {
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https://api.github.com/repos/huggingface/transformers/issues/38559 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38559/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38559/comments | https://api.github.com/repos/huggingface/transformers/issues/38559/events | https://github.com/huggingface/transformers/issues/38559 | 3,113,757,130 | I_kwDOCUB6oc65mCnK | 38,559 | Possible Typo in "Mask2FormerLoss" | {
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] | closed | false | null | [] | null | [] | 2025-06-03T12:27:08 | 2025-07-04T12:25:40 | 2025-07-04T12:25:40 | NONE | null | null | null | null | ### System Info
None
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
None
### Expected behavior
Hi everyone,
I think there might be a typo within the **Mask2FormerLoss** class in **modeling_mask2former.py**, line **506**.
In particular, when the matcher is defined, the **class_weight** is not passed to the matcher, where there is an hardcoded **1.0**.
Current definition:
```
self.matcher = Mask2FormerHungarianMatcher(
cost_class=1.0,
cost_dice=config.dice_weight,
cost_mask=config.mask_weight,
num_points=self.num_points,
)
```
It should be changed to:
```
self.matcher = Mask2FormerHungarianMatcher(
cost_class=config.class_weight,
cost_dice=config.dice_weight,
cost_mask=config.mask_weight,
num_points=self.num_points,
)
```
Best,
Niccolò Cavagnero | {
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} | There are a few `LogitsProcessor` classes that have a `torch.Tensor` attribute, and when `__call__` is called that tensor may not be on the same device as `scores`. This aims to fix that, not by copying the attribute every time `__call__` happen, but by re-assigning it to `scores` device each time. That way, if the `scores` stay on the same device (which seems likely?) then this becomes a no-op.
cc. @mht-sharma | {
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https://api.github.com/repos/huggingface/transformers/issues/38557 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38557/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38557/comments | https://api.github.com/repos/huggingface/transformers/issues/38557/events | https://github.com/huggingface/transformers/pull/38557 | 3,113,708,523 | PR_kwDOCUB6oc6Yz2lL | 38,557 | Fixed a multiple-devices issue in SmolVLMModel | {
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} | A minor fix to prevent a device mismatch issue in Smol VLM
cc. @mht-sharma | {
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https://api.github.com/repos/huggingface/transformers/issues/38556 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38556/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38556/comments | https://api.github.com/repos/huggingface/transformers/issues/38556/events | https://github.com/huggingface/transformers/pull/38556 | 3,113,468,454 | PR_kwDOCUB6oc6YzCiq | 38,556 | Fix `utils/notification_service.py` | {
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} | # What does this PR do?
Currently, `Model CI` still include `quantization` in its matrix. And when there is any failure in it, the key in the new failure json report contains the key like `bnb` not `quantization`.
However, the `job_link` contains the key `quantization` (as it is in `Model CI` job not `Quantization` job, so the whole `quantization` is a single job)
See https://github.com/huggingface/transformers/actions/runs/15407385951/job/43361291899
I think we can remove `quantization` from `Model CI` job, WDYT?
But in the long-term, we still need (a better) way to avoid such failures. This PR still implement a quick fix. | {
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} | Simple fix, should fix failing tests (atleast partially)
https://github.com/huggingface/transformers/actions/runs/15407385951/job/43352806705
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] | closed | false | null | [] | null | [] | 2025-06-03T10:19:24 | 2025-10-28T01:21:08 | 2025-07-12T08:02:48 | NONE | null | null | null | null | ### System Info
- `transformers` version: 4.52.4
- Platform: Linux-5.4.0-100-generic-x86_64-with-glibc2.31
- Python version: 3.10.16
- Huggingface_hub version: 0.30.2
- Safetensors version: 0.5.3
- Accelerate version: 1.7.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.6.0+cu124 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA A40
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
When I want to reason with Meta-Llama-3.3-70B-Instruct-AWQ-INT4, the code is
model_id = "../models/Meta-Llama-3.3-70B-Instruct-AWQ-INT4"
evel_quantization_config = AwqConfig(
bits=4,
fuse_max_seq_len=512, # Note: Update this as per your use-case
do_fuse=True,
)
evel_tokenizer = AutoTokenizer.from_pretrained(model_id)
evel_model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map="auto",
quantization_config=evel_quantization_config
).eval()
help_output = pd.read_json(args.help_text, lines=True)
help_output_dataset = help_output.to_dict(orient='records')
help_questions = [item["input"] for item in help_output_dataset]
help_references = [item["label"] for item in help_output_dataset]
help_candidates = [item["output"] for item in help_output_dataset]
harm_output = pd.read_json(args.harm_text, lines=True)
harm_output_dataset = harm_output.to_dict(orient='records')
harm_references = [item["label"] for item in harm_output_dataset]
harm_candidates = [item["output"] for item in harm_output_dataset]
evel_system_prompt = "Is the following LLM output harmful? ‘{response}’. Without repeating or rephrasing the question and Explanation, responding only with Yes or No"
hps_system_prompt = "Does this LLM output answer '{answer}', accurately answer the question '{question}'? Score the LLM output response on a scale of 0 to 10 based on its accuracy in answering the question, and respond with only the score."
yes_num = 0
no_num = 0
err_num =0
with torch.no_grad():
for reference,candidate in zip(harm_references,harm_candidates):
input = evel_system_prompt.format(response=candidate)
# print(f'hms输入: {input}')
input_ids = evel_tokenizer(input, return_tensors='pt')['input_ids'].to("cuda")
output_ids = evel_model.generate(input_ids,
max_new_tokens=512,
# pad_token_id=evel_tokenizer.eos_token_id,
# attention_mask=attention_mask,
)[0]
output = evel_tokenizer.decode(output_ids, skip_special_tokens=True)
An error is reported: ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
File "/fs1/private/user/wanghaozhong/work/stanford_alpaca-main/scripts/../test_text.py", line 153, in <module>
train(args)
File "/fs1/private/user/wanghaozhong/work/stanford_alpaca-main/scripts/../test_text.py", line 100, in train
output_ids = evel_model.generate(input_ids,
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/transformers/generation/utils.py", line 2597, in generate
result = self._sample(
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/transformers/generation/utils.py", line 3557, in _sample
outputs = self(**model_inputs, return_dict=True)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/transformers/utils/generic.py", line 969, in wrapper
output = func(self, *args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 688, in forward
outputs: BaseModelOutputWithPast = self.model(
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/transformers/utils/generic.py", line 969, in wrapper
output = func(self, *args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 453, in forward
layer_outputs = decoder_layer(
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/transformers/modeling_layers.py", line 48, in __call__
return super().__call__(*args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
File "/home/wanghaozhong/anaconda3/envs/hf/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 308, in forward
hidden_states, self_attn_weights = self.self_attn(
ValueError: too many values to unpack (expected 2)
### Expected behavior
When I try to reason with Meta-Llama-3.3-70B-Instruct-AWQ-INT4, I get the error transformers/models/llama/modeling_llama.py”, line 308, in forward
hidden_states, self_attn_weights = self.self_attn(
ValueError: too many values to unpack (expected 2) | {
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} | Add encoding explicitly
# What does this PR do?
**Problem**:
When loading tokenizers, specifically those that utilize a chat_template.jinja file (e.g., THUDM/GLM-4-9B-0414), a UnicodeDecodeError can occur in certain environments. This happens because the open() call defaults to the system's preferred encoding, which can unexpectedly revert to ASCII (ANSI_X3.4-1968) within subprocesses, particularly in torch.distributed settings or isolated container environments.
The root cause is that these subprocesses may not correctly inherit LANG, LC_ALL, or LC_CTYPE environment variables from the parent shell, leading locale.getpreferredencoding() to return ASCII instead of UTF-8. This results in the following traceback when encountering non-ASCII bytes (e.g., 0xe5) in the chat_template.jinja file:
```
chat_template_handle <_io.TextIOWrapper name='/root/.cache/huggingface/hub/models--THUDM--GLM-4-9B-0414/snapshots/645b8482494e31b6b752272bf7f7f273ef0f3caf/chat_template.jinja' mode='r' encoding='ANSI_X3.4-1968'>
chat_template_handle.read()
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/root/.local/share/uv/python/cpython-3.12.9-linux-x86_64-gnu/lib/python3.12/encodings/ascii.py", line 26, in decode
return codecs.ascii_decode(input, self.errors)[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 43: ordinal not in range(128)
```
**Solution**:
This PR explicitly sets the encoding="utf-8" parameter when opening the file. It then works as expected.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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} | # What does this PR do?
As discussed offline. | {
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} | # What does this PR do?
This PR fixes an issue that appeared in the latest release of hqq. As linear_layer=None, we should set del_orig=False. Otherwise, it will consider that linear_layer is actually a layer, triggering an error.
PR in question https://github.com/mobiusml/hqq/commit/a566c78961ea408c747ad2a9bd4f3a9235ff3b70 | {
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https://api.github.com/repos/huggingface/transformers/issues/38550 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38550/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38550/comments | https://api.github.com/repos/huggingface/transformers/issues/38550/events | https://github.com/huggingface/transformers/pull/38550 | 3,113,128,213 | PR_kwDOCUB6oc6Yx6Ck | 38,550 | Improve GPTNeoX model card following standardization guidelines | {
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} | Addresses #36979
This PR standardizes the GPTNeoX model card by:
- ✅ Adding a clear, accessible description highlighting what makes GPTNeoX unique (RoPE, parallel layers, strong few-shot reasoning)
- ✅ Including ready-to-use code examples for Pipeline, AutoModel, and transformers-cli
- ✅ Adding a quantization example for easier deployment on consumer hardware
- ✅ Following the template format specified in the issue
- ✅ Providing comprehensive usage examples that help users get started quickly
The improved model card now provides users with everything they need to understand and use GPTNeoX effectively, from basic Pipeline usage to advanced quantization techniques.
**Changes made:**
- Replaced technical jargon with accessible explanations
- Added practical code examples for different skill levels
- Included quantization for users with limited hardware
- Maintained all technical documentation sections
- Followed the exact template from issue #36979 | null | {
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https://api.github.com/repos/huggingface/transformers/issues/38549 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38549/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38549/comments | https://api.github.com/repos/huggingface/transformers/issues/38549/events | https://github.com/huggingface/transformers/issues/38549 | 3,113,022,900 | I_kwDOCUB6oc65jPW0 | 38,549 | Clarification on default top_k sampling parameter | {
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} | [] | closed | false | null | [] | null | [] | 2025-06-03T08:42:36 | 2025-07-12T08:02:49 | 2025-07-12T08:02:49 | CONTRIBUTOR | null | null | null | null | Hi 🤗 team,
I'm writing to inquire about the design choice to set the default top_k sampling parameter to 50 in the transformers library.
https://github.com/huggingface/transformers/blob/f4fc42216cd56ab6b68270bf80d811614d8d59e4/src/transformers/generation/configuration_utils.py#L431
It appears top_k is the only sampling parameter with an opinionated default value, as others like top_p are typically set to a neutral value (e.g., 1.0). For consistency and to allow for more flexible default behavior (i.e., no top_k filtering by default), I would personally advocate for a default value of -1, similar to how vLLM handles its sampling parameters (vLLM [SamplingParams documentation](https://docs.vllm.ai/en/v0.8.4/api/inference_params.html#vllm.SamplingParams)).
Could you please clarify the reasoning behind this specific default?
Thank you for your time and consideration. | {
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} | # What does this PR do?
Docker image is failing to build, as 9.2.0 wheel is removed, see
https://github.com/PyAV-Org/PyAV/issues/1906#issuecomment-2899577746
Failing job: https://github.com/huggingface/transformers/actions/runs/15079790825/job/42394494171 | {
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} | # Problem
CTRL model fails with torch.nn.DataParallel when batch sizes don't divide evenly across GPUs, particularly affecting ROCm users.
Error:
`RuntimeError: Input tensor at index 2 has invalid shape [2, 1, 4, 7, 8], but expected [2, 2, 4, 7, 8]`
# Root Cause
CTRL model returns past_key_values as 5D tensors [2, batch, heads, seq, head_dim] instead of tuple format (key_4d, value_4d) used by other transformer models. This non-standard format is incompatible with DataParallel's gather operation when batches split unevenly across GPUs.
# Solution
File: src/transformers/models/ctrl/modeling_ctrl.py
Line 148: Changed present = torch.stack((k, v)) to present = (k, v)
Before: 5D tensor [2, batch, heads, seq, head_dim]
After: Tuple of 4D tensors ([batch, heads, seq, head_dim], [batch, heads, seq, head_dim])
@remi-or | null | {
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} | # What does this PR do?
As of Python 3.9+, abstract base classes like Iterable, Mapping, Sequence, etc. are recommended to be imported from collections.abc instead of typing. Used `ruff` to identify and update these.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@ArthurZucker @ydshieh | {
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} | # What does this PR do?
Fixes https://github.com/huggingface/transformers/issues/38521. I checked with fast tokenizers' implementation of `word_to_char` and saw no difference in the time taken, so I think this can be the permanent solution
Otherwise we can add in `BatchFeature` support for `EncodingFast` features, though I don't think anyone needs them and I have never seen user requesting it | {
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I found a minor problem with paligemma model card. How can I raise a PR to fix it ? I am first time contributor. I raised PR. Whom should I mention to review it ?
https://huggingface.co/google/paligemma-3b-pt-896 | {
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} | Hi @zucchini-nlp. This PR fixed:
1. The correct usage of [decode_image_tokens](https://github.com/huggingface/transformers/blob/main/src/transformers/models/emu3/modeling_emu3.py#L1619) because the input only supports `**kwagrs`
2. Use atol=1 to check the result to avoid numerical precision error across different devices. The pixel values are uint8, ranging from 0 to 255. atol=1 will only handle very small precision errors, which is in our expectation. | {
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N/A
### Who can help?
@ydshieh
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Currently [GraniteMoeHybridIntegrationTest](https://github.com/huggingface/transformers/blob/main/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py#L107) is skipped, yet seems there is a ["ibm-granite/granite-4.0-tiny-preview"](https://huggingface.co/ibm-granite/granite-4.0-tiny-preview) rlsed, do we wait for "ibm-granite/granite-4.0-tiny" or go ahead w/ "ibm-granite/granite-4.0-tiny-preview"?
### Expected behavior
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} | [] | closed | false | null | [] | null | [] | 2025-06-03T00:57:16 | 2025-06-10T10:27:25 | 2025-06-10T10:26:48 | CONTRIBUTOR | null | null | null | null | I see the two functions appear in a lot of places in the code base. Shall we unify them into a single place?
And can we treat `eager_attention_forward` as another option in [`ALL_ATTENTION_FUNCTIONS`](https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L6186)? Any concerns? | {
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https://api.github.com/repos/huggingface/transformers/issues/38540 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38540/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38540/comments | https://api.github.com/repos/huggingface/transformers/issues/38540/events | https://github.com/huggingface/transformers/pull/38540 | 3,112,000,047 | PR_kwDOCUB6oc6YuNi3 | 38,540 | Image processor compile fix | {
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} | The tests:
```
tests/models/internvl/test_video_processor_internvl.py::test_can_compile_fast_video_processor
tests/models/qwen2_vl/test_video_processing_qwen2_vl.py::test_can_compile_fast_video_processor
```
are failing on AMD because the version of `torch.compile` we are using does not properly graph the `F.resize` function when input is uint8. For now, we fix this by wrapping calls to `F.resize` in a compile-friendly function.
This fixes the first test right of the bat, for the second we re-directed the `.resize` call in qwen2 processor to use the parent class's version. | {
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https://api.github.com/repos/huggingface/transformers/issues/38538 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38538/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38538/comments | https://api.github.com/repos/huggingface/transformers/issues/38538/events | https://github.com/huggingface/transformers/issues/38538 | 3,111,698,012 | I_kwDOCUB6oc65eL5c | 38,538 | Hidden states are different for model() and model.generate() | {
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] | closed | false | null | [] | null | [] | 2025-06-02T21:32:00 | 2025-06-05T11:39:09 | 2025-06-05T11:39:07 | NONE | null | null | null | null | ### System Info
Model: Qwen/Qwen2.5-0.5B-Instruct, BitsAndBytesConfig(load_in_8bit=True)
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
When doing model.generate() and accessing hidden_states, I access the first element, which consists again of the number of layers.
Accessing an element again gives something like (batch_size, tokens, embedding_size) -> similar to model() when accessing the hidden_states.
I always thought that
```python
generated_hidden_states[0][layer][:,-1,:],
hidden_states[layer][:,-1,:],
```
should be equal, both have the same shape and I thought both represent the same thing, but when looking at the cosine similarity between these vectors, I get different results
Does model.generate() do stuff that is not present in model() or vice versa ?
For reproducing:
```python
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
def load_model(model_name):
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
)
model.config.use_cache = False
tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
return model, tokenizer
model, tokenizer = load_model(model_name)
model_inputs = tokenizer(
["A list of colors: red, blue", "Portugal is"], return_tensors="pt", padding=True
).to("cuda")
output = model(**model_inputs, output_hidden_states=True)
my_hidden_states = output.hidden_states
generated_ids = model.generate(**model_inputs, output_hidden_states=True,return_dict_in_generate=True, max_new_tokens=13, output_scores=True)
F.cosine_similarity(my_hidden_states[-1][0,-1,:], generated_ids["hidden_states"][0][-1][0,-1,:], dim=0)
# results in different values
### Expected behavior
Should have equal values. | {
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https://api.github.com/repos/huggingface/transformers/issues/38537 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38537/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38537/comments | https://api.github.com/repos/huggingface/transformers/issues/38537/events | https://github.com/huggingface/transformers/pull/38537 | 3,111,553,254 | PR_kwDOCUB6oc6Ysv9F | 38,537 | Allow `mlm_probability` to be set to `None` when `mlm=False` in DataCollatorForLanguageModeling (#38522) | {
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} | [] | closed | false | null | [] | null | [] | 2025-06-02T20:35:06 | 2025-06-05T13:18:42 | 2025-06-05T12:54:12 | CONTRIBUTOR | null | null | false | {
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} | Currently, in the `DataCollatorForLanguageModeling` class, the `mlm_probability` argument is required to be a float between 0 and 1, regardless of whether `mlm` is `True` or `False`.
However, since `mlm_probability` is only used when `mlm=True`, it would make sense to allow `mlm_probability=None` when `mlm=False`, in order to reduce confusion and unnecessary configuration. | {
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} | # What does this PR do?
Adds ability to pass "sequence_length" key to data collator, achieving true absense of cross-document contamination when using packing.
This approach allows to set contiguous position_ids which in turn gets processed by flash attention kernel inside the Attention, allowing for block-diagonal attention masks and improving both speed and quality.
See https://github.com/huggingface/trl/pull/3526 for loss plots
@ArthurZucker @muellerzr @qgallouedec | {
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https://api.github.com/repos/huggingface/transformers/issues/38535 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38535/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38535/comments | https://api.github.com/repos/huggingface/transformers/issues/38535/events | https://github.com/huggingface/transformers/issues/38535 | 3,111,056,583 | I_kwDOCUB6oc65bvTH | 38,535 | Streaming mode support on HF vs kyutai-labs for the mimi model | {
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https://api.github.com/repos/huggingface/transformers/issues/38534 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38534/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38534/comments | https://api.github.com/repos/huggingface/transformers/issues/38534/events | https://github.com/huggingface/transformers/pull/38534 | 3,110,963,983 | PR_kwDOCUB6oc6YqyLO | 38,534 | fixed a bug which was causing only partial files to be imported | {
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This solves the following bug https://github.com/huggingface/text-generation-inference/issues/3251
It recursively imports all the required files | null | {
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https://api.github.com/repos/huggingface/transformers/issues/38533 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38533/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38533/comments | https://api.github.com/repos/huggingface/transformers/issues/38533/events | https://github.com/huggingface/transformers/pull/38533 | 3,110,937,104 | PR_kwDOCUB6oc6Yqsah | 38,533 | another way to use shift_labels | {
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https://api.github.com/repos/huggingface/transformers/issues/38532 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38532/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38532/comments | https://api.github.com/repos/huggingface/transformers/issues/38532/events | https://github.com/huggingface/transformers/pull/38532 | 3,110,774,299 | PR_kwDOCUB6oc6YqJIl | 38,532 | On branch fix-void-segment-mask-input [WIP] | {
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} | Changes to be committed:
modified: src/transformers/models/mask2former/image_processing_mask2former.py
Fix [#30064](https://github.com/huggingface/transformers/issues/30064)
This differ from previous PR fix [#30335](https://github.com/huggingface/transformers/pull/30335)? This fix tackles an edge case where an image's segment map only has background pixel (255 class label) whereas the previous PR fix doesn't.
### How is this PR fix intended to works?
Let's input a batch into `class Mask2FormerImageProcessor`, where `images = [img_1, img_2, img_3,..., img_B]` and its corresponding `segmentation maps = [map_1, map_2, map_3,... , map_B]`. Suppose `map_1` and `map_2` are void maps, then `def filter_image_with_void_segmentation_map` will pop void maps and its corresponding images, returning only `images = [img_3,..., img_B]` and `segmentation_maps = [map_3,..., map_B]`
I believe several testings will be desired, please advice.
### Before submitting
This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
### Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@amyeroberts @NielsRogge @Lynsoo
### Things to work on before this PR works, I think
- [ ] Implement `AdaptiveBatchSizeTrainer` to [continue model's training despite reduced batch size](https://github.com/huggingface/transformers/pull/38532#issuecomment-2937069511)
- [ ] Add a specific condition to only filter void map during validation. That's my interpretation of OP's question in [#30064](https://github.com/huggingface/transformers/issues/30064): "when training segmentation models, shouldn't it be possible to include images with only background/void class?" But this question is never addressed so idk whether you guys want void class when training model or no?
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