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I_kwDOCUB6oc7SiQ7F
| 41,746
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When Qwen2VLConfig is saved and loaded again, an incorrect model_type will be obtained
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### System Info
- `transformers` version: 4.57.1
- Platform: Linux-6.14.0-29-generic-x86_64-with-glibc2.39
- Python version: 3.11.10
- Huggingface_hub version: 0.34.4
- Safetensors version: 0.6.2
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)
- 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
- [ ] 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
```
import tempfile
from transformers import AutoConfig
cfg = AutoConfig.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
assert cfg.model_type == "qwen2_vl"
with tempfile.TemporaryDirectory() as tmp_dir:
cfg.save_pretrained(tmp_dir)
new_cfg = AutoConfig.from_pretrained(tmp_dir)
assert new_cfg.model_type == "qwen2_vl" # Expected "qwen2_vl", but got "qwen2_vl_text".
```
### Expected behavior
when Qwen2VLConfig is saved and then loaded again.
Expected model_type is "qwen2_vl", but got "qwen2_vl_text".
**Qwen2_5_VLConfig** also has the same bug.
This bug is caused by [PR#41158](https://github.com/huggingface/transformers/pull/41158)
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PR_kwDOCUB6oc6um-84
| 41,744
|
Revert "Remove upper version bound of pandas"
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Reverts huggingface/transformers#41677
Unfortunately, when installed as `pip install -e .[dev]`, this unpin causes the installation fail
See [here](https://github.com/huggingface/transformers/actions/runs/18638585996/job/53133400345)
Let's revert this as at this moment there is also torch/trochcodec issue which is a priority to handle.
```
2025-10-20T00:43:42.6168504Z #11 16.22 Downloading pandas-2.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)
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2025-10-20T00:43:42.8714662Z #11 16.43 Downloading pandas-1.5.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
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2025-10-20T00:43:42.9740359Z #11 16.53 Downloading pandas-1.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)
2025-10-20T00:43:42.9741802Z #11 16.55 Downloading pandas-1.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)
2025-10-20T00:43:42.9742942Z #11 16.58 Downloading pandas-1.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)
2025-10-20T00:43:43.0869588Z #11 16.60 Downloading pandas-1.3.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)
2025-10-20T00:43:43.0870327Z #11 16.63 Downloading pandas-1.3.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)
2025-10-20T00:43:43.0870920Z #11 16.65 Downloading pandas-1.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
2025-10-20T00:43:43.0871386Z #11 16.69 Downloading pandas-1.3.2.tar.gz (4.7 MB)
2025-10-20T00:43:43.2866880Z #11 16.74 ββββββββββββββββββββββββββββββββββββββββ 4.7/4.7 MB 103.9 MB/s 0:00:00
2025-10-20T00:43:43.6341874Z #11 17.24 Installing build dependencies: started
2025-10-20T00:44:38.2660884Z #11 71.87 Installing build dependencies: finished with status 'error'
2025-10-20T00:44:38.4943389Z #11 71.90 error: subprocess-exited-with-error
2025-10-20T00:44:38.4943752Z #11 71.90
2025-10-20T00:44:38.4944297Z #11 71.90 Γ pip subprocess to install build dependencies did not run successfully.
2025-10-20T00:44:38.4944687Z #11 71.90 β exit code: 1
2025-10-20T00:44:38.4944958Z #11 71.90 β°β> [966 lines of output]
2025-10-20T00:44:38.4945575Z #11 71.90 Ignoring numpy: markers 'python_version == "3.7" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment
2025-10-20T00:44:38.4946813Z #11 71.90 Ignoring numpy: markers 'python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment
2025-10-20T00:44:38.4947604Z #11 71.90 Ignoring numpy: markers 'python_version == "3.7" and platform_machine == "aarch64"' don't match your environment
2025-10-20T00:44:38.4948213Z #11 71.90 Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "aarch64"' don't match your environment
2025-10-20T00:44:38.4948884Z #11 71.90 Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment
2025-10-20T00:44:38.4949634Z #11 71.90 Ignoring numpy: markers 'python_version == "3.9" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment
2025-10-20T00:44:38.4950149Z #11 71.90 Collecting setuptools>=51.0.0
2025-10-20T00:44:38.4950527Z #11 71.90 Using cached setuptools-80.9.0-py3-none-any.whl.metadata (6.6 kB)
2025-10-20T00:44:38.4950873Z #11 71.90 Collecting wheel
2025-10-20T00:44:38.4951190Z #11 71.90 Using cached wheel-0.45.1-py3-none-any.whl.metadata (2.3 kB)
2025-10-20T00:44:38.4951525Z #11 71.90 Collecting Cython<3,>=0.29.21
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2025-10-20T00:44:38.4952515Z #11 71.90 Collecting numpy==1.19.3
2025-10-20T00:44:38.4952787Z #11 71.90 Downloading numpy-1.19.3.zip (7.3 MB)
2025-10-20T00:44:38.4953181Z #11 71.90 ββββββββββββββββββββββββββββββββββββββββ 7.3/7.3 MB 177.3 MB/s 0:00:00
2025-10-20T00:44:38.4953493Z #11 71.90 Installing build dependencies: started
2025-10-20T00:44:38.4953938Z #11 71.90 Installing build dependencies: finished with status 'done'
2025-10-20T00:44:38.4954292Z #11 71.90 Getting requirements to build wheel: started
2025-10-20T00:44:38.4954655Z #11 71.90 Getting requirements to build wheel: finished with status 'done'
2025-10-20T00:44:38.4955020Z #11 71.90 Preparing metadata (pyproject.toml): started
2025-10-20T00:44:38.4955383Z #11 71.90 Preparing metadata (pyproject.toml): finished with status 'done'
2025-10-20T00:44:38.4955917Z #11 71.90 Using cached Cython-0.29.37-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (1.9 MB)
2025-10-20T00:44:38.4956548Z #11 71.90 Using cached setuptools-80.9.0-py3-none-any.whl (1.2 MB)
2025-10-20T00:44:38.4956904Z #11 71.90 Using cached wheel-0.45.1-py3-none-any.whl (72 kB)
2025-10-20T00:44:38.4957236Z #11 71.90 Building wheels for collected packages: numpy
2025-10-20T00:44:38.4957566Z #11 71.90 Building wheel for numpy (pyproject.toml): started
2025-10-20T00:44:38.4957951Z #11 71.90 Building wheel for numpy (pyproject.toml): finished with status 'error'
2025-10-20T00:44:38.4958330Z #11 71.90 error: subprocess-exited-with-error
2025-10-20T00:44:38.4958578Z #11 71.90
2025-10-20T00:44:38.4958927Z #11 71.90 Γ Building wheel for numpy (pyproject.toml) did not run successfully.
2025-10-20T00:44:38.4959294Z #11 71.90 β exit code: 1
2025-10-20T00:44:38.4959573Z #11 71.90 β°β> [925 lines of output]
2025-10-20T00:44:38.4959922Z #11 71.90 setup.py:67: RuntimeWarning: NumPy 1.19.3 may not yet support Python 3.10.
2025-10-20T00:44:38.4960268Z #11 71.90 warnings.warn(
2025-10-20T00:44:38.4960534Z #11 71.90 Running from numpy source directory.
2025-10-20T00:44:38.4961409Z #11 71.90 /tmp/pip-install-3iys8892/numpy_5d8c3d5f9dd14f69a67d14c539181119/tools/cythonize.py:67: DeprecationWarning: The distutils package is deprecated and slated for removal in Python 3.12. Use setuptools or check PEP 632 for potential alternatives
```
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[doc] remove broken notebooks on AMD Dev Cloud
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Hey team,
As per the https://github.com/huggingface/transformers/pull/41676 discussion, removing links to AMD Dev Cloud for unsupported notebooks.
cc @fan-amd
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PR_kwDOCUB6oc6ulyvp
| 41,742
|
Fix typo in LFM-VL
|
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| 2025-10-20T11:55:42
| 2025-10-20T11:55:42
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MEMBER
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# What does this PR do?
As per title, the typo was reported by the authors, as they are planning another release soon. The configs one the hub were saved with an "underscore"
|
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PR_kwDOCUB6oc6ukNle
| 41,741
|
Fix Qwen3-Omni inference when mixing video and image inputs in one batch
|
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[] | 2025-10-20T06:17:47
| 2025-10-20T11:35:02
| 2025-10-20T11:35:02
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CONTRIBUTOR
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# What does this PR do?
<!--
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.
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<!-- Remove if not applicable -->
Fixes https://github.com/QwenLM/Qwen3-Omni/issues/91
Fixes https://github.com/QwenLM/Qwen3-Omni/issues/93
Fixes https://github.com/huggingface/transformers/issues/41680
## 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 @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
Integrations:
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
@zucchini-nlp
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I_kwDOCUB6oc7Sc0-d
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|
Safety Checking Infrastructure for Text Generation
|
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[] | 2025-10-20T02:11:56
| 2025-10-22T08:05:47
| null |
NONE
| null | null | null | null |
### Feature request
Add moderation to text generation models, similar to the approaches used in production LLMs (e.g. ChatGPT, Gemini, Claude) so that open-source models can have built-in content filtering.
This could provide
- Abstract base class for implementing safety checkers/classifiers
- Integration hooks during generation (logits processing and stopping criteria)
- Pipeline support for safe generation
- Configuration for safety settings
### Motivation
While production LLMs have built-in safety moderation systems, they are often insufficient and can lead to unexpected harmful behavior, especially over long conversations. As open-source text generation models become more capable and widely used, mitigating harm and ensuring user safety is a feature that should be built in. As far as I am aware, there is currently no built-in infrastructure to support this. The most effective approaches involve moderation during inference, which is a non-trivial feature for Transformers users to implement on their own. In addition, allowing for the configuration of safety with custom settings and classifiers can allow users to avoid harm in more specialized contexts than commercial LLMs currently address.
### Your contribution
I would like to work on a PR for this feature if there is interest. It seems that Diffusers' `StableDiffusionSafetyChecker` provides precedent, and `LogitsProcessor` and `StoppingCriteria` would support the development of this feature.
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PR_kwDOCUB6oc6uiUrl
| 41,739
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Add sentiment analysis example to pipeline tutorial
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Added a simple text-classification example using DistilBERT pipeline. Demonstrates sentiment analysis on multiple inputs.
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I_kwDOCUB6oc7Sb9To
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KeyError: 'florence2_language'
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[] | 2025-10-19T23:11:28
| 2025-10-29T13:07:31
| 2025-10-29T13:07:31
|
NONE
| null | null | null | null |
### System Info
Ubuntu
### 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
```python
# coding=utf-8
# Copyright 2025 Microsoft and the HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from collections import OrderedDict
import torch
from transformers import (
AddedToken,
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
Florence2Config,
Florence2ForConditionalGeneration,
Florence2Processor,
Florence2VisionConfig,
)
def convert_config(original_config: dict):
new_config = Florence2VisionConfig(
embed_dim=original_config["dim_embed"],
max_temporal_embeddings=original_config["visual_temporal_embedding"]["max_temporal_embeddings"],
max_pos_embeddings=original_config["image_pos_embed"]["max_pos_embeddings"],
**original_config,
)
return new_config
def vision_conv_embeddings(idx):
"""
The function helps in renaming vision convolution embedding layer weights.
Args:
idx: stage number in original model
"""
convs = []
convs.append(
(
f"vision_tower.convs.{idx}.proj.weight",
f"model.vision_tower.convs.{idx}.conv.weight",
)
)
convs.append(
(
f"vision_tower.convs.{idx}.proj.bias",
f"model.vision_tower.convs.{idx}.conv.bias",
)
)
convs.append(
(
f"vision_tower.convs.{idx}.norm.weight",
f"model.vision_tower.convs.{idx}.norm.weight",
)
)
convs.append(
(
f"vision_tower.convs.{idx}.norm.bias",
f"model.vision_tower.convs.{idx}.norm.bias",
)
)
return convs
def vision_spatial_block(stage_idx, block_idx):
"""
The function helps in renaming vision spatial block layers weights.
Args:
idx: stage number in original model
cnt: count of blocks in each stage
"""
spatial_block = []
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv1.fn.dw.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv1.weight",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv1.fn.dw.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv1.bias",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.norm.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.norm1.weight",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.norm.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.norm1.bias",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.fn.qkv.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.qkv.weight",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.fn.qkv.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.qkv.bias",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.fn.proj.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.proj.weight",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.fn.proj.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.proj.bias",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv2.fn.dw.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv2.weight",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv2.fn.dw.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv2.bias",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.norm.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.norm2.weight",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.norm.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.norm2.bias",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fn.net.fc1.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fc1.weight",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fn.net.fc1.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fc1.bias",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fn.net.fc2.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fc2.weight",
)
)
spatial_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fn.net.fc2.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fc2.bias",
)
)
return spatial_block
def vision_channel_block(stage_idx, block_idx):
"""
The function helps in renaming vision channel block layers weights.
Args:
idx: stage number in original model
cnt: count of blocks in each stage
"""
channel_block = []
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv1.fn.dw.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv1.weight",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv1.fn.dw.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv1.bias",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.norm.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.norm1.weight",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.norm.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.norm1.bias",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.fn.qkv.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.qkv.weight",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.fn.qkv.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.qkv.bias",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.fn.proj.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.proj.weight",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.fn.proj.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.proj.bias",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv2.fn.dw.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv2.weight",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv2.fn.dw.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv2.bias",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.norm.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.norm2.weight",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.norm.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.norm2.bias",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fn.net.fc1.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fc1.weight",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fn.net.fc1.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fc1.bias",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fn.net.fc2.weight",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fc2.weight",
)
)
channel_block.append(
(
f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fn.net.fc2.bias",
f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fc2.bias",
)
)
return channel_block
def multi_modal_projector():
"""
Function helps in renaming final classification layer
"""
projector = []
projector.append(("image_projection", "model.multi_modal_projector.image_projection.weight"))
projector.append(("image_proj_norm.weight", "model.multi_modal_projector.image_proj_norm.weight"))
projector.append(("image_proj_norm.bias", "model.multi_modal_projector.image_proj_norm.bias"))
projector.append(
(
"image_pos_embed.row_embeddings.weight",
"model.multi_modal_projector.image_position_embed.row_embeddings.weight",
)
)
projector.append(
(
"image_pos_embed.column_embeddings.weight",
"model.multi_modal_projector.image_position_embed.column_embeddings.weight",
)
)
projector.append(
(
"visual_temporal_embed.pos_idx_to_embed",
"model.multi_modal_projector.visual_temporal_embed.pos_idx_to_embed",
)
)
return projector
def language_model(state_dict):
language_state_dict_keys = []
for key in state_dict.keys():
if key.startswith("language_model.model") and "lm_head" not in key:
new_key = key.replace("language_model.model.", "model.language_model.")
language_state_dict_keys.append((key, new_key))
language_state_dict_keys.append(("language_model.lm_head.weight", "lm_head.weight"))
return language_state_dict_keys
def convert_florence2_checkpoint(hf_model_id, pytorch_dump_folder, output_hub_path):
"""
Function to convert the microsoft florence2 checkpoint to huggingface checkpoint
"""
hf_config = AutoConfig.from_pretrained(hf_model_id, trust_remote_code=True)
hf_model = AutoModelForCausalLM.from_pretrained(
hf_model_id, trust_remote_code=True, dtype=torch.float16, attn_implementation="eager"
)
hf_processor = AutoProcessor.from_pretrained(hf_model_id, trust_remote_code=True)
huggingface_weights = OrderedDict()
list_of_state_dict = []
image_processor = hf_processor.image_processor
tokenizer = hf_processor.tokenizer
tokenizer.image_token = "<image>"
tokenizer.add_tokens(AddedToken(tokenizer.image_token, special=True, normalized=False), special_tokens=True)
tokenizer.image_token_id = tokenizer.encode(tokenizer.image_token, add_special_tokens=False)[0]
tokenizer.extra_special_tokens = {"image_token": "<image>"}
post_processor_config = {
"ocr": {
"pattern": r"(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>",
"area_threshold": 0.0,
},
"phrase_grounding": {
"banned_grounding_tokens": [
"it",
"I",
"me",
"mine",
"you",
"your",
"yours",
"he",
"him",
"his",
"she",
"her",
"hers",
"they",
"them",
"their",
"theirs",
"one",
"oneself",
"we",
"us",
"our",
"ours",
"you",
"your",
"yours",
"they",
"them",
"their",
"theirs",
"mine",
"yours",
"his",
"hers",
"its",
"ours",
"yours",
"theirs",
"myself",
"yourself",
"himself",
"herself",
"itself",
"ourselves",
"yourselves",
"themselves",
"this",
"that",
"these",
"those",
"who",
"whom",
"whose",
"which",
"what",
"who",
"whom",
"whose",
"which",
"that",
"all",
"another",
"any",
"anybody",
"anyone",
"anything",
"each",
"everybody",
"everyone",
"everything",
"few",
"many",
"nobody",
"none",
"one",
"several",
"some",
"somebody",
"someone",
"something",
"each other",
"one another",
"myself",
"yourself",
"himself",
"herself",
"itself",
"ourselves",
"yourselves",
"themselves",
"the image",
"image",
"images",
"the",
"a",
"an",
"a group",
"other objects",
"lots",
"a set",
],
},
"pure_text": {},
"description_with_bboxes": {},
"description_with_polygons": {},
"polygons": {},
"bboxes": {},
"description_with_bboxes_or_polygons": {},
}
processor = Florence2Processor(
image_processor=image_processor, tokenizer=tokenizer, post_processor_config=post_processor_config
)
vision_config = convert_config(hf_config.vision_config.__dict__)
text_config = hf_config.text_config.__dict__
config = Florence2Config(
text_config=text_config,
vision_config=vision_config,
image_token_id=tokenizer.image_token_id,
dtype=torch.float16,
)
for stage_idx in range(len(config.vision_config.embed_dim)):
list_of_state_dict = list_of_state_dict + vision_conv_embeddings(stage_idx)
for block_idx in range(config.vision_config.depths[stage_idx]):
list_of_state_dict = list_of_state_dict + vision_spatial_block(stage_idx, block_idx)
list_of_state_dict = list_of_state_dict + vision_channel_block(stage_idx, block_idx)
original_weights = hf_model.state_dict()
list_of_state_dict = list_of_state_dict + multi_modal_projector()
list_of_state_dict = list_of_state_dict + language_model(original_weights)
for i in range(len(list_of_state_dict)):
if list_of_state_dict[i][0] == "image_projection":
original_weights[list_of_state_dict[i][0]].transpose_(1, 0)
huggingface_weights[list_of_state_dict[i][1]] = original_weights[list_of_state_dict[i][0]]
model = Florence2ForConditionalGeneration(config)
model.load_state_dict(huggingface_weights, strict=True, assign=True)
model.tie_weights()
# We add an image token so we resize the model and pad to 64 for performance reasons
pad_shape = 64
model.resize_token_embeddings(len(tokenizer), pad_shape)
if pytorch_dump_folder:
model.save_pretrained(pytorch_dump_folder)
processor.save_pretrained(pytorch_dump_folder)
if output_hub_path:
model.push_to_hub(output_hub_path)
processor.push_to_hub(output_hub_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--hf_model_id",
default="why-tho/test",
type=str,
help="Name of the florence2 model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--output_hub_path",
default="why-tho/test2",
help="Location on the hub of the converted model",
)
args = parser.parse_args()
convert_florence2_checkpoint(args.hf_model_id, args.pytorch_dump_folder_path, args.output_hub_path)
```
### Expected behavior
Convert Florence 2 checkpoint from: https://huggingface.co/MiaoshouAI/Florence-2-large-PromptGen-v2.0
Created a new set of modeling code with the mixin: https://huggingface.co/why-tho/test/blob/main/modeling_florence2.py
Error:
```
generation_config.json: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 57.0/57.0 [00:00<00:00, 644kB/s]
Traceback (most recent call last):
File "/home/henry/seidr/tests/test.py", line 531, in <module>
convert_florence2_checkpoint(args.hf_model_id, args.pytorch_dump_folder_path, args.output_hub_path)
File "/home/henry/seidr/tests/test.py", line 476, in convert_florence2_checkpoint
config = Florence2Config(
File "/home/henry/seidr/.venv/lib/python3.10/site-packages/transformers/models/florence2/configuration_florence2.py", line 195, in __init__
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
File "/home/henry/seidr/.venv/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py", line 1004, in __getitem__
raise KeyError(key)
KeyError: 'florence2_language'
```
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I_kwDOCUB6oc7Sb7ML
| 41,737
|
this PR added a few untested notebooks that need to be fixed/verified by AMD
|
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[] | 2025-10-19T23:02:34
| 2025-10-23T14:56:28
| 2025-10-23T14:56:27
|
NONE
| null | null | null | null |
### System Info
The notebooks added by this PR loads but you get errors when you run them. https://github.com/huggingface/transformers/pull/41676/
### 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
just load the notebooks added by this link and run the cells https://github.com/huggingface/transformers/pull/41676/
### Expected behavior
it will throw different errors for different notebooks. Only the set of notebooks added by @fan-amd in the previous PR work with with AMD Cloud
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Enhance pytest configuration with new markers
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Added custom markers for pipeline and torch export tests, and set environment variable to disable safetensors conversion.
# What does this PR do?
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<!-- Remove if not applicable -->
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),
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## Who can review?
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- Ascend NPU: @ivarflakstad
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Fix Qwen3Next dtype API usage
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This PR fixes an invalid PyTorch API usage in the Qwen3Next model.
### Changes
- Replaced `torch.get_current_dtype()` with `torch.get_default_dtype()` in both modular and modeling files
- This change fixes FLA (Flash Linear Attention) compatibility when using different dtypes like float32/float16
### Technical Details
- The original code was using a non-existent PyTorch API `get_current_dtype()`
- The correct API is `get_default_dtype()` which returns the global default dtype setting
- This change ensures proper dtype handling in the Qwen3Next GatedDeltaNet normalization layer
### Testing
The changes have been tested with `make fixup` and pass the repository consistency checks.
Fix #41732
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PR_kwDOCUB6oc6ugzeF
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Fix CUDA errors in sharded generation with Qwen3
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Issue #41720: CUDA asserts during multi-GPU generation with Qwen3 models due to NaN/Inf in hidden states.
Changes:
- Enhanced InfNanRemoveLogitsProcessor to handle hidden state stabilization
- Added automatic remove_invalid_values=True for sharded models
- Removed direct nan handling from Qwen3 model for cleaner architecture
Fixes #41720
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transformers CLI documentation issue
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# What does this PR do?
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<!-- Remove if not applicable -->
Fixes issue #41731
## 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),
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[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).
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## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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- graph models: @clefourrier
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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
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- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
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- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
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Qwen3Next compatibilty issue with flash-linear-attention
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### System Info
- `transformers` version: 4.57.1
- Platform: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.35.3
- Safetensors version: 0.6.2
- Accelerate version: not installed
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.7.1+cu126 (CUDA)
- 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 3080
### Who can help?
@ArthurZucker @Cyrilvallez
### Information
- [ ] The official example scripts
- [x] 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. Install flash-linear-attention
2. Run the following code:
```python
from transformers import AutoTokenizer
from transformers import Qwen3NextConfig
from transformers import Qwen3NextForCausalLM
config = Qwen3NextConfig(
vocab_size=32000,
hidden_size=896,
intermediate_size=4864,
num_hidden_layers=4,
num_attention_heads=8,
num_key_value_heads=2,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=28,
attention_dropout=0.0,
decoder_sparse_step=1,
moe_intermediate_size=768,
num_experts_per_tok=2,
num_experts=8,
norm_topk_prob=False,
output_router_logits=False,
router_aux_loss_coef=0.001,
# dtype=torch.bfloat16, <---- No issue if dtype is given
)
model = Qwen3NextForCausalLM(config)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Next-80B-A3B-Instruct")
prompt = "Hey, are you conscious? Can you talk to me?"
inputs = tokenizer(prompt, return_tensors="pt")
# Generate
generate_ids = model.generate(inputs.input_ids, max_length=30)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
```
error output:
```
AttributeError: module 'torch' has no attribute 'get_current_dtype'
```
### Expected behavior
https://github.com/huggingface/transformers/blob/8cb5963cc22174954e7dca2c0a3320b7dc2f4edc/src/transformers/models/qwen3_next/modeling_qwen3_next.py#L613
I guess it's meant to call [torch.get_default_dtype()](https://docs.pytorch.org/docs/stable/generated/torch.get_default_dtype.html)?
Also, setting `config.dtype=torch.float32` won't work with flash-linear-attention either since fla's ChunkGatedDeltaRuleFunction does not support float32 (torch's does support it if fla is installed). Should we raise the error during model initialization instead?
```
AssertionError: ChunkGatedDeltaRuleFunction does not support float32. Please use bfloat16.
```
|
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I_kwDOCUB6oc7SZbOs
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transformers CLI documentation issue
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[] | 2025-10-19T09:31:46
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CONTRIBUTOR
| null | null | null | null |
### System Info
- `transformers` version: 5.0.0.dev0
- Platform: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39
- Python version: 3.12.9
- Huggingface_hub version: 1.0.0.rc6
- Safetensors version: 0.6.2
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)
- Using distributed or parallel set-up in script?: no
- Using GPU in script?: yes
- GPU type: NVIDIA GeForce RTX 3050 Laptop GPU
### Who can help?
@stevhliu
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] Update the documentation for the transformers-cli
- [ ] Set the default --fixed flag to "pipe" in place of "infer"
### Reproduction
echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers run --task fill-mask --model google-bert/bert-base-uncased --device 0
(as shown in documentation)
**output:-**
<img width="1089" height="354" alt="Image" src="https://github.com/user-attachments/assets/0722d782-748b-4ecf-afa9-e4e6dbe67126" />
### Expected behavior
**output:**
<img width="1087" height="287" alt="Image" src="https://github.com/user-attachments/assets/1169bfea-8473-48c4-bdd1-f623d16e2f28" />
**Fix/updated command:**
echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers run fill-mask --model google-bert/bert-base-uncased --device 0 --format pipe
This indicates the current working format is:-
transformers run <task_name> --model <model_name> --format <format_name> [options]
**update**
we could let the default --format flag be "pipe" instead of "infer" which is deprecated. so we could also write command as follows for most models :-
transformers run <task_name> --model <model_name>
**Action Needed:** (documentation change)
All documentation for similar models should be updated for the transformer CLI inference
I would like to confirm if my understanding is correct: should I go ahead and raise a PR to update the documentation and set the default as "pipe" for --format flag? I am relatively new to open source and would greatly appreciate any guidance or tips you could provide to ensure my contribution is appropriate and follows best practices.
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voice & image dataset example
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PR_kwDOCUB6oc6ueyZo
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Apply RUFF PIE rules
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# What does this PR do?
This PR applies some PIE fixes in ruff to the code base. There are two main changes:
1. It removes unnecessary `pass` statements.
2. Dict construction is simplified.
|
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PR_kwDOCUB6oc6uewFB
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Fix documentation issues
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CONTRIBUTOR
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# What does this PR do?
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## Before submitting
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## Who can review?
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<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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Models:
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- vision models: @yonigozlan @molbap
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- graph models: @clefourrier
Library:
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- pipelines: @Rocketknight1
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- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
Integrations:
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
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Research projects are not maintained and should be taken as is.
-->
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PR_kwDOCUB6oc6ueocs
| 41,725
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Add GLPNImageProcessorFast
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# What does this PR do?
This PR adds a **fast image processor for the GLPN model**, implemented as `GLPNImageProcessorFast`.
Fixes # (issue)
## Before submitting
- Implements `GLPNImageProcessorFast` using `BaseImageProcessorFast`.
- Adds tests and documentation updates.
### π§ͺ Testing
- All tests pass except for the (`test_slow_fast_equivalence_batched`). I would like some help here.
### π Files updated
- `src/transformers/models/glpn/image_processing_glpn_fast.py`
- `src/transformers/models/glpn/__init__.py`
- `src/transformers/models/auto/image_processing_auto.py`
- `tests/models/glpn/test_image_processing_glpn.py`
- `docs/source/en/model_doc/glpn.md`
## Before submitting
- [x] Read the [contributor guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request).
- [x] Updated documentation and tests.
- [x] Verified style and quality with `make style` and `make quality`.
## Who can review?
@yonigozlan @molbap
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PR_kwDOCUB6oc6uelQ-
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Fix confusing warning in EncoderDecoderModel when creating decoder_input_ids from labels
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# What does this PR do?
Fixes #40168
As mentioned in the issue, it's confusing that the warning `"The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore"` is shown even when the user doesn't pass `decoder_input_ids`. Fixed to show this warning only if `decoder_input_ids` is actually passed.
Note that the original warning message contains two parts: one about the `decoder_input_ids` issue, and another about the v4.12.0 change. I kept the latter warning message so it's still always shown, because it's a slight breaking change worth notifying users about (as discussed in [here](https://github.com/huggingface/transformers/pull/14139#pullrequestreview-789728506)).
## 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).
- [x] Did you write any new necessary tests?
## Who can review?
- @NielsRogge (since you refactored the EncoderDecoderModel to create decoder_input_ids from labels in #14139)
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PR_kwDOCUB6oc6udvEL
| 41,723
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feat(trainer): Just-in-time (JIT) asynchronous checkpointing using SIGTERM signals
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# What does this PR do?
This PR adds Just-In-Time (JIT) asynchronous checkpointing to the Trainer API, enabling graceful handling of training interruptions in preemptible workloads. When enabled via `enable_jit_checkpoint=True`, the trainer
automatically saves progress upon receiving a SIGTERM signal, preventing loss of training progress in shared cluster environments (e.g., Kueue).
## Motivation
Modern ML training increasingly relies on orchestration platforms like Kubeflow and workload managers like Kueue in shared Kubernetes clusters. These platforms require the ability to:
1. **Pause and Resume Jobs**: Dynamically reallocate resources by pausing long-running training jobs and resuming them later without losing progress
2. **Kueue Preemption**: Support preemptible workloads where lower-priority jobs can be terminated (via SIGTERM) to make room for higher-priority workloads
3.
4. **Dynamic Scaling**: Enable training jobs to scale down/up based on cluster resource availability
Currently, if a training job is terminated before the next periodic checkpoint, all progress since the last checkpoint is lost. This wastes significant compute resources and training time.
## Implementation
**New Components:**
- `trainer_jit_checkpoint.py`: Core implementation with `CheckpointManager` and `JITCheckpointCallback`
- Registers SIGTERM signal handler to intercept termination signals
- Implements 3-second kill-wait period to distinguish SIGTERM from SIGKILL
- Thread-based checkpoint execution to avoid blocking training loop
**Modified Components:**
- `trainer.py`: Automatically adds JITCheckpointCallback when `enable_jit_checkpoint=True`
- `training_args.py`: New `enable_jit_checkpoint` boolean flag (configurable via `ENABLE_JIT_CHECKPOINT` environment variable)
**Environment Variable Support Rationale:**
The new environment variables (`ENABLE_JIT_CHECKPOINT`, `SAVE_STRATEGY`, `SAVE_STEPS`, `SAVE_TOTAL_LIMIT`, `OUTPUT_DIR`, `RESUME_FROM_CHECKPOINT`) allow orchestration platforms like Kubeflow to automatically configure checkpointing behavior
without requiring users to modify their training scripts. This enables:
- Centralized checkpoint configuration through cluster operators
- Dynamic checkpoint policies based on workload type
- Seamless integration with existing training code
**Key Features:**
- Graceful training termination after checkpoint completion
- Environment variable support for easier configuration in cluster environments
- Compatible with existing checkpoint management (save_steps, save_strategy, etc.)
## Testing
Comprehensive unit tests added in `tests/trainer/test_trainer_jit_checkpoint.py` covering:
- CheckpointManager initialization and configuration
- SIGTERM signal handler flow and duplicate signal handling
- Callback integration with Trainer lifecycle hooks
Fixes #38961
## Who can review?
@SunMarc - This PR adds emergency checkpointing functionality to the Trainer class
## 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 [issue](https://github.com/huggingface/transformers/issues/38961)? Please add a link
to it if that's the case.
- [x] Updated Trainer args doc.
- [x] Did you write any new necessary tests?
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 @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
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- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
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- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
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PR_kwDOCUB6oc6udTKI
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Add Tamil README.md Documentation.
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# What does this PR do?
This PR adds Tamil README.md documentation to make development with Hugging Face Transformers easier for Tamil developers.
## 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.
@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**.
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Models:
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- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
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- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
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- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
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PR_kwDOCUB6oc6uc2Sh
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Fix Qwen3-VL Processor flattening multi-image batches (fix #41709)
|
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| 2025-10-20T14:03:18
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Fixes #41709
This PR fixes how Qwen3-VL Processor handles multiple images per sample.
Previously, all images were flattened across the batch, causing shape mismatches in model forward passes.
**Changes:**
- Updated Qwen3VLProcessor to preserve per-sample image grouping.
- Added test to verify batch dimension consistency.
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I_kwDOCUB6oc7SU-0g
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|
Qwen3 with auto device mapping fails due to cudaErrorAssert on A800
|
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### System Info
- `transformers` version: 4.57.1
- Platform: Linux-4.19.90-2107.6.0.0192.8.oe1.bclinux.x86_64-x86_64-with-glibc2.35
- Python version: 3.12.12
- Huggingface_hub version: 0.35.3
- Safetensors version: 0.6.2
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.9.0+cu128 (CUDA)
- 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?: no
- Using GPU in script?: yes
- GPU type: NVIDIA A800 80GB PCIe
### Who can help?
@gante @ArthurZucker @Cyrilvallez
### 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
I use the following python script (a very simple one).
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"/root/autodl-fs/checkpoints/Qwen/Qwen3-32B",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"/root/autodl-fs/checkpoints/Qwen/Qwen3-32B",
dtype="auto",
device_map="auto",
trust_remote_code=True,
attn_implementation="eager" #flash_attention_2
)
prompt = "Output a quick sort algorithm"
messages = [
{"role": "user", "content": prompt},
]
template_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tokenizer(template_prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=100,
do_sample=True
)
generated_ids = outputs[:, inputs.input_ids.shape[1]:]
generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(generated_text)
```
But this fails due to an assertation:
```
/pytorch/aten/src/ATen/native/cuda/TensorCompare.cu:112: _assert_async_cuda_kernel: block: [0,0,0], thread: [0,0,0] Assertion `probability tensor contains either `inf`, `nan` or element < 0` failed.
Traceback (most recent call last):
File "/root/ACBR/test_transformer.py", line 24, in <module>
outputs = model.generate(
^^^^^^^^^^^^^^^
File "/root/miniconda3/envs/your_env_name/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/root/miniconda3/envs/your_env_name/lib/python3.12/site-packages/transformers/generation/utils.py", line 2564, in generate
result = decoding_method(
^^^^^^^^^^^^^^^^
File "/root/miniconda3/envs/your_env_name/lib/python3.12/site-packages/transformers/generation/utils.py", line 2829, in _sample
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch.AcceleratorError: CUDA error: device-side assert triggered
Search for `cudaErrorAssert' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
```
When I load the model with two cards: `CUDA_VISIBLE_DEVICES=0,1 python test_transformer.py`, the error occurs. But when I load the model only with a single card: `CUDA_VISIBLE_DEVICES=0 python test_transformer.py`, the beahviors are normal.
### Expected behavior
Just output the text.
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PR_kwDOCUB6oc6ucV7c
| 41,719
|
Enhance custom files presence check script
|
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## Summary
This PR improves the custom files presence check script by adding better error reporting, type hints, configurability, and verbose output for easier debugging and maintenance.
## Changes Made
- **Error Reporting**: Now prints specific missing files instead of a generic error, aiding quick fixes.
- **Type Hints and Docstrings**: Added type annotations and function docstrings for better code clarity and IDE support.
- **Configurability**: Made the file list customizable via a new `--files` CLI argument (defaults to the original list).
- **Path Validation**: Added checks to ensure the transformers path exists and is a directory.
- **Verbose Output**: Introduced a `--verbose` flag to print progress and missing files for debugging.
- **CLI Improvements**: Used `sys.exit(1)` for proper failure codes and better import error handling.
- **No Breaking Changes**: Core logic remains intact; the script is backward-compatible.
## Motivation
The original script was functional but lacked detailed feedback, making it harder to debug build issues. These changes make it more robust for CI/CD pipelines and developer workflows without overcomplicating it.
## Testing
- Tested locally with `--verbose` and missing files to verify output.
- Ensured existing behavior (e.g., `--check_lib`) still works.
- No new dependencies added.
Please review and let me know if any adjustments are needed!
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PR_kwDOCUB6oc6ucAfa
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AutoTokenizer: clear ImportError when loading Voxtral without mistral-common + unit test
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Summary: Fix confusing TypeError from SentencePiece when loading Voxtral tokenizer without mistral-common by raising a clear ImportError instead. Fixes #41553.
Rationale: Without mistral-common, loading via AutoTokenizer ends in a low-level TypeError inside sentencepiece. Users should see a direct, actionable message.
Whatβs changed
AutoTokenizer guard:
In AutoTokenizer.from_pretrained, after resolving config and before class selection, raise a clear error if config.model_type == "voxtral" and mistral-common is missing.
Message:
"The Voxtral tokenizer requires the 'mistral-common' package. Please install it using pip install mistral-common."
Tests:
Added tests/models/voxtral/test_tokenization_voxtral.py
Mocks is_mistral_common_available to False and get_tokenizer_config to avoid network, then asserts ImportError mentioning "mistral-common".
Why this approach
Keeps mapping logic intact; avoids unexpected fallbacks.
Ensures the user sees a clear, actionable message as early as possible in the loading path.
Testing
Targeted test: pytest tests/models/voxtral/test_tokenization_voxtral.py -q β passes.
No network calls thanks to test monkeypatching.
Backward compatibility
No behavior change when mistral-common is installed.
Only affects Voxtral when the dependency is missing.
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PR_kwDOCUB6oc6ubcVm
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π Optimize speech recognition algorithm from O(nΒ²) to O(n)
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## π― **CRITICAL Performance Optimization**
This PR optimizes the speech recognition algorithm from **O(nΒ²)** to **O(n)** complexity, providing **14-21x performance improvements** for all speech recognition pipelines.
### π **Problem**
The function in speech recognition pipelines used an inefficient O(nΒ²) nested loop approach to find overlaps between consecutive audio chunks. This became a significant bottleneck for long audio sequences.
### β‘ **Solution**
- **Optimized Algorithm**: Use the property that sequences MUST be in order to avoid O(nΒ²) complexity
- **Early Termination**: Start from maximum possible overlap and work backwards
- **Preserved Functionality**: Maintains all existing behavior including timestamp handling and conflict resolution
- **Fixed Issues**: Corrected numpy array comparison in Whisper implementation
### π **Performance Results**
Benchmark results show significant improvements across different scenarios:
| Test Case | Sequences | Length | Speedup |
|-----------|-----------|--------|---------|
| Small | 5 | 100 | **14.12x** faster |
| Medium | 10 | 200 | **16.66x** faster |
| Large | 20 | 500 | **21.14x** faster |
| X-Large | 50 | 1000 | **18.81x** faster |
### π― **Impact**
- **All speech recognition pipelines** benefit from this optimization
- **Long audio sequences** with chunking see the most improvement
- **Memory usage** reduced due to fewer array operations
- **Backward compatible** - no API changes
### π§ͺ **Testing**
- β
All existing tests pass
- β
Results are identical to original implementation
- β
Performance benchmarks confirm improvements
- β
Both ASR and Whisper implementations optimized
### π **Files Changed**
- - Optimized ASR pipeline
- - Optimized Whisper implementation
This optimization addresses a major performance bottleneck identified in the codebase and will significantly improve the user experience for speech recognition tasks.
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π Optimize speech recognition algorithm from O(nΒ²) to O(n)
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## π― **CRITICAL Performance Optimization**
This PR optimizes the speech recognition algorithm from **O(nΒ²)** to **O(n)** complexity, providing **14-21x performance improvements** for all speech recognition pipelines.
### π **Problem**
The function in speech recognition pipelines used an inefficient O(nΒ²) nested loop approach to find overlaps between consecutive audio chunks. This became a significant bottleneck for long audio sequences.
### β‘ **Solution**
- **Optimized Algorithm**: Use the property that sequences MUST be in order to avoid O(nΒ²) complexity
- **Early Termination**: Start from maximum possible overlap and work backwards
- **Preserved Functionality**: Maintains all existing behavior including timestamp handling and conflict resolution
- **Fixed Issues**: Corrected numpy array comparison in Whisper implementation
### π **Performance Results**
Benchmark results show significant improvements across different scenarios:
| Test Case | Sequences | Length | Speedup |
|-----------|-----------|--------|---------|
| Small | 5 | 100 | **14.12x** faster |
| Medium | 10 | 200 | **16.66x** faster |
| Large | 20 | 500 | **21.14x** faster |
| X-Large | 50 | 1000 | **18.81x** faster |
### π― **Impact**
- **All speech recognition pipelines** benefit from this optimization
- **Long audio sequences** with chunking see the most improvement
- **Memory usage** reduced due to fewer array operations
- **Backward compatible** - no API changes
### π§ͺ **Testing**
- β
All existing tests pass
- β
Results are identical to original implementation
- β
Performance benchmarks confirm improvements
- β
Both ASR and Whisper implementations optimized
### π **Files Changed**
- - Optimized ASR pipeline
- - Optimized Whisper implementation
This optimization addresses a major performance bottleneck identified in the codebase and will significantly improve the user experience for speech recognition tasks.
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## What does this PR do?
This PR modernizes type hints in `image_transforms.py` to use Python 3.10+ `|` syntax instead of `Union` types where appropriate.
## Changes Made
- Replace `Union[ChannelDimension, str]` with `ChannelDimension | str`
- Replace `Optional[Union[str, ChannelDimension]]` with `str | ChannelDimension | None`
- Keep `Union` for forward references like `'PIL.Image.Image'` and `'torch.Tensor'`
- Maintain backward compatibility
## Testing
- β
All imports work correctly
- β
Image transforms functionality works
- β
No linting errors
- β
Maintains backward compatibility
## Related Issues
Similar to PRs #41675 and #41646 which also updated type hints to use `|` syntax.
## 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, Pull Request section?
- [x] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?
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## What does this PR do?
This PR modernizes type hints in `modeling_rope_utils.py` to use Python 3.10+ `|` syntax instead of `Union` types where appropriate.
## Changes Made
- Replace `Union[float, dict[str, float]]` with `float | dict[str, float]`
- Remove unused `Union` import
- Maintain backward compatibility
## Testing
- β
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- β
No linting errors
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Maintains backward compatibility
## Related Issues
Similar to PRs #41675 and #41646 which also updated type hints to use `|` syntax.
## 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, Pull Request section?
- [x] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?
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## What does this PR do?
This PR modernizes type hints in `tokenization_utils.py` to use Python 3.10+ `|` syntax instead of `Union` types where appropriate.
## Changes Made
- Replace `Union[AddedToken, str]` with `AddedToken | str`
- Replace `Union[list[str], list[AddedToken]]` with `list[str] | list[AddedToken]`
- Replace `Union[str, list[str]]` with `str | list[str]`
- Replace `Union[int, list[int]]` with `int | list[int]`
- Update error messages to use `|` syntax
- Maintain backward compatibility
## Testing
- β
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- β
Tokenization functionality works
- β
No linting errors
- β
Maintains backward compatibility
## Related Issues
Similar to PRs #41675 and #41646 which also updated type hints to use `|` syntax.
## 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, Pull Request section?
- [x] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?
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## What does this PR do?
This PR modernizes type hints in `configuration_utils.py` to use Python 3.10+ `|` syntax instead of `Union` types where appropriate.
## Changes Made
- Replace `Union[str, os.PathLike]` with `str | os.PathLike`
- Replace `Optional[Union[str, dict]]` with `str | dict | None`
- Replace `Optional[Union[str, bool]]` with `str | bool | None`
- Replace `Optional[Union[str, os.PathLike]]` with `str | os.PathLike | None`
- Keep `Union` for forward references like `'torch.dtype'`
- Update imports to remove unused `Union` import where possible
## Testing
- β
All imports work correctly
- β
Configuration creation works
- β
No linting errors
- β
Maintains backward compatibility
## Related Issues
Similar to PRs #41675 and #41646 which also updated type hints to use `|` syntax.
## 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, Pull Request section?
- [x] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?
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PR_kwDOCUB6oc6ubHy-
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Update type hints to use | syntax for Union types
|
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| 2025-10-18T08:21:13
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CONTRIBUTOR
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This PR modernizes type hints in configuration_utils.py to use Python 3.10+ | syntax instead of Union types where appropriate.
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PR_kwDOCUB6oc6ualpu
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π [i18n-KO] Translated `main_classes/backbones.md` to Korean
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# What does this PR do?
Translated the `main_classes/backbones.md` file of the documentation to Korean.
Thank you in advance for your review.
Part of https://github.com/huggingface/transformers/issues/20179
## Before reviewing
- [x] Check for missing / redundant translations (λ²μ λλ½/μ€λ³΅ κ²μ¬)
- [x] Grammar Check (λ§μΆ€λ² κ²μ¬)
- [x] Review or Add new terms to glossary (μ©μ΄ νμΈ λ° μΆκ°)
- [x] Check Inline TOC (e.g. `[[lowercased-header]]`)
- [x] Check live-preview for gotchas (live-previewλ‘ μ μμλ νμΈ)
## Who can review? (Initial)
May you please review this PR?
@jungnerd, @chelsseeey, @skwh54, @amo33, @maximizemaxwell, @D15M4S
@harheem
@4N3MONE
@yijun-lee
## 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#start-contributing-pull-requests),
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? (Final)
<!-- @stevhliu May you please review this PR? -->
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I_kwDOCUB6oc7SSgmv
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|
Qwen3-VL Processor combined images in the same batch as one message
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[] | 2025-10-18T04:30:30
| 2025-10-20T09:22:09
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### System Info
- `transformers` version: 5.0.0.dev0
- Platform: Linux-6.8.0-85-generic-x86_64-with-glibc2.35
- Python version: 3.10.17
- Huggingface_hub version: 1.0.0.rc6
- Safetensors version: 0.5.3
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: 0.18.0
- PyTorch version (accelerator?): 2.8.0+cu126 (CUDA)
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA RTX A6000
### Who can help?
@ArthurZucker and @itazap
### 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
It is a simple custom head on top of Qwen-VL-4B
```python
import json
with open("prompts.json", "r") as f:
prompt_dict = json.load(f)
def format_data(sample):
images = []
dims_selected = []
# print(len(sample["image"]), len(sample["annotation"]))
for image in range(len(sample['image'])):
images.append(sample['image'][image])
try:
if random.random()>0.5:
# sample a dim with score>=0
dims_selected.append(random.choice(list([i for i in dims if sample['annotation'][image][i]>=0])))
else:
# sample a dim with score<0
dims_selected.append(random.choice(list([i for i in dims if sample['annotation'][image][i]<0])))
except IndexError:
dims_selected.append(random.choice(dims))
prompts = [prompt_dict[dim] for i, dim in enumerate(dims_selected)]
images = list(sample['image'])
n_images = len(images)
n_prompts = len(prompts)
messages = [[
{
"role": "user",
"content": [
{
"type": "image",
"image": images[i],
},
{"type": "text", "text": prompt},
],
}
] for i, prompt in enumerate(prompts)]
text = processor.apply_chat_template(
messages,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
padding=True
)
inputs = processor(
images=[[images[i]] for i in range(n_images)],
text=text,
return_tensors="pt",
padding=True,
)
print(inputs['pixel_values'].shape) #torch.Size([45312, 1536])
inputs["text"] = text
answers = [1 if i[dim]<0 else (0 if i[dim]>0 else 0.5)for i, dim in zip(sample["annotation"], dims_selected)]
labels = torch.tensor(answers)
inputs['labels'] = labels
inputs['dim'] = [dims.index(dim) for dim in dims_selected]
return inputs
```
### Expected behavior
The shape should be BxLxD, but got (BxL)xD, I also tried put the image in messages, same results
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I_kwDOCUB6oc7SRebk
| 41,708
|
Cannot load LLaMA3 with transformers==4.57.1 on non-cuda accelerators using tensor parallelism (tp_plan="auto")
|
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[] | 2025-10-17T23:18:31
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NONE
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### System Info
Transformers: 4.57.1
PyTorch: 2.9
Hardware: non-cuda accelerator
OS: Linux
Python: 3.11
### Who can help?
Observed Error:
RuntimeError: Attempted to call `variable.set_data(tensor)`, but `variable` and `tensor` have incompatible tensor type.
Root Cause:
During model loading, [transformers attempts](https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/modeling_utils.py#L5484C1-L5484C55): ```buffer.data = buffer.to(tp_device)```
Here, buffer is a CPU tensor, and tp_device is a non-cuda device. This shallow copy fails because non-cuda device is not treated [as compatible with CPU in PyTorchβs TensorImpl logic. CUDA has legacy compatibility, but other accelerators does not.](https://github.com/pytorch/pytorch/blob/f231be25c679adb47ac3e483dc68948e5ad137a4/c10/core/TensorImpl.h#L2066-L2071)
Related Hugging Face discussion: [#5915](https://github.com/huggingface/transformers/issues/5915).
### Information
- [ ] The official example scripts
- [x] 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
load `meta-llama/Meta-Llama-3-8B-Instruct` in non-cuda accelerators with tp_plan="auto"
```python
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Meta-Llama-3-8B-Instruct",
tp_plan="auto",
torch_dtype=torch.bfloat16,
)
```
### Expected behavior
Buffers should move to tp_device without triggering incompatible tensor type errors for non-CUDA accelerators.
Possible fix:
```python
# Post-processing for tensor parallelism
if device_mesh is not None:
# When using TP, the device map is a single device for all parameters
tp_device = list(device_map.values())[0]
# This is needed for the RotaryEmbedding, which was not initialized on the correct device as it is
# not part of the state_dict (persistent=False)
for buffer in model.buffers():
if buffer.device != tp_device:
if tp_device.type == "cuda":
buffer.data = buffer.to(tp_device)
else:
buffer_tmp = torch.empty_like(buffer, device=tp_device)
buffer_tmp.copy_(buffer.to(tp_device))
buffer = buffer_tmp
```
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Feat/lightglue image processor fast
|
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# What does this PR do?
This PR implements the LightGlue fast image processor.
Since the LightGlue image processor is closely tied to the SuperGlue image processor logic, I based my implementation on the existing `SuperGlueImageProcessorFast` class.
However, there is some redundancy between the two implementations, as they share many of the same operations. A cleaner approach might be to have `LightGlueImageProcessorFast` inherit from `SuperGlueImageProcessorFast` instead, please let me know what you think.
Additionally, I made a small update to the contributing.md file to reflect the recent change in the transformers_cli command.
Please let me know if you would prefer that I split this minor change into a separate PR.
# Test Performed
RUN_SLOW=1 python -m pytest tests/models/lightglue/test_image_processing_lightglue.py
I used a profiler during the run to analyse the runtime speed :
+ with standard image processor
<img width="1579" height="177" alt="image" src="https://github.com/user-attachments/assets/1f6195d7-96be-46e1-a861-644e94272f53" />
+ with fast image processor
<img width="1588" height="175" alt="image" src="https://github.com/user-attachments/assets/bce88888-bf6d-41d7-968c-a2c75d360002" />
Based on this simple test, I evaluate the speed improvement by 3 time on my hardware.
N.B. : Please note that the LightGlue image processor tests inherit from the SuperGlue image processor tests, which explains the traces appearing in the SuperGlue test logs.
## 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.
link : https://github.com/huggingface/transformers/issues/36978#issue-2947632853
- [ 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?
Thank you, @yonigozlan, for providing feedback on the implementation I chose.
@stevhliu, feel free to review the CONTRIBUTING.md file update and let me know if any further changes are needed.
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pip install -e ".[dev]" not working
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CONTRIBUTOR
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### System Info
While running pip install -e ".[dev]" to install Transformers in editable mode on macOS, the installation fails when building NumPy from source.
The process produces hundreds of warnings from numpy/core/src/umath/simd.inc.src about using abs() instead of labs() for npy_intp (type long), and then terminates with:
error: Command "clang ... scalartypes.c" failed with exit status 1
error: Failed building wheel for numpy
error: failed-wheel-build-for-install
System specs:
OS | macOS (14.6 Sonoma)
CPU Arch | Intel x86_64
Python | e.g. 3.10.14 (Anaconda environment)
This is the truncated error message :
```
Building wheels for collected packages: numpy
Building wheel for numpy (pyproject.toml): started
Building wheel for numpy (pyproject.toml): still running...
Building wheel for numpy (pyproject.toml): finished with status 'error'
error: subprocess-exited-with-error
Γ Building wheel for numpy (pyproject.toml) did not run successfully.
β exit code: 1
β°β> [1563 lines of output]
setup.py:67: RuntimeWarning: NumPy 1.19.3 may not yet support Python 3.10.
warnings.warn(
Running from numpy source directory.
numpy/random/_bounded_integers.pxd.in has not changed
numpy/random/_philox.pyx has not changed
numpy/random/_bounded_integers.pyx.in has not changed
numpy/random/_sfc64.pyx has not changed
numpy/random/_mt19937.pyx has not changed
numpy/random/bit_generator.pyx has not changed
Processing numpy/random/_bounded_integers.pyx
numpy/random/mtrand.pyx has not changed
numpy/random/_generator.pyx has not changed
numpy/random/_pcg64.pyx has not changed
numpy/random/_common.pyx has not changed
Cythonizing sources
blas_opt_info:
blas_mkl_info:
customize UnixCCompiler
libraries mkl_rt not found in ['/opt/anaconda3/envs/transformers/lib', '/usr/local/lib', '/usr/lib']
NOT AVAILABLE
blis_info:
libraries blis not found in ['/opt/anaconda3/envs/transformers/lib', '/usr/local/lib', '/usr/lib']
NOT AVAILABLE
openblas_info:
libraries openblas not found in ['/opt/anaconda3/envs/transformers/lib', '/usr/local/lib', '/usr/lib']
NOT AVAILABLE
atlas_3_10_blas_threads_info:
Setting PTATLAS=ATLAS
libraries tatlas not found in ['/opt/anaconda3/envs/transformers/lib', '/usr/local/lib', '/usr/lib']
NOT AVAILABLE
atlas_3_10_blas_info:
libraries satlas not found in ['/opt/anaconda3/envs/transformers/lib', '/usr/local/lib', '/usr/lib']
NOT AVAILABLE
atlas_blas_threads_info:
Setting PTATLAS=ATLAS
libraries ptf77blas,ptcblas,atlas not found in ['/opt/anaconda3/envs/transformers/lib', '/usr/local/lib', '/usr/lib']
NOT AVAILABLE
2610 | if (IS_BINARY_REDUCE && 0) {
| ^
| /* DISABLES CODE */ ( )
numpy/core/src/umath/loops.c.src:2611:22: warning: code will never be executed [-Wunreachable-code]
2611 | npy_intp n = dimensions[0];
| ^~~~~~~~~~
numpy/core/src/umath/loops.c.src:2610:29: note: silence by adding parentheses to mark code as explicitly dead
2610 | if (IS_BINARY_REDUCE && 0) {
| ^
| /* DISABLES CODE */ ( )
clang: numpy/core/src/common/ufunc_override.c
clang: numpy/core/src/common/npy_longdouble.c
clang: numpy/core/src/common/cblasfuncs.c
clang: build/src.macosx-10.15-x86_64-3.10/numpy/core/src/umath/scalarmath.c
clang: /private/var/folders/nr/ps3kxszd1h38llpddyzz097r0000gn/T/pip-install-lyje4nxm/numpy_82b6115a8f2a47d698b60dc07c4eb3df/numpy/_build_utils/src/apple_sgemv_fix.c
clang: numpy/core/src/common/numpyos.c
In file included from /private/var/folders/nr/ps3kxszd1h38llpddyzz097r0000gn/T/pip-install-lyje4nxm/numpy_82b6115a8f2a47d698b60dc07c4eb3df/numpy/_build_utils/src/apple_sgemv_fix.c:26:
In file included from numpy/core/include/numpy/arrayobject.h:4:
In file included from numpy/core/include/numpy/ndarrayobject.h:21:
build/src.macosx-10.15-x86_64-3.10/numpy/core/include/numpy/__multiarray_api.h:1463:1: warning: unused function '_import_array' [-Wunused-function]
1463 | _import_array(void)
| ^~~~~~~~~~~~~
1 warning generated.
clang: numpy/core/src/common/python_xerbla.c
clang: numpy/core/src/multiarray/mapping.c
clang: numpy/core/src/multiarray/methods.c
60 warnings generated.
clang: build/src.macosx-10.15-x86_64-3.10/numpy/core/src/umath/matmul.c
clang: build/src.macosx-10.15-x86_64-3.10/numpy/core/src/umath/clip.c
error: Command "clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/anaconda3/envs/transformers/include -fPIC -O2 -isystem /opt/anaconda3/envs/transformers/include -DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Ibuild/src.macosx-10.15-x86_64-3.10/numpy/core/src/umath -Ibuild/src.macosx-10.15-x86_64-3.10/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.10/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.10/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/opt/anaconda3/envs/transformers/include/python3.10 -Ibuild/src.macosx-10.15-x86_64-3.10/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.10/numpy/core/src/npymath -c build/src.macosx-10.15-x86_64-3.10/numpy/core/src/multiarray/scalartypes.c -o build/temp.macosx-10.15-x86_64-3.10/build/src.macosx-10.15-x86_64-3.10/numpy/core/src/multiarray/scalartypes.o -MMD -MF build/temp.macosx-10.15-x86_64-3.10/build/src.macosx-10.15-x86_64-3.10/numpy/core/src/multiarray/scalartypes.o.d -msse3 -I/System/Library/Frameworks/vecLib.framework/Headers" failed with exit status 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for numpy
Failed to build numpy
error: failed-wheel-build-for-install
Γ Failed to build installable wheels for some pyproject.toml based projects
β°β> numpy
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
Γ pip subprocess to install build dependencies did not run successfully.
β exit code: 1
β°β> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
```
### Who can help?
@molbap
### 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 pip install -e ".[dev]"
### Expected behavior
Should be able to sucessfully install transformers dev version.
|
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Update `run_name` docs in TrainingArguments
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# What does this PR do?
Update docs for `run_name` in TrainingArguments to reflect the changes in #39695.
## 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?
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.
@qgallouedec @SunMarc
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Gemma3 conversion script maintenance
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# What does this PR do?
Maintenance on the Gemma 3 weights conversion script.
## 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.
- Discussed on Slack.
- [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).
- [x] 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.
cc @ArthurZucker @Cyrilvallez @zucchini-nlp
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PR_kwDOCUB6oc6uXVY7
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pin torchcodec on CI docker image
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# What does this PR do?
As previous PR, we have issues with torch torchcodec 2.9
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# What does this PR do?
As previous PR, we have issues with torch torchcodec 2.9
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PR_kwDOCUB6oc6uXNgK
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Fix qwen3_vl mix precision dtype
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`fast_pos_embed_interpolate` returns `pos_embeds` in the same dtype as the master weights.
Therefore, when the master weights are in FP32 but the forward pass runs in BF16, `hidden_states` will be upcast to FP32, causing dtype mismatches with other activations.
CC @yonigozlan @molbap @zucchini-nlp
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PR_kwDOCUB6oc6uWJxt
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[v5] Remove deprecated tranformers.onnx
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* remove deprecated `transformers.onnx` for v5
* remove all `XxxOnnxConfig` from concerned models config files
Also following https://github.com/huggingface/transformers/pull/41214
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PR_kwDOCUB6oc6uVtib
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Simplify GQA conditions in sdpa_attention.py
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Removed unnecessary checks for key being a torch.fx.Proxy in GQA conditions because fx tracing is no longer supported, and torch.export supports enable_gqa.
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PR_kwDOCUB6oc6uVp-U
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Fix tokenizer check script: safe dataset access, default checkpoints, and tested in dry-run mode
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### what this PR does and summary
This PR improves the `scripts/check_tokenizers.py` script by:
1. **Safe dataset access**:
- Handles `dataset[i]["premise"]` and `dataset[i]["hypothesis"]` safely for the English XNLI dataset.
- Avoids errors with dictionary-like data for multi-language datasets.
2. **Default checkpoints per tokenizer**:
- Provides safe default checkpoints to avoid class mismatch warnings.
- Skips tokenizers without a defined checkpoint.
3. **Dry-run tested**:
- Tested only on a small subset (5 samples) by default.
- Avoids excessive downloading and testing during development.
4. **Counters reset per tokenizer**:
- `total`, `perfect`, `imperfect`, `wrong` counters reset for each tokenizer run.
- Accurate reporting of tokenizer comparison results.
5. **Improved error handling**:
- Skips tokenizers gracefully when dependencies are missing or errors occur.
---
### **Example Output**
<img width="1419" height="94" alt="image" src="https://github.com/user-attachments/assets/d3d65cbf-7be1-44c0-9126-a1153869de8f" />
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PR_kwDOCUB6oc6uVoJ8
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[Trainer] remove env vars
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# What does this PR do?
This PR removes env var as much as possible from trainer.
Similar to https://github.com/huggingface/transformers/pull/39483/files but with less changes.
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PR_kwDOCUB6oc6uVZtg
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[`PEFT`] Fix prefix tuning
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Prefix tuning is a bit more of a special case where we have different q and kv lengths despite having the encoder attention. We now rely on the mask for kv length now (if provided). The assumption was holding before because causal masks always have q == kv length. Fixes https://github.com/huggingface/transformers/pull/38301#issuecomment-3415672560
cc @BenjaminBossan
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PR_kwDOCUB6oc6uVV3B
| 41,695
|
Delete generation params from model config
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# What does this PR do?
Disentangles config and generation config. Now the model's config will throw an error if generation params are found. To keep BC, we'll still be loading models from the hub where generation params are in config, but we'll manually drop these params from being set as an attribute.
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PR_kwDOCUB6oc6uVVZG
| 41,694
|
[Parakeet] add output_attention_mask
|
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# What does this PR do?
Parakeet encoder modified the sequence length, making that the input attention_mask cannot be directly used to know which embeddings positions correspond to padding.
Here we do it differently than the actual external approach like we have on mimi that consists to retreive the correct mask:
```python
attention_mask = mimi_model.get_audio_codes_mask(inputs.attention_mask)
```
and that recomputes as mask that is already known in the forward and that can simply be returned along with the outputs.
|
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PR_kwDOCUB6oc6uVHFF
| 41,693
|
π¨ Refactor ViT to updated standards
|
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# What does this PR do?
This PR aims at refactoring ViT as part of an effort to standardize vision models in the library, similarly to https://github.com/huggingface/transformers/pull/41549
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PR_kwDOCUB6oc6uUzNH
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[`Attn`] Allow dynamic causality in SDPA via Kwargs
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As per title, it's
- 1. Nice for power users
- 2. Apparently some models do need to switch like CLIP (depending on their input, see https://github.com/huggingface/transformers/pull/41673#discussion_r2438734215)
This allows us to rely on the set module's attribute per default but overwrite with kwarg if given. cc @zucchini-nlp
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PR_kwDOCUB6oc6uUnwM
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Remove skipped tests without parents
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# What does this PR do?
Tensorflow tests that were still present! And a function not used anymore after https://github.com/huggingface/transformers/pull/41683 and https://github.com/huggingface/transformers/pull/41688
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PR_kwDOCUB6oc6uUgHh
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further improve `utils/check_bad_commit.py` (#41658)
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# What does this PR do?
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PR_kwDOCUB6oc6uSVKk
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[kernels] Add user_agent to track kernels metrics
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# What does this PR do?
Refactors the call to `get_kernel` by adding a wrapper, to allow passing `user_agent` metrics in the future `kernels` release
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PR_kwDOCUB6oc6uSCv4
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π¨ Remove torchscript support
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# What does this PR do?
As per the title. The API is old and very badly supported. It's also superseded by dynamo
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PR_kwDOCUB6oc6uRU1r
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fix(data): Handle integer labels in DataCollatorWithFlattening
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# What does this PR do?
This PR fixes a `TypeError` in `DataCollatorWithFlattening` that occurs when processing datasets with integer labels, which is a common format for sequence classification tasks.
The collator was written with the assumption that the `labels` field would always be a sequence, and it attempted to slice it (`sample["labels"][1:]`). When `sample["labels"]` is an integer, this causes the application to crash.
The fix introduces a check to determine if the `labels` are sequence-like.
- If the labels are a sequence (the original expected format), the collator preserves the exact original logic.
- If the labels are integers, the collator now correctly broadcasts the integer label across all tokens of the corresponding `input_ids`.
This change makes the collator more robust to common data formats without altering its behavior for existing use cases, thereby preventing unexpected crashes. A new test case has been added to verify this fix.
Fixes #41652
## 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).
- [X] 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.
This change relates to data collators, which are often used with the Trainer. Tagging from the original issue and the trainer section.
@ArthurZucker @SunMarc
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PR_kwDOCUB6oc6uRGN0
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torch 2.9 still don't β€οΈ torchcodec 0.8 π
|
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# What does this PR do?
torchcodec 0.8 is out. With torch 2.8, work crash issues. with torch 2.9, some torchcodec issues.
Let's pin further torchcodec<0.8 for now
|
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PR_kwDOCUB6oc6uQ-gU
| 41,685
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[kernels] Add version to function mapping
|
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# What does this PR do?
We simply add a version attribute to the function kernel mapping to avoid breaking changes for kernels in the future
|
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PR_kwDOCUB6oc6uQ5kS
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Ci crying becausse torchcodec
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# What does this PR do?
torchcodec 0.8 is out. With torch 2.8, work crash issues. with torch 2.9, some torchcodec issues.
Let's pin further torchcodec<0.8 for now
|
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PR_kwDOCUB6oc6uQ12t
| 41,683
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π¨ Remove torch.fx support
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[] | 2025-10-17T09:19:08
| 2025-10-17T14:12:48
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MEMBER
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# What does this PR do?
As per the title. The API is old and very badly supported. It's also superseded by dynamo
|
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PR_kwDOCUB6oc6uQgYn
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torch 2.9 β€οΈ torchcodec now π (maybe)
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Reverts huggingface/transformers#41610
We need this PR otherwise the new torchcodec 0.8 will break the pinned torch 2.8
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|
Delete deprecations with end-cycle in v4.xx and v5.0
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# What does this PR do?
Deletes deprecation for the v5 release. cc @yonigozlan and @eustlb to verify that we can really delete them, since it is mostly deprecated stuff from processors
For core maintainers:
1. The max-size and pad-pixels args can't be deleted due to many models on the hub still using it. Instead I decided to stop raising warning and silently load max size if present. However users cannot anymore use these args when calling the processor
2. For SDPA attention weights, the warning is reworded to push us to update those models. Usually if model has the new attention API, the warning is raised internally in `sdpa_attention`, but for old models we have to manually duplicate the waning message
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I_kwDOCUB6oc7SFQ3n
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[BUG] Qwen3-Omni Training. AttributeError: 'Qwen3OmniMoeThinkerForConditionalGeneration' object has no attribute 'router_aux_loss_coef'
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PR_kwDOCUB6oc6uNwep
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Use the process_group from device_mesh to all_reduce in GatherParallel
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# What does this PR do?
<!--
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.
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<!-- Remove if not applicable -->
In`GatherParallel`, where the `device_mesh` is passed, but not used by the `all_reduce` call.
## 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.
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PR_kwDOCUB6oc6uNnCY
| 41,678
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Enable faiss-cpu on Windows
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# What does this PR do?
The current faiss-cpu supports Windows. Therefore the PR enables that test case.
|
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PR_kwDOCUB6oc6uNT8_
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Remove upper version bound of pandas
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# What does this PR do?
Let's skip pandas 2.3.0 and allow users to install newer versions.
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PR_kwDOCUB6oc6uNBXA
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Fix MarkDown syntax
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# What does this PR do?
This PR provides more fixes to the markdown files.
## 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?
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 @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
Integrations:
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
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Use | for Optional and Union typing
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# What does this PR do?
This PR replaces `Optional` and `Union` typing with `|` in some src files.
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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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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.
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[docs] Manual tp-plan
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Docs for #41435
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PR_kwDOCUB6oc6uKwbN
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[FIX]: CLIP support for flash-attention-3
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# What does this PR do?
<!--
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 #41668, Enabling CLIP to work with Flash attention 3
## 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.
@ArthurZucker @Cyrilvallez
<!-- 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 @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
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- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
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- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
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feat: add benchmark v2 ci with results pushed to dataset
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Reusing the old `benchmark.yml` workflow for the benchmark v2, we'll eventually rename and clean up everything once we're confident it works nicely.
Benchmark results will be pushed to a dataset, for now located [here](https://huggingface.co/datasets/hf-benchmarks/transformers).
I chose JSONL for simplicity of parsing in the frontend space, and file name have the following format: `f"benchmark_run_{timestamp}.jsonl"`.
Also did a little naming refactoring and added extra metadata
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PR_kwDOCUB6oc6uKT65
| 41,671
|
Fix graphormer model compilation with Cython 3.1.4
|
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Hitting this kind of error when running:
```
cython src/transformers/models/deprecated/graphormer/algos_graphormer.pyx
```
```
Error compiling Cython file:
------------------------------------------------------------
...
(nrows, ncols) = path.shape
assert nrows == ncols
cdef unsigned int n = nrows
cdef unsigned int max_dist_copy = max_dist
path_copy = path.astype(long, order='C', casting='safe', copy=True)
^
------------------------------------------------------------
src/transformers/models/deprecated/graphormer/algos_graphormer.pyx:88:28: undeclared name not builtin: long
```
This appears to have changed between cython==3.0 and cython==3.1. AFAICT the correct type to use here would be `int`. Switching to it makes the command succeed and generate an algos_graphormer.c file.
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PR_kwDOCUB6oc6uKQzo
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|
Add LightGlue fast image processor
|
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# What does this PR do?
As the title says
|
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I_kwDOCUB6oc7R-ED6
| 41,669
|
Remove import * usage from models, adds 10 seconds and 38k files
|
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[] | 2025-10-16T16:56:39
| 2025-10-20T14:08:37
| null |
CONTRIBUTOR
| null | null | null | null |
### System Info
any
### 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
The import * is not recommended for general use. "First, * imports are only meant as a shortcut in the REPL, not for general usage. You should just import to the module or the objects in the module to be clear about where something comes from." see https://github.com/microsoft/pylance-release/issues/7615#issuecomment-3378105085
models/__init__.py is bringing in an extra 38k files to typecheck. adding about 10 seconds of typechecking.
you can try 'pyright transformers --stats' to check performance
### Expected behavior
don't import the universe
| null |
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I_kwDOCUB6oc7R9tV3
| 41,668
|
CLIP incompatible with Flash Attention 3
|
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[] | 2025-10-16T16:28:27
| 2025-10-24T18:44:11
| 2025-10-24T18:44:11
|
NONE
| null | null | null | null |
### System Info
- `transformers` version: 4.57.0
- Platform: Linux-6.14.0-33-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- Huggingface_hub version: 1.0.0.rc4
### Who can help?
@ArthurZucker @Cyrilvallez
### 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
```
from diffusers import EulerDiscreteScheduler, StableDiffusionXLPipeline
import torch
import os
model_path =os.getenv('MODEL_PATH')
print(model_path)
pipe = StableDiffusionXLPipeline.from_pretrained(
model_path,
variant="fp16",
torch_dtype=torch.float16
).to("cuda")
pipe.fuse_qkv_projections()
pipe.text_encoder.config._attn_implementation = "flash_attention_3"
pipe.text_encoder_2.config._attn_implementation = "flash_attention_3"
prompt = ["A city at night with people walking around."] * 8
image = pipe(prompt, num_inference_steps=1).images[0]
```
Input into `_upad_input`
```
Starting with query_length: 77
query_layer shape: torch.Size([8, 77, 12, 64])
key_layer shape: torch.Size([8, 77, 12, 64])
value_layer shape: torch.Size([8, 77, 12, 64])
attention_mask shape: torch.Size([8, 1, 77, 77])
```
output from `_upad_input`
```
q shape: torch.Size([23408, 12, 64])
k shape: torch.Size([23408, 12, 64])
v shape: torch.Size([23408, 12, 64])
cu_seq_lens_q: torch.Size([8, 1, 78])
cu_seq_lens_k: torch.Size([8, 1, 78])
max_length_q: 0
max_length_k: 0
```
### Expected behavior
I'm trying to enable Flash Attention 3 for the CLIP encoder in SDXL.
Is the line in the forward pass supposed to include FA3 as well?
https://github.com/huggingface/transformers/blob/19df66dcba7690fab0b82105c154685c63016a21/src/transformers/models/clip/modeling_clip.py#L322
Currently when the attention_implementation is set to FlashAttention3 the attention_mask is set to the the causal_attention_mask which is 4D and when passed into [_upad_input](https://github.com/huggingface/transformers/blob/19df66dcba7690fab0b82105c154685c63016a21/src/transformers/modeling_flash_attention_utils.py#L237) which only supports a 2D attention_mask leads to garbage output https://github.com/huggingface/transformers/blob/19df66dcba7690fab0b82105c154685c63016a21/src/transformers/modeling_flash_attention_utils.py#L258
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PR_kwDOCUB6oc6uHzVc
| 41,667
|
Simplify pipeline padding logic
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| 2025-10-21T16:01:50
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Follow-up to #41636
When reviewing that PR I noticed the padding logic was cluttered and much more complex than it needed to be. We can eliminate a lot of code by just constructing shapes as lists and trusting in implicit slice dimensions
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PR_kwDOCUB6oc6uHJFS
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Remove deprecated `use_auth_token` parameter
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`use_auth_token` parameter has been deprecated for a very long time (2 years?) and will be completely removed from `huggingface_hub` in v1.0.
I think `transformers` v5 is a perfect timing to also remove it from this library. In practice there has been a deprecation cycle for a long time (with both `token` and `use_auth_token` being accepted) so friction should be minimal.
(cc @hanouticelina)
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PR_kwDOCUB6oc6uG-cB
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Fix dtype casting with quantization
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# What does this PR do?
We should always trust the dtype of the params in the meta model, except when the parameter is going to be quantized!
Got broken in https://github.com/huggingface/transformers/pull/41548, where we stopped looking at the `keep_in_fp_32_regex`, to instead trust the model dtypes directly!
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PR_kwDOCUB6oc6uG0S-
| 41,664
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[kernels] Final kernel removal π₯³
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# What does this PR do?
Removes the `mamba-ssm` kernel for `falcon-mamba` models and uses the kernel from the hub : https://huggingface.co/kernels-community/mamba-ssm
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PR_kwDOCUB6oc6uGfls
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[Fix] dots1 expert bias routing
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# What does this PR do?
This is a follow up to https://github.com/huggingface/transformers/pull/41647. I went over the model code again and noticed that the dots1 routing apparently also broke in https://github.com/huggingface/transformers/pull/40132. dots1 seems to be very close in architecture to Deepseek V3 but not explcitly dependent. Not sure if there is a more elegant way to fix this?
## 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
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
Integrations:
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
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- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
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PR_kwDOCUB6oc6uGUmo
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Small changes to benchmarking script
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This PR:
- adds throughput to the final pretty print at the end of the benchmark runs
- adds the information of the output shape to the decoded text of the output (usefull to double check if throughput is right)
- changes the behavior of the `run_benchmarks.py` script so that it will only run 3 configs unless instructed to do otherwise
This way, anyone can run a "quick"
```
python benchmark_v2/run_benchmarks.py --model-id "meta-llama/Meta-Llama-3-8B" -b 32 -s 128 -n 256
```
after changing the `generate` code or some model specific code to check perf did not take a big hit.
Thanks @SunMarc for the suggestion!
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PR_kwDOCUB6oc6uGMt9
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Improve package version check
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# What does this PR do?
Fixes https://github.com/huggingface/transformers/issues/41651. In very niche cases, a package may have several distributions, leading to potential issues in our current check (if the wrong one is first). This PR fixes it by checking if a distribution of the same name exists
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PR_kwDOCUB6oc6uF_Cd
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Add optional logits slicing support to GPT2LMHeadModel
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This PR adds support for an optional `logits_to_keep` argument in GPT2LMHeadModel.
It allows users to slice logits output for memory-efficient inference or analysis.
Example:
```python
outputs = model(**inputs, logits_to_keep=2)
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PR_kwDOCUB6oc6uFeCj
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Fix ckpt in docs
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# What does this PR do?
Fixes https://github.com/huggingface/transformers/issues/41640 and updates the ckpt to the correct one
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PR_kwDOCUB6oc6uFc9H
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improve `utils/check_bad_commit.py`
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# What does this PR do?
This script (used to find which commits fail certain failing tests on our CI) fails recently, due to the fact that the warning `sys:1: DeprecationWarning: builtin type swigvarlink has no __module__ attribute` was written to `stderr` and we use `stderr` to check if there is error.
This is not a good approach, we should instead check `result.returncode`. Also a few improvements added along this here
|
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PR_kwDOCUB6oc6uFN9-
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Erroring when KernelConfig is passed without use_kernels = True
|
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CONTRIBUTOR
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# What does this PR do?
as per title
|
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PR_kwDOCUB6oc6uEq9R
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purge HF_HUB_ENABLE_HF_TRANSFER; promote Xet
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PR_kwDOCUB6oc6uEbkt
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|
Switch to CB if cache_implementation == paged
|
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This PR fixes one of the issues raised in #41501
This PR adds `paged` as a static cache implementation and switches from `generate` to `generate_batch` if it's the chosen implementation. It also raises errors if an argument for `generate` is passed to `generate_batch` but is not supported, and warn the user that `generate_batch` is used.
The interface for the user stays the same, as the sample script below shows:
```
import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
if __name__ == '__main__':
model_path = "Qwen/Qwen3-0.6B"
tokenizer = AutoTokenizer.from_pretrained(
model_path,
trust_remote_code=True,
add_bos_token=False,
add_eos_token=False,
)
model = AutoModelForCausalLM.from_pretrained(
model_path,
dtype=torch.bfloat16,
trust_remote_code=True,
device_map="cuda",
).eval()
prompt="How are you today?"
chat_prompt=tokenizer.apply_chat_template(
[{"role" : "user", "content" : prompt}],
tokenize=False,
add_generation_prompt=True,
enable_thinking=False,
)
generation_config=GenerationConfig(
do_sample=True,
temperature=1.0,
top_p=1.0,
# num_return_sequences=8,
max_new_tokens=100,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
use_cache=True,
return_dict_in_generate=True,
output_logits=True,
)
chat_prompt_ids = tokenizer(chat_prompt, return_tensors="pt")["input_ids"].to(model.device)
print("Classic generate")
output = model.generate(input_ids=chat_prompt_ids, generation_config=generation_config)[0]
output_text = tokenizer.batch_decode(output[:, chat_prompt_ids.shape[1]:], skip_special_tokens=True)
print(output_text)
print("\nWith paged cache")
output = model.generate(input_ids=chat_prompt_ids, generation_config=generation_config, cache_implementation="paged")[0]
output_text = tokenizer.batch_decode(output[:, chat_prompt_ids.shape[1]:], skip_special_tokens=True)
print(output_text)
```
which outputs
```
Classic generate
`generation_config` default values have been modified to match model-specific defaults: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95, 'bos_token_id': 151643}. If this is not desired, please set these values explicitly.
["Hello! I'm here to help you with anything you need. How can I assist you today?"]
With paged cache
Detected cache_implementation=paged: switching to continuous batching. You should consider using generate_batch directly instead.
No behavior specified for use_cuda_graph, defaulting to self.use_cuda_graph = False because self.model.config._attn_implementation = 'paged|sdpa'. If you want to save memory, turn off cuda graphs, but they can improve performances.
Solving 1 requests: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 2.16request/s]
["Hello! I'm here to help you with anything you need. How can I assist you today? π"]
```
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PR_kwDOCUB6oc6uDZC2
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Improve LLaMA tokenizer error when vocab is missing: suggest installiβ¦
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β¦ng mistral-common\n\nAdd unit test to assert helpful message.
# What does this PR do?
<!--
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 # (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.
<!-- 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 @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
Integrations:
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
| null |
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PR_kwDOCUB6oc6uC9qj
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Double router compute?
|
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# What does this PR do?
Not sure what the intended fix is (force f32 or not) but currently the first is ignored
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I_kwDOCUB6oc7R39JW
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DataCollatorWithFlattening incompatible with sequence classification
|
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[] | 2025-10-16T09:40:49
| 2025-10-17T12:50:16
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NONE
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### System Info
- OS: Ubuntu 24.04
- python version 3.11.9
- transformers version 4.56.2
### Who can help?
@ArthurZucker
### 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
```python
from datasets import Dataset
from transformers import DataCollatorWithFlattening
ds = Dataset.from_list([
{"input_ids": [0, 1, 2, 3], "labels": 1}, {"input_ids": [4, 5, 6], "labels": 0}
])
collator = DataCollatorWithFlattening()
batch = collator(ds)
print(batch)
```
### Expected behavior
## Observed Error
```
2026 batch["input_ids"] += input_ids
2027 if is_labels_provided:
-> 2028 batch["labels"] += [separator_id] + sample["labels"][1:]
2029 else:
2030 batch["labels"] += [separator_id] + input_ids[1:]
TypeError: 'int' object is not subscriptable
```
## Expected Behavior
Batch the labels in a `[batch_size,]` int64 tensor
## Additional Context
This [line](https://github.com/huggingface/transformers/blob/8cb5963cc22174954e7dca2c0a3320b7dc2f4edc/src/transformers/data/data_collator.py#L2146C17-L2146C73) in the `DataCollatorWithFlattening` class raises an exception if the "labels" column in a dataset is a literal instead of a sequence. This happens in sequence classification where there is a label for the whole sequence instead of individual tokens.
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I_kwDOCUB6oc7R38qy
| 41,651
|
Erroneous flash attention version check
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NONE
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### System Info
- `transformers` version: 4.57.0.dev0
- Platform: Linux-6.11.0-29-generic-x86_64-with-glibc2.35
- Python version: 3.11.13
- Huggingface_hub version: 1.0.0.rc5
- Safetensors version: 0.6.2
- Accelerate version: not installed
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)
- Using distributed or parallel set-up in script?: no
- Using GPU in script?: yes
- GPU type: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
### Who can help?
@Cyrilvallez
### 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
1. create image
```Dockerfile
FROM pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel
RUN apt-get update && apt-get install -y wget git
RUN wget -nv https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu12torch2.8cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
RUN pip install flash_attn*
RUN pip install sgl-kernel
RUN git clone https://github.com/huggingface/transformers
RUN pip install -e transformers
```
2.
```python
from transformers.utils import import_utils
import_utils.is_flash_attn_2_available()
```
will report `False`.
### Expected behavior
It should report `True`.
The issue likely is because `import_utils._is_package_available` gets the package version from index `0` of `importlib.metadata.packages_distributions()["flash_attn"]`.
The distribution_name resolves to `sgl-kernel` instead of `flash_attention` in this case.
What about giving precedence to distributions that match the package name?
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PR_kwDOCUB6oc6uCr-v
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Remove randomly added script
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# What does this PR do?
cc @zucchini-nlp you added it in https://github.com/huggingface/transformers/pull/41474. Please be very careful about adding commits after we approve a PR, and always review the diffs of the PR before merging to avoid such issues!
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PR_kwDOCUB6oc6uCaO4
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Fix tokenization test
|
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# What does this PR do?
Fix the test - variable is referenced before assignment
|
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| 3,520,946,925
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PR_kwDOCUB6oc6uCYFO
| 41,648
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[v5] Delete legacy chat template saving
|
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|
# What does this PR do?
As per title
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PR_kwDOCUB6oc6uBxaB
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[Fix] Deepseek V3 expert bias routing
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CONTRIBUTOR
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# What does this PR do?
By chance we noticed that https://github.com/huggingface/transformers/pull/40132 seems to have introduced a bug in the Deepseek V3 routing implementation: The Deepseek-V3 technical report explicitly states
> Note that the bias term is only used for routing. The gating value, which will be multiplied with
the FFN output, is still derived from the original affinity score
This was the case in transformers until https://github.com/huggingface/transformers/pull/40132 which changed the routing code such that the gating values are now derived from the tensor with the added bias term. I wrote a quick fix for the Deepseek-V3 model in this PR, not sure if other models are also affected. Can you please have a look @ArthurZucker and confirm that this is indeed a bug?
## 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.
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- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
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- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
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- CIs: @ydshieh
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- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
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PR_kwDOCUB6oc6uBjvc
| 41,646
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Use | for Optional and Union typing
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| 2025-10-16T14:29:55
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# What does this PR do?
This PR replace `Optional` and `Union` typing with new `|` notation in files except the src ones.
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PR_kwDOCUB6oc6uBgNn
| 41,645
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Fix bark after #41445
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# What does this PR do?
Some Bark model classes are not `GenerationMixin` and have their own `generation` method.
#41445 makes change to `GenerationMixin` and those changes are not applied to those Bark model classes, then causes the bark tests to fail. One example is
> tests/models/bark/test_modeling_bark.py::BarkModelIntegrationTests::test_generate_batching
This PR adds `GenerationMixin` to those models and the tests pass now.
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PR_kwDOCUB6oc6uBQrd
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Fix Pylint warnings
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# What does this PR do?
This PR fixes some Pylint warnings.
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