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Adds `--gradient_accumulation_steps` argument to the image classification no_trainer example script, addressing issue #18436.
## Motivation
Gradient accumulation allows training with larger effective batch sizes by accumulating gradients over multiple batches before performing an optim... | null | {
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transformer version: latest commit
in file: https://github.com/huggingface/transformers/blob/main/src/transformers/utils/auto_docstring.py#L1121 (and any other files using emoji symbols)
This "🚨" symbol cause encoding error as the system charset is not UTF-8 encoded. (especially on Windows, the UTF... | null | {
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Hi this is using TRL but it seems like a lower level issue.
I'm training a variant of Qwen3 (Intern-S1-mini) but I'm not using the vision tower so it's effectively Qwen3-8B. I've been doing finetuning and checking different attention implementations i.e. SPDA vs. Flash Attention 2. However, I've been... | null | {
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Failed to parse argument "include_num_input_tokens_seen" using HfArgumentParser, code is unusable.
error: argument --include_num_input_tokens_seen/--include-num-input-tokens-seen: Truthy value expected: got non_padding but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).
### Who ... | null | {
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Fix small typo. Without consequences, but still confusing.
Fixes https://github.com/huggingface/transformers/issues/41919 | null | {
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This PR adds graceful fallback to slow tokenizers when `AutoTokenizer.from_pretrained()` with `use_fast=True` fails at runtime. Subsequently, this PR should improve robustness issue in tokenizer loading
## Problem
Currently, when `use_fast=True` is specified but the fast tokenizer fails... | null | {
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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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Why:
Without this, the constant isn’t exported when using
from transformers.utils.constants import *.
Impact:
- Keeps the constants module consistent.
- No functional changes or breaking impact. | null | {
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transformers version: 4.57.1
python version: 3.11
### Who can help?
@Cyrilvallez @eustlb
### 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 ... | null | {
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Related to https://github.com/huggingface/transformers/pull/41922, this PR corrects the assignment of the `rope_scaling` dictionary present on some Gemma 3 pre-trained models on HF Hub when normalizing to the new `rope_parameters` value.
## Before submitting
- [ ] This PR fixes a typo or ... | null | {
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This PR fixes an issue where quantized models (e.g., RedHatAI/Qwen2.5-VL-7B-Instruct-quantized.w8a8) fail to load due to a dtype incompatibility during weight initialization.
Problem
When loading quantized models (dtype=torch.int8), the method _load_pretrained_model() still calls initialize_w... | null | {
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It prevents import errors during dependency checks and allows transformers to load normally.
Changes made:
- Updated `setup.py` to conditionally check for safetensors
- Improved dependency handling logic
Tested locally: verified th... | null | {
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```
1.tests/models/video_llama_3/test_modeling_video_llama_3.py::VideoLlama3IntegrationTest::test_small_model_integration_test
2.tests/models/video_llama_3/test_modeling_video_llama_3.py::VideoLlama3IntegrationTest::test_small_model_integration_test_batch_wo_ima
ge
3... | null | {
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Should fix #41929 . The `check_model_inputs` / `can_record_outputs` interaction is not always trivial and models with several entrypoints such as `VisionModel` vs `VisionTransformer` are missing some, adding it here. Also added a modification in `generic` to make sure the flag was captured, n... | null | {
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macos 26.0
python 3.10
pytorch 2.7.1
transformers 4.57.1
### Who can help?
@yonigozlan @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... | null | {
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"merged_at... | - Add clear error message when mistral-common is missing for AutoTokenizer loading Voxtral
- Add unit test
# What does this PR do?
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"merged_at... | Fixing a few deepspeed tests
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https://api.github.com/repos/huggingface/transformers/issues/41924 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/41924/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/41924/comments | https://api.github.com/repos/huggingface/transformers/issues/41924/events | https://github.com/huggingface/transformers/issues/41924 | 3,563,519,251 | I_kwDOCUB6oc7UZv0T | 41,924 | `output_attentions=True` always warns for non-`"eager"` attention implementations, even when a custom AttentionInterface backend does return attention weights | {
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- `transformers` version: 4.57.1
- Platform: Linux-5.10.233-223.887.amzn2.x86_64-x86_64-with-glibc2.26
- Python version: 3.10.19
- Huggingface_hub version: 0.36.0
- Safetensors version: 0.6.2
- Accelerate version: not installed
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch... | null | {
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https://api.github.com/repos/huggingface/transformers/issues/41923 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/41923/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/41923/comments | https://api.github.com/repos/huggingface/transformers/issues/41923/events | https://github.com/huggingface/transformers/pull/41923 | 3,563,463,159 | PR_kwDOCUB6oc6wPhqk | 41,923 | fix some ut failures on XPU w/ torch 2.9 | {
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"merged_at... | cases are below, all passed. @ydshieh , pls help review, thx very much.
> tests/models/aya_vision/test_modeling_aya_vision.py::AyaVisionIntegrationTest::test_small_model_integration_generate_text_only
> tests/models/aya_vision/test_modeling_aya_vision.py::AyaVisionIntegrationTest::test_small_model_integration_forwa... | {
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"merged_at... | # What does this PR do?
Fixes the rope_parameters in the weights conversion script for Gemma 3.
These should be:
```
local => default @ 10_000.0
global => linear(8.0) @ 1_000_000.0
``` | null | {
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"merged_at... | If run below 2 cases on 2 accelerators:
> pytest -rA tests/models/speech_to_text/test_modeling_speech_to_text.py::Speech2TextModelIntegrationTests::test_generation_librispeech
>pytest -rA tests/models/speech_to_text/test_modeling_speech_to_text.py::Speech2TextModelIntegrationTests::test_generation_librispeech_bat... | null | {
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https://api.github.com/repos/huggingface/transformers/issues/41920 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/41920/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/41920/comments | https://api.github.com/repos/huggingface/transformers/issues/41920/events | https://github.com/huggingface/transformers/pull/41920 | 3,562,379,835 | PR_kwDOCUB6oc6wL7Po | 41,920 | evaluate>=0.4.6 is needed | {
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"merged_at... | Some HF Transformers tests/examples fail with main and `evaluate < 0.4.6`
Fixing:
```
stderr: [rank0]: Traceback (most recent call last):
stderr: [rank0]: File "/code/users/stas/github/transformers-alst-integration/examples/pytorch/question-answering/run_qa.py", line 692, in <module>
stderr: [rank0]: main(... | {
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In LFM2-VL image_processing_lfm2_vl_fast.py line 212 following the MEAN and STD from imagenet is used for preprocessing.
However it seems like they are swapped:
image_mean = IMAGENET_STANDARD_STD
image_std = IMAGENET_STANDARD_MEAN
or is this correct ?
### Who can help?
@Cyrilvallez
### Inf... | null | {
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Refactor `test_tensor_parallel.py` by removing `subprocess`. This way we can easily debug with breakpoint the test if it fails. On top of that, I made the tests more robust by testing on more than `--nproc_per_node> 2.` `--nproc_per_nodes = 8` crashes because the llama model we use is too ti... | null | {
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- TODO:
- update my branch
- explain changes
## 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
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Run slow v2! | null | {
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- `transformers` version: 4.57.1
- Platform: macOS-26.0.1-arm64-arm-64bit
- Python version: 3.12.0
- Huggingface_hub version: 0.35.3
- Safetensors version: 0.5.3
- Accelerate version: 1.7.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.6.0 (NA)
-... | null | {
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@ArthurZucker @Cyri... | {
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I'm using `peft.LoraConfig()` to fine tune `ModernBertForTokenClassification`. According to the trail of the exception i'm getting, the `forward()` method of `ModernBertForTokenClassification` is missing `**kwargs`:
```
│ /home/gsgs2tk/ADA_ModelingFramework/.venv/lib/python3.11/site-packages/peft/tun... | null | {
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transformers==5.0.0dev
autoawq==0.2.9
autoawq_kernels==0.0.9
torch==2.6.0+cu124
### Who can help?
Due to PR #35235, the `past_key_values` is no longer a returned value of attention modules.
However, when using AWQ models with Fused modules [AWQ Fused modules docs](https://huggingface.co/docs/transfo... | null | {
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transformers version: 4.57.1
python version: 3.11
### 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 det... | null | {
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https://api.github.com/repos/huggingface/transformers/issues/41904 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/41904/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/41904/comments | https://api.github.com/repos/huggingface/transformers/issues/41904/events | https://github.com/huggingface/transformers/pull/41904 | 3,559,133,986 | PR_kwDOCUB6oc6wBO5K | 41,904 | Fix inaccurate eval and train loss computation with variable batch sizes | {
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"merged_at... | Fixes #41898
When drop_last=False (default), the last batch may contain fewer samples than per_device_eval_batch_size. Using a fixed batch_size to repeat the scalar loss causes the last batch to be over-represented in the final average loss calculation.
Changes:
- Trainer: Use observed_batch_size instead of fixe... | null | {
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"merged_at... | # What does this PR do?
Fixes #41902
This PR prevents tokens from being passed twice when loading adapters in `from_pretrained`. Fixes TypeError: `find_adapter_config_file()` got multiple values for keyword argument 'token'.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can ... | {
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master branch: transformers @ git+https://github.com/huggingface/transformers.git@1f0b490a2c42eb129dccc69031ccb537058689c4
its not possible to load models in latest transformers version. Because adaptor_config gets a token added to it which results in duplicate value being passed later on.
### Who c... | {
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Update pytree registration for DynamicCache. Before this change the cache values are flatten as `(key0, key1, ..., value0, value1, ...)`. This change matches the old cache interface by flattening to `(key0, value0, key1, value1, ...)`. This ordering matches transformers conversion and is cons... | null | {
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https://api.github.com/repos/huggingface/transformers/issues/41900 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/41900/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/41900/comments | https://api.github.com/repos/huggingface/transformers/issues/41900/events | https://github.com/huggingface/transformers/pull/41900 | 3,558,032,145 | PR_kwDOCUB6oc6v9alw | 41,900 | Remove unnecessary slicing in sdpa_attention_forward | {
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"merged_at... | The slicing in sdpa_attention_forward was there only because some masks were not constructed correctly (I was told). When the key size is dynamic, the slice op also prevents torch.export from correctly reasoning about its size.
cc @vasqu
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"merged_at... | # What does this PR do?
This PR adds separate checkpoint limits for regular checkpoints and best model checkpoints in the Trainer.
## Changes:
- Adds `save_total_limit_best` parameter to `TrainingArguments` to control the number of best checkpoints kept
- Separates checkpoint management logic so that best model... | null | {
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Same issue f... | null | {
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"merged_at... | # What does this PR do?
This PR adds MXFP4 and MXFP8 backwards support in combination with MXFP4 forward, allowing for lightning-fast QAT on Blackwell GPUs.
It's blocked by `QuTLASS v0.2.0` and `fp_quant v0.3.0` release we're hoping to release within a day or two.
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"merged_at... | Summary:
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
# 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 refle... | null | {
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**Propose adding a new vision model, `MiniViT`, a compact Vision Transformer variant optimized for small image datasets (e.g., CIFAR-10/100).**
Include model implementation, configuration, feature extractor, tests, example script, and model card for Hub integration.
**Motivation:**
- There is d... | null | {
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Mostly:
- Make `docker/transformers-all-latest-gpu/Dockerfile` more readable and clean as we now need to handle `torchcodec` (using `cpu`) along with `torch` (`cuda`)
- Remove `push-ci` stuff. We are not paying any attention to it. We have something running on a very small subset now.
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Reverts https://github.com/huggingface/transformers/pull/41411, because for some packages like `optimum` we don't have loaders as expected from most packages. | {
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"merged_at... | Fixes #41875
The encoder-decoder's cross attn cache was initialized with the same layer types as the self attn cache. However, the model only uses full attention for its cross attentions making this initialization faulty. This only happens with fa2 as it relies on the mask to match with the size of the cache while s... | null | {
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PEFT is an optional dependency of transformers with a min version of 0.5.0. However, starting with transformers v5, older PEFT versions will not work anymore (see #41406). The minimum PEFT version will be 0.18.0.
This PR updates the PEFT integration to require PEFT 0.18.0. This allows us t... | null | {
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[FG-CLIP2](https://arxiv.org/abs/2510.10921) is a new generation of text-image cross-modal model excels in fine-grained discrimination and embedding. It is the foundation model for fine-grained vision-language understanding in both English and Chinese.
Across 29 datasets and 8 diverse tasks... | null | {
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When using the DINOv3ViTLayer in fp16 mode, the training process gets killed and throws an error.
`
exitcode : -8 (pid: 2536) 1967
error_file: <N/A> 1968
traceback : Signal 8 (SIGFPE) received by PID xxxx`
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### Environment
* transformers: 4.53.2
* torch: 2.7.1+cu128
* Model: Qwen3-30B-A3B
### Who can help?
@seven-mile @ArthurZucker
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQ... | null | {
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"merged_at... | Here I updated the test_processor_utils.py file with BertTokenizerFast, RobertaTokenizerFast and tested with HF token...
<img width="1199" height="331" alt="image" src="https://github.com/user-attachments/assets/ee533b6a-6a6f-4829-a693-5cd61d3e1bf0" />
Fix: #41837 | null | {
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[FG-CLIP2](https://arxiv.org/abs/2510.10921) is a new generation of text-image cross-modal model excels in fine-grained discrimination and embedding. It is the foundation model for fine-grained vision-language understanding in both English and Chinese.
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Adds a comprehensive quickstart guide for CPU inference optimization using 🤗 Optimum.
## Motivation
**Problem**: Users frequently ask "How do I speed up CPU inference?" but documentation is scattered across Transformers and Optimum repos, making it hard to get started.
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In older version of transformers, LlamaAttention init attribute num_heads.
class LlamaAttention(nn.Module):
def __init__(self, config):
self.num_heads = config.num_attention_heads
self.head_dim = config.hidden_size // config.num_attention_heads
However, in the recent versions, th... | null | {
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System info:
- `transformers` version: 4.57.1
- Platform: Linux-5.4.210-39.1.bert-x86_64-with-glibc2.39
- Python version: 3.12.3
- Huggingface_hub version: 0.34.4
- Safetensors version: 0.5.3
- Accelerate version: 1.10.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch v... | null | {
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This PR improves the `batch_decode()` method in `PreTrainedTokenizerBase` to robustly distinguish between single sequences and batches of sequences, preventing unexpected behavior when users pass flat lists of token IDs.
## Problem
The current implementation of `batch_decode()` does not... | {
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`main/src/transformers/tokenization_utils_base.py`
does not robustly distinguish between a **single sequence (`list[int]`)** and a **batch of sequences (`list[list[int]]`)**.
As a result, passing a single flat list (or 1D numpy/torch tensor) can cause unexpected be... | {
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This PR fixes a bug in the default initialization of `image_rows` and `image_cols` in the Idefics3 and SmolVLM processors.
## Problem
The original code incorrectly used `len(text)` to determine the size of the default `image_rows` and `image_cols` lists:
```python
image_rows = inputs.... | null | {
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Python3.12
Output of pip freeze:
```
certifi==2025.10.5
charset-normalizer==3.4.4
filelock==3.20.0
fsspec==2025.9.0
hf-xet==1.2.0
huggingface-hub==0.36.0
idna==3.11
numpy==2.3.4
packaging==25.0
protobuf==6.33.0
PyYAML==6.0.3
regex==2025.10.23
requests==2.32.5
safetensors==0.6.2
sentencepiece==0.2.1
tok... | null | {
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This PR introduces an automatic CPU safety heuristic that ensures models loaded with unsafe dtypes (e.g., float16 or bfloat16) on CPU are handled gracefully.
Previously, users loading such models on CPU could face unexpected slowdowns, NaNs, or runtime errors.
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This PR adds **CPU dtype safety heuristics** to the model loading flow (`PreTrainedModel.from_pretrained`) to prevent potential slowdowns or errors when models are inadvertently loaded in `torch.float16` or `torch.bfloat16` on CPU.
It introduces a utility `apply_cpu_safety_settings()` in `... | {
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<!--StartFragment--><html><head></head><body><h1>RFC: Automatic CPU dtype fallback and thread optimization in Transformers</h1>
<h2>Summary</h2>
<p>Add automatic dtype validation and thread optimization for CPU inference in Transformers. When users specify <code>dtype=torch.float16</code> or <code>torch.b... | {
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Fixes #41738
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
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This PR fixes Auto classes (`AutoImageProcessor` and `AutoVideoProcessor`) to properly support dynamically registered processors via the `.register()` method.
## Problem
When users call `AutoImageProcessor.register()` or `AutoVideoProcessor.register()`, the registration only updates the... | null | {
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This PR fixes several issues in the auto processing modules:
1. **Fixes `AutoImageProcessor.register` bug**: Removes incorrect validation logic that was copied from tokenizers. The validation checked for `slow_image_processor_class` attribute consistency, but fast image processors don't ha... | null | {
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- `transformers` version: 4.57.1
- Platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.35
- Python version: 3.10.18
- 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 (acceler... | null | {
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InternVL3_5_Flash employ an additional router to use dynamic resolution of input images and this is not compatible with previous class "InternVLForConditionalGeneration". As a result, I would like to inquire whether it is possible to support the Flash version of InternVL3-5.
<img width="430" hei... | null | {
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Exactly same setup and same machine on Linux is almost 2x faster
9.5 second / it vs 5.8 second / it
On Windows it can't utilize GPU power it utilizes like 250 watt out of 575 watt
What can be culprit?
transformers==4... | null | {
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Adds two context parallel tests for the CI
<!--
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 awesom... | null | {
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Hello! I need your help because I can't verify my identity via email: I receive a link, open it, but get a blank page and nothing else(((
I've tried several times.
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An o... | {
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As discussed with @vasqu | {
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- `transformers` version: 4.54.0.dev0
- Platform: Linux-6.6.105+-x86_64-with-glibc2.35
- Python version: 3.12.12
- Huggingface_hub version: 0.35.3
- Safetensors version: 0.6.2
- Accelerate version: 1.11.0
- Accelerate config: not found
- DeepSpeed version: not installed
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Adds a couple of missing methods found in `PreTrainedTokenizer`, this allows Mistral models to be used with outlines and thus work with Pydantic models.
Builds on this PR https://github.com/huggingface... | null | {
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"merged_at... | # What does this PR do?
Fixes #37196
## Before submitting
- [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://disc... | {
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This pr is just a copy of the previous pr. Further tests and fixes are needed.
Fixes #37196
## Before submitting
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section... | {
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"merged_at... | Non-vmap creation of masks. These work with all our base masks and we only default back to vmap when using patterns we cannot guarantee (i.e. additional and/or masks).
Note:
- Non-vmap works with every mask that has anything index based
- Merged old/new sdpa under one function --> easier maintenance imo
- Executo... | null | {
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"merged_at... | Description:
Replaced deepcopy with a shallow copy to avoid AttributeError with fast tokenizers.
Added a test (tests/utils/test_processor_utils.py) to verify multiple tokenizers save/load correctly.
Fix #41837 | null | {
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Loading checkpoints for model Qwen/Qwen3-Next-80B-A3B-Instruct was very slow
this change brought down checkpoint loading times from 10mins+ to about 1.5mins
the load method is called over 4 million times (123,799 per layer and there are 41 layers). Looping through the state dict (size ~1500) was therefore very ... | {
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"merged_at... | While I was working on `parse_response` I noticed that the UX was annoying in places because I had to keep decoding and then calling `parse_response`, which added a lot of boilerplate. Making `parse_response` optionally able to handle token IDs directly cut down on that a lot, so I figured I'd fix it now while there's ... | {
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"merged_at... | This PR changes the benchmarking workflow to gather more data per benchmark. cc. @McPatate I only changed one workflow file, not sure I did not miss others.
It also adds a check on the benchmark config to disable FA when it is not installed to avoid an exception. | null | {
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