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https://api.github.com/repos/huggingface/transformers/issues/40840 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40840/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40840/comments | https://api.github.com/repos/huggingface/transformers/issues/40840/events | https://github.com/huggingface/transformers/pull/40840 | 3,409,367,966 | PR_kwDOCUB6oc6oL8Eg | 40,840 | feat: add qwen2 pruning support | {
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This PR enables models with structured pruning to be loaded correctly. The model configuration files after structured pruning will be modified, as the layer_head_num and layer_inter_size parameters may vary across different layers of the network. When the Transformers library loads the config... | null | {
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https://api.github.com/repos/huggingface/transformers/issues/40839 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40839/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40839/comments | https://api.github.com/repos/huggingface/transformers/issues/40839/events | https://github.com/huggingface/transformers/pull/40839 | 3,409,296,262 | PR_kwDOCUB6oc6oLsqp | 40,839 | Simplify unnecessary Optional typing | {
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Remove `Optional` in some typing because the default value is not `None` and the variable is not checked against `is {not} None`. | {
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`is_causal` is not forwarded in `sdpa_attention_paged_forward`. If that is intentional, a comment should be added. | {
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### PR Description
**Add Top-H decoding (entropy-bounded truncation) as a LogitsWarper**
---
#### Summary
This PR adds **Top-H**, a new sampling method for text generation, as a `LogitsWarper` in 🤗 Transformers.
Top-H truncates the next-token distribution by greedily including the most probable tokens... | {
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Adding my name to contributor list
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- Platform: Linux-5.15.0-1048-aws-x86_64-with-glibc2.31
- Python version: 3.12.11
- Huggingface_hub version: 0.34.4
- Safetensors version: 0.5.3
- Accelerate version: 1.10.1
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it lays ground for my next PR, where I am aiming to unify `DefaultImagesKwargsFastInit`and `ImagesKwargs` since they are almost identical except for three kwargs (size_divisor, do_pad and pad_size)
This PR consolidates naming of the above there kwargs. Specifically:
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- Accelerate config: - compute_environment: LOCAL_MACHINE
- distributed_type: MULTI_GPU
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@ArthurZucker for review | {
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Hello,
I'm trying to run an offline inference over a dataset with 170k rows, each 100-500 tokens. That's sizeable, at least for me.
`transformers` seems to offer no support for batch inferencing. The current way `pipeline` when iterated over a `dataset` is by doing a for loop - computing one query ... | null | {
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I suddenly cannot run `Qwen/Qwen2.5-32B-Instruct` model locally! It happened all of a sudden.
Debugging gives:
```
(Pdb) PretrainedConfig.get_config_dict('Qwen/Qwen2.5-32B-Instruct') ... | {
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I expect more and more to follow suite and our current way of handling this, i... | {
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This PR fixes Florence 2 model kwargs issue with `num_items_per_batch`. Since BART decoder don't take any kwargs, we shouldn't pass them in forward, otherwise we get an error.
`TypeError: BartDecoder.forward() got an unexpected keyword argument 'num_items_in_batch'`
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This PR fixes Florence 2 model kwargs issue with `num_items_per_batch`. Since BART decoder don't take any kwargs, we shouldn't pass them in forward, otherwise we get an error.
`TypeError: BartDecoder.forward() got an unexpected keyword argument 'num_items_in_batch'`
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Should fix https://github.com/huggingface/transformers/issues/40815
- [x] Fix `return None` getter regression
- [x] Added tests for get_decoder weird behaviour in general (to be safe)
- [ ] Looking into the twin setter method as well | {
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This PR fixes Trainer remaining failing tests:
- sigopt is archived, so no need to put in integration
- fix hp api
- liger failing test | {
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"followers_url": "htt... | null | [] | 2025-09-11T14:49:29 | 2025-10-25T03:41:49 | null | MEMBER | null | null | null | null | In this issue we share our plan for the upcoming version 5 of transformers. We've talked about version 5 for years and it's finally around the corner! We'll release a blog post announcing the focus of this release shortly, and wanted to share what we believe the process will look like over the coming weeks.
- Soon, a ... | null | {
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For v5! Long due cleanup | null | {
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https://api.github.com/repos/huggingface/transformers/issues/40820 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40820/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40820/comments | https://api.github.com/repos/huggingface/transformers/issues/40820/events | https://github.com/huggingface/transformers/pull/40820 | 3,406,739,682 | PR_kwDOCUB6oc6oDBx5 | 40,820 | Add models to benchmarks | {
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"merged_at... | # What does this PR do?
This PR adds bert, gemma3, gpt, mistral3 and qwen2 to the new benchmarking pipeline.
## 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/t... | null | {
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https://api.github.com/repos/huggingface/transformers/issues/40819 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40819/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40819/comments | https://api.github.com/repos/huggingface/transformers/issues/40819/events | https://github.com/huggingface/transformers/pull/40819 | 3,406,663,817 | PR_kwDOCUB6oc6oCxB- | 40,819 | [`Jetmoe`] Fix RoPE | {
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"merged_at... | Seems like the integration tests on our CI also died for a while.
Fixes the rope dimension for jetmoe by setting a respective attribute mapping - the normal calculation is not valid, ie `hidden_dim / num_attn_heads`. For reference, why this is valid see https://github.com/huggingface/transformers/blob/895b3ebe418ebc... | {
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https://api.github.com/repos/huggingface/transformers/issues/40818 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40818/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40818/comments | https://api.github.com/repos/huggingface/transformers/issues/40818/events | https://github.com/huggingface/transformers/pull/40818 | 3,405,876,739 | PR_kwDOCUB6oc6oABSE | 40,818 | Fix TrainingArguments.parallelism_config NameError with accelerate<1.10.1 | {
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```python
NameError: name 'ParallelismConfig' is not defined
```
```python
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**Environment**
- Linux, A100 GPU (CUDA 12.1, driver version 530.30.02)
- python=3.10.0
- transformers=4.50.0 (upgrade to 4.56.1 leads to the same error)
### Who can help?
@ArthurZucker @gante
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] A... | {
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In the release of transformers v4.56.0, this PR https://github.com/huggingface/transformers/pull/39509 introduced a refactor of the public `get_decoder` method which previously existed on modes by moving it to the PreTrainedModel class.
Unfortunately this introduced a significant behavior change in th... | {
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Second linear layer in `Starcoder2`s MLP `c_proj` should be row-sharded and not column-sharded. Otherwise, column-sharding both `c_fc` and `c_proj` results in incorrect sharding and tensor shape mismatch.
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the oth... | {
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Transformers main branch (commit [0f1b128](https://github.com/huggingface/transformers/commit/0f1b128d3359a26bd18be99c26d7f04fb3cba914) )
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🎯 part of the effort to efforce better standardization
We have been migrating `past_key_values` from the old tuple of tuples of tensors format to the new `Cache` format. However, many type hints and docstrings were not updated accordingly -- our users are getting incorrect information fro... | {
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As the title says, this adds a test that will fail for models added after September 1st 2025, if they include a slow image processor but not a fast one.
Cc @qubvel as we discussed this | {
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Add a padding point to box embeds as is done in the original repo.
The prompt encoder of the original repo actually never uses its `_embed_boxes` method. Instead the boxesare first converted to points then passed to `_embed_points`, which has this padding logic whereas `_embed_boxes` doesn't... | {
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"merged_at... | # What does this PR do?
This PR fixes a couple of issues related to DP:
- hight fp16 norm_grad -> we need to prepare the optimizer, otherwise, we don't unscale the optimizer when doing gradient clipping + grad scaler
- Fix logic when using `num_items_in_batch`. For DP, when `num_items_per_batch is not None`, the ... | {
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<img width="1368" height="1212" alt="Image" src="https://github.com/user-attachments/assets/fd6da07d-23ce-42fb-a7d0-0ff646ae918c" />
As far as I know, original SigLIP2's loss contains three parts, Sigmoid loss, LocCa loss, and SILC/TIPS loss.
However, in the Transformers library, the loss for SigL... | null | {
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As the title says.
Fixes https://github.com/huggingface/transformers/issues/40770 | {
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https://api.github.com/repos/huggingface/transformers/issues/40796 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40796/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40796/comments | https://api.github.com/repos/huggingface/transformers/issues/40796/events | https://github.com/huggingface/transformers/pull/40796 | 3,402,569,212 | PR_kwDOCUB6oc6n0rQ1 | 40,796 | [`RMSNorm`] Fix rms norm init for models that center around 1 | {
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https://github.com/huggingface/transformers/blob/75202b09283ee48263642114013155034459195a/src/transformers/models/gemma3/modeling_gemma3.py#L146
The issue here that this accentuates computations with our standard init of 1 (1+1=2, do... | {
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"merged_at... | # Adding Support for Qwen3-VL Series
This PR introduces support for the upcoming **Qwen3-VL** models, including dense and MoE variants, as well as the Instruct and Thinking versions. As the next generation of the Qwen-VL family, Qwen3-VL brings notable advances in visual understanding while preserving strong pure-te... | {
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Currently, in model.generate, the Transformers implementation stores all logits on GPU until the generation is finished, at which point they are returned as a PyTorch tensor.
This design causes significant GPU memory usage, especially for long generations, since the logits for every token must rem... | null | {
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Draft PR which will allow us to have a strict type validation on all processing kwargs without having to add a dataclass object for each. The idea is to keep `TypedDict` for hinting and dynamically adapt a `TypedDict` to be compatible with `huggingface_hub.strict` validators
This will allo... | {
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If it's present on the hub config, and layer_types is not, it will be read | {
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Enables assisted generation on `Gemma3ForConditionalGeneration`. Most of the novelty consists of a more precise compile-compatible prefill detection. If the new pattern is desirable, I can push it into more models :)
(This also fixes flaky issues in `Gemma3Vision2TextModelTest::test_prompt... | {
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* 1 Handle loading non-existent checkpoints
Setting resume_from_checkpoint to true at the start of training will result in an error because the latest checkpoint cannot be found. Therefore, we should only set to false or null at the beginning. If you then interrupt training and want to res... | null | {
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Fixes a syntactic error. | {
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Add `Optional`, fix `np.array` and fix `noqa: F821` in typing. | {
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- `transformers` version: 4.56.1
- Platform: Linux-6.8.0-60-generic-x86_64-with-glibc2.35
- Python version: 3.11.11
- 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 (acceler... | {
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Fixes https://github.com/huggingface/transformers/issues/40731. It happens once in several runs and quite hard to reproduce. TBH I reproduced it only once and can't get the issue anymore, but from visual inspection looks like the root was in calling `cached_files` with several files
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- `transformers` version: 4.53.3
- Platform: Linux-6.14.0-1014-gcp-x86_64-with-glibc2.39
- Python version: 3.12.3
- Huggingface_hub version: 0.34.4
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It has been deprecated. | {
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"merged_at... | Fix `quantization_config=None` equivalence with omitted parameter in `AutoModel.from_pretrained`.
Currently, `AutoModel.from_pretrained` treats differently missing and `None` `quantization_config`. I think passing `None` should be treated the same as omitting the key altogether, not as an invalid quantization config... | null | {
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As the title says. | {
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- `transformers` version: 4.55.0
- Platform: Linux-5.10.0-1.0.0.28-x86_64-with-glibc2.31
- Python version: 3.10.18
- Huggingface_hub version: 0.34.4
- Safetensors version: 0.6.2
- Accelerate version: 1.7.0
- Accelerate config: not found
- DeepSpeed version: 0.16.9
- PyTorch version (accelerator?): 2... | {
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Fix typos in tests and util | {
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https://api.github.com/repos/huggingface/transformers/issues/40779 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40779/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40779/comments | https://api.github.com/repos/huggingface/transformers/issues/40779/events | https://github.com/huggingface/transformers/issues/40779 | 3,400,477,943 | I_kwDOCUB6oc7Kryz3 | 40,779 | AttributeError: module 'torchcodec' has no attribute 'decoders' | {
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I've trained and loaded my best model, however when inferring the model (see code below) I get this error
```
from transformers import pipeline
classifier = pipeline("audio-classification", model="my_awesome_min... | {
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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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"merged_at... | # Add FastAPI + Docker deployment example for Transformers inference
This PR adds a production-ready FastAPI service example that demonstrates how to deploy Hugging Face Transformers models with Docker containerization.
## What's included:
- FastAPI service supporting any Transformers pipeline
-... | {
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Currently `tokenizer.apply_chat_template` can be used to convert a chat in the form list of dicts into a formatted string for text generation. However, there is currently no convenient method for doing the reverse. A new function (hypothetically called `tokenizer.parse_chat_template`) could handle ... | null | {
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"merged_at... | ## Fix condition for emitting warning when generation exceeds max model length
### Purpose
The current transformers version prints inconsistent warnings when exceeding the max model length L.
For prefills of a prompt of max model length L (max_position_embeddings) no warning is printed. This is expected, as the ... | {
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"merged_at... | **Summary**
Renaming `get_state_dict()` method in `hf_quantizer` to `get_state_dict_and_metadata()` and updating functionality to return metadata along with state_dict.
This reduces confusion related to enabling torchao safetensor support built out in [this PR](https://github.com/huggingface/transformers/pull/40... | {
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`num_items_in_batch` ends up being on CPU when in multi-gpu ddp setting. When trying to compute the total number of items across devices, this fails as seen in #39896. This PR moves it to the correct device before gathering across gpus.
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(see title)
Fixes #40644 | {
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This PR adds the support of codes for the upcoming Qwen3-Next models. For information about Qwen, please visit:
👉 https://github.com/QwenLM/Qwen3
Special thanks to @Cyrilvallez and @ArthurZucker for their valuable feedback and thorough review of this PR! | {
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--- System Info ---
Platform: Linux-6.12.10-76061203-generic-x86_64-with-glibc2.35
Python: 3.11.13 | packaged by conda-forge | (main, Jun 4 2025, 14:48:23) [GCC 13.3.0]
--- PyTorch and CUDA Info ---
PyTorch Version: 2.7.1+cu128
Is CUDA available: True
CUDA Version: 12.8
cuDNN Version: 90701
GPU Name:... | {
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Transformers v5 | {
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- Python version: 3.11.13
- Huggingface_hub version: 0.34.4
- Safetensors version: 0.6.2
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: not installed
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Hi together,
is there a reason or any other thread where 3D models like those at mmdet3d are discussed to be implemented. I have not found any discussion.
Thanks
### Open source status
- [ ] The model implementation is available
- [ ] The model weights are available
### Provide useful links fo... | null | {
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❤️ Really hope @SunMarc can take a look at this PR. | {
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"merged_at... | Fixes #40754
This PR addresses a mismatch in `Llama4ForCausalLM` where the output of `Llama4TextModel` did not always provide `hidden_states` or `attentions`.
The fix replaces direct attribute access with `getattr(..., None)` to prevent crashes when these fields are missing.
What was changed:
- Updated `mod... | {
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https://api.github.com/repos/huggingface/transformers/issues/40756 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40756/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40756/comments | https://api.github.com/repos/huggingface/transformers/issues/40756/events | https://github.com/huggingface/transformers/pull/40756 | 3,394,269,704 | PR_kwDOCUB6oc6nY2Yw | 40,756 | [WIP] Add Canary | {
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"merged_at... | # What does this PR do?
For now, a simple draft of converted [canary-1b-v2](https://huggingface.co/nvidia/canary-1b-v2)'s tokenizer:
```python
from transformers import PreTrainedTokenizerFast
tokenizer = PreTrainedTokenizerFast.from_pretrained("eustlb/canary-1b-v2")
``` | null | {
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Add support for forecasting with Covariates | null | {
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**System Info**
- `transformers` version: 4.55.4
- Platform: Linux-6.15.9-201.fc42.x86_64-x86_64-with-glibc2.41
- Python version: 3.13.5
- Huggingface_hub version: 0.34.4
- Safetensors version: 0.6.2
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTo... | {
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"merged_at... | # What does this PR do?
If using `and/or_mask_function` arguments in `create_causal_mask`, it is important to apply those masks before any other deviations such as packed sequence, padding etc, as users expect to slightly deviate from the pattern, but not to take such internals into account!
Also take the opportu... | {
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https://api.github.com/repos/huggingface/transformers/issues/40752 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40752/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40752/comments | https://api.github.com/repos/huggingface/transformers/issues/40752/events | https://github.com/huggingface/transformers/issues/40752 | 3,393,471,719 | I_kwDOCUB6oc7KRETn | 40,752 | How to extract attention weights for the first generated token? | {
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**Description:**
Hi, I'm trying to extract the attention weights **of the first generated token** (i.e., the first new token produced by `generate()`) with respect to the input prompt. However, I'm observing inconsis... | {
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ubuntu24.04
vscode Jupyter extension
### Who can help?
@ArthurZucker
### 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)
... | {
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- `transformers` version: 4.56.0
- Platform: Linux-5.15.92-2.el8.navix.ncc.x86_64-x86_64-with-glibc2.39
- Python version: 3.12.3
- Huggingface_hub version: 0.34.4
- Safetensors version: 0.5.2
- Accelerate version: 1.10.1
- Accelerate config:
```
distributed_type: FSDP
mixed_precision: bf16
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"merged_at... | # What does this PR do?
This PR replaces the JetMoe model card with a new model card matching the format introduced in [#36979](https://github.com/huggingface/transformers/issues/36979).
Note: The attention mask image link is currently blank because the image has not been uploaded yet. I will update it once the d... | {
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The previous implementation always averaged losses, which under-reported values in token-level training.
The new implementation provides a clean, token-aware reduction method that works consistently across single and multi-GPU se... | null | {
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cc: @muellerzr @SunMarc | {
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Currently, T5 cannot take 4D attention masks (batch_size, num_heads, seq_len, seq_len) as inputs. Passing a 4D attention_mask and a 4D decoder_attention_mask like so leads to a shape-related exception :
```python
import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration
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Always import torch in some source files that are PT only. | null | {
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Most fixes are using `np.ndarray`. | {
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