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PR_kwDOCUB6oc6sFm-U
| 41,342
|
Make default RoPE path explicit in RotaryEmbedding classes
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# 🧩 Pull Request: Make default RoPE path explicit in Llama RotaryEmbedding initialization
## 📜 Summary
This PR addresses issue [#39753](https://github.com/huggingface/transformers/issues/39753), ensuring that the *default* Rotary Positional Embedding (RoPE) initialization path is made explicit in the Llama model.
Previously, `inv_freq` was implicitly assigned, which went against the library’s philosophy of explicit initialization for reproducibility and clarity.
This change:
- Makes the `"default"` RoPE path explicit by selecting `ROPE_INIT_FUNCTIONS["default"]` when `self.rope_type == "default"`.
- Uses `ROPE_INIT_FUNCTIONS[self.rope_type]` for non-default cases.
- Keeps the initialization logic (`inv_freq`, `attention_scaling`) delegated to the RoPE function—no manual computation is done in the constructor.
The modification is minimal, isolated, and maintains backward compatibility.
---
## 🧠 Motivation
Following the discussion in [#39753](https://github.com/huggingface/transformers/issues/39753), the goal is to make RoPE initialization behavior transparent and avoid implicit defaults.
This aligns with 🤗 Transformers’ philosophy of **clarity and explicit model configuration**.
---
## 🛠️ Changes Made
In `transformers/src/transformers/models/llama/modeling_llama.py` and more 20 models:
```python
self.config = config
if self.rope_type == "default":
self.rope_init_fn = ROPE_INIT_FUNCTIONS["default"]
else:
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
```
- Delegates all logic to the existing RoPE functions.
- Reduces potential maintenance risk by avoiding any new computation paths.
- Keeps `inv_freq` and `attention_scaling` initialization consistent with the rest of the library.
---
## ✅ Checklist
- [x] The PR title summarizes the contribution.
- [x] Linked to issue [#39753](https://github.com/huggingface/transformers/issues/39753).
- [x] Tested locally — no change in model outputs or test failures.
- [x] Backward compatible (no breaking changes).
---
## 🧪 Testing
- Verified initialization behavior for both `"default"` and non-default RoPE types.
- Confirmed no change in model outputs.
- Ran relevant test subset:
```bash
pytest
```
---
## 💬 Additional Notes
This is a small but meaningful cleanup that improves code readability and aligns initialization logic with the library’s explicit philosophy.
No other files or RoPE logic were touched.
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Cannot install latest transformers version 4.57.0 in free-threaded python version 3.14
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### System Info
On Debian 12 with
`Python 3.14.0rc3 free-threading build (main, Oct 3 2025, 22:25:41) [GCC 12.2.0]`
the following command fails: `pip install transformers`, since hf-xet cannot be installed due to the following error:
```
Building wheels for collected packages: hf-xet
Building wheel for hf-xet (pyproject.toml) ... error
error: subprocess-exited-with-error
× Building wheel for hf-xet (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [263 lines of output]
Running `maturin pep517 build-wheel -i /home/x/.threaded/bin/python --compatibility off`
Python reports SOABI: cpython-314t-x86_64-linux-gnu
Computed rustc target triple: x86_64-unknown-linux-gnu
Installation directory: /home/x/.cache/puccinialin
Rustup already downloaded
Installing rust to /home/x/.cache/puccinialin/rustup
warn: It looks like you have an existing rustup settings file at:
warn: /home/x/.rustup/settings.toml
warn: Rustup will install the default toolchain as specified in the settings file,
warn: instead of the one inferred from the default host triple.
info: profile set to 'minimal'
info: default host triple is x86_64-unknown-linux-gnu
warn: Updating existing toolchain, profile choice will be ignored
info: syncing channel updates for 'stable-x86_64-unknown-linux-gnu'
info: default toolchain set to 'stable-x86_64-unknown-linux-gnu'
Checking if cargo is installed
cargo 1.90.0 (840b83a10 2025-07-30)
⚠️ Warning: You specified the python source as /tmp/pip-install-haqniebn/hf-xet_cf9de919019944f9af86b1a64876b431/hf_xet/python, but the python module at /tmp/pip-install-haqniebn/hf-xet_cf9de919019944f9af86b1a64876b431/hf_xet/python/hf_xet is missing. No python module will be included.
🔗 Found pyo3 bindings with abi3 support
🐍 Not using a specific python interpreter
📡 Using build options features from pyproject.toml
⚠️ Warning: CPython 3.14t at /home/x/.threaded/bin/python does not yet support abi3 so the build artifacts will be version-specific.
Compiling proc-macro2 v1.0.101
Compiling unicode-ident v1.0.18
Compiling libc v0.2.175
Compiling cfg-if v1.0.1
Compiling shlex v1.3.0
Compiling memchr v2.7.5
Compiling itoa v1.0.15
Compiling once_cell v1.21.3
Compiling pin-project-lite v0.2.16
Compiling serde v1.0.219
Compiling bytes v1.10.1
Compiling futures-core v0.3.31
Compiling zerocopy v0.8.26
Compiling futures-sink v0.3.31
Compiling cc v1.2.33
Compiling ryu v1.0.20
Compiling serde_json v1.0.142
Compiling tracing-core v0.1.34
Compiling futures-channel v0.3.31
Compiling pin-utils v0.1.0
Compiling futures-io v0.3.31
Compiling getrandom v0.3.3
Compiling futures-task v0.3.31
Compiling stable_deref_trait v1.2.0
Compiling slab v0.4.11
Compiling crossbeam-utils v0.8.21
Compiling smallvec v1.15.1
Compiling base64 v0.22.1
Compiling autocfg v1.5.0
Compiling thiserror v2.0.15
Compiling lazy_static v1.5.0
Compiling pkg-config v0.3.32
Compiling option-ext v0.2.0
Compiling target-lexicon v0.12.16
Compiling fnv v1.0.7
Compiling litemap v0.8.0
Compiling byteorder v1.5.0
Compiling writeable v0.6.1
Compiling zeroize v1.8.1
Compiling bytemuck v1.23.2
Compiling quote v1.0.40
Compiling crossbeam-queue v0.3.12
Compiling heed-traits v0.8.0
Compiling rustls-pki-types v1.12.0
Compiling http v1.3.1
Compiling syn v2.0.106
Compiling mio v1.0.4
Compiling socket2 v0.6.0
Compiling getrandom v0.2.16
Compiling synchronoise v1.0.1
Compiling page_size v0.4.2
Compiling rand_core v0.9.3
Compiling untrusted v0.9.0
Compiling icu_normalizer_data v2.0.0
Compiling constant_time_eq v0.3.1
Compiling arrayref v0.3.9
Compiling icu_properties_data v2.0.1
Compiling syn v1.0.109
Compiling bitflags v2.9.2
Compiling arrayvec v0.7.6
Compiling pyo3-build-config v0.23.5
Compiling dirs-sys v0.5.0
Compiling ring v0.17.14
Compiling lmdb-rkv-sys v0.11.2
Compiling blake3 v1.8.2
Compiling anstyle v1.0.11
Compiling httparse v1.10.1
Compiling percent-encoding v2.3.1
Compiling safe-transmute v0.11.3
Compiling http-body v1.0.1
Compiling dtor-proc-macro v0.0.5
Compiling dirs v6.0.0
Compiling thiserror v1.0.69
Compiling dtor v0.0.6
Compiling ppv-lite86 v0.2.21
Compiling heck v0.5.0
Compiling ctor-proc-macro v0.0.6
Compiling rustls v0.23.31
Compiling try-lock v0.2.5
Compiling tower-service v0.3.3
Compiling want v0.3.1
Compiling shellexpand v3.1.1
Compiling ctor v0.4.3
Compiling aho-corasick v1.1.3
Compiling anyhow v1.0.99
Compiling subtle v2.6.1
Compiling rand_chacha v0.9.0
Compiling log v0.4.27
Compiling regex-syntax v0.8.5
Compiling rand v0.9.2
Compiling atomic-waker v1.1.2
Compiling utf8parse v0.2.2
Compiling anstyle-parse v0.2.7
Compiling form_urlencoded v1.2.1
Compiling sync_wrapper v1.0.2
Compiling anstyle-query v1.1.4
Compiling ipnet v2.11.0
Compiling rustix v1.0.8
Compiling utf8_iter v1.0.4
Compiling colorchoice v1.0.4
Compiling is_terminal_polyfill v1.70.1
Compiling tower-layer v0.3.3
Compiling openssl-probe v0.1.6
Compiling anstream v0.6.20
Compiling rustls-native-certs v0.8.1
Compiling webpki-roots v1.0.2
Compiling rand_core v0.6.4
Compiling num-traits v0.2.19
Compiling iri-string v0.7.8
Compiling linux-raw-sys v0.9.4
Compiling typenum v1.18.0
Compiling strsim v0.11.1
Compiling clap_lex v0.7.5
Compiling version_check v0.9.5
Compiling clap_builder v4.5.44
Compiling generic-array v0.14.7
Compiling rand_chacha v0.3.1
Compiling http-body-util v0.1.3
Compiling fastrand v2.3.0
Compiling either v1.15.0
Compiling predicates-core v1.0.9
Compiling regex-automata v0.4.9
Compiling mockall_derive v0.13.1
Compiling itertools v0.14.0
Compiling rand v0.8.5
Compiling derivative v2.2.0
Compiling synstructure v0.13.2
Compiling tempfile v3.20.0
Compiling uuid v1.18.0
Compiling cfg-if v0.1.10
Compiling static_assertions v1.1.0
Compiling heapify v0.2.0
Compiling time-core v0.1.4
Compiling num-conv v0.1.0
Compiling powerfmt v0.2.0
Compiling termtree v0.5.1
Compiling crc32fast v1.5.0
Compiling more-asserts v0.3.1
Compiling time-macros v0.2.22
Compiling deranged v0.4.0
Compiling predicates-tree v1.0.12
Compiling gearhash v0.1.3
Compiling predicates v3.1.3
Compiling lock_api v0.4.13
Compiling colored v2.2.0
Compiling csv-core v0.1.12
Compiling downcast v0.11.0
Compiling serde_derive v1.0.219
Compiling tokio-macros v2.5.0
Compiling zerofrom-derive v0.1.6
Compiling tracing-attributes v0.1.30
Compiling yoke-derive v0.8.0
Compiling futures-macro v0.3.31
Compiling zerovec-derive v0.11.1
Compiling displaydoc v0.2.5
Compiling tokio v1.47.1
Compiling thiserror-impl v2.0.15
Compiling async-trait v0.1.89
Compiling futures-util v0.3.31
Compiling pin-project-internal v1.1.10
Compiling rustls-webpki v0.103.4
Compiling tracing v0.1.41
Compiling zerofrom v0.1.6
Compiling yoke v0.8.0
Compiling error_printer v0.14.5 (/tmp/pip-install-haqniebn/hf-xet_cf9de919019944f9af86b1a64876b431/error_printer)
Compiling thiserror-impl v1.0.69
Compiling zerovec v0.11.4
Compiling zerotrie v0.2.2
Compiling regex v1.11.1
Compiling clap_derive v4.5.45
Compiling pin-project v1.1.10
Compiling serde_repr v0.1.20
Compiling tinystr v0.8.1
Compiling potential_utf v0.1.2
Compiling icu_collections v2.0.0
Compiling whoami v1.6.1
Compiling icu_locale_core v2.0.0
Compiling fragile v2.0.1
Compiling twox-hash v2.1.1
Compiling parking_lot_core v0.9.11
Compiling lz4_flex v0.11.5
Compiling time v0.3.41
Compiling file_utils v0.14.2 (/tmp/pip-install-haqniebn/hf-xet_cf9de919019944f9af86b1a64876b431/file_utils)
Compiling icu_provider v2.0.0
Compiling futures-executor v0.3.31
Compiling num-integer v0.1.46
Compiling retry-policies v0.4.0
Compiling futures v0.3.31
Compiling icu_properties v2.0.1
Compiling icu_normalizer v2.0.0
Compiling pyo3-macros-backend v0.23.5
Compiling pyo3-ffi v0.23.5
Compiling sha2-asm v0.6.4
Compiling countio v0.2.19
Compiling clap v4.5.45
Compiling half v2.6.0
Compiling regex-syntax v0.6.29
Compiling oneshot v0.1.11
Compiling scopeguard v1.2.0
Compiling protobuf v3.7.2
Compiling num-bigint v0.4.6
error: failed to run custom build command for `pyo3-ffi v0.23.5`
Caused by:
process didn't exit successfully: `/tmp/pip-install-haqniebn/hf-xet_cf9de919019944f9af86b1a64876b431/hf_xet/target/release/build/pyo3-ffi-f89ed39e89227bf3/build-script-build` (exit status: 1)
--- stdout
cargo:rustc-check-cfg=cfg(Py_LIMITED_API)
cargo:rustc-check-cfg=cfg(Py_GIL_DISABLED)
cargo:rustc-check-cfg=cfg(PyPy)
cargo:rustc-check-cfg=cfg(GraalPy)
cargo:rustc-check-cfg=cfg(py_sys_config, values("Py_DEBUG", "Py_REF_DEBUG", "Py_TRACE_REFS", "COUNT_ALLOCS"))
cargo:rustc-check-cfg=cfg(invalid_from_utf8_lint)
cargo:rustc-check-cfg=cfg(pyo3_disable_reference_pool)
cargo:rustc-check-cfg=cfg(pyo3_leak_on_drop_without_reference_pool)
cargo:rustc-check-cfg=cfg(diagnostic_namespace)
cargo:rustc-check-cfg=cfg(c_str_lit)
cargo:rustc-check-cfg=cfg(rustc_has_once_lock)
cargo:rustc-check-cfg=cfg(fn_ptr_eq)
cargo:rustc-check-cfg=cfg(Py_3_7)
cargo:rustc-check-cfg=cfg(Py_3_8)
cargo:rustc-check-cfg=cfg(Py_3_9)
cargo:rustc-check-cfg=cfg(Py_3_10)
cargo:rustc-check-cfg=cfg(Py_3_11)
cargo:rustc-check-cfg=cfg(Py_3_12)
cargo:rustc-check-cfg=cfg(Py_3_13)
cargo:rerun-if-env-changed=PYO3_CROSS
cargo:rerun-if-env-changed=PYO3_CROSS_LIB_DIR
cargo:rerun-if-env-changed=PYO3_CROSS_PYTHON_VERSION
cargo:rerun-if-env-changed=PYO3_CROSS_PYTHON_IMPLEMENTATION
cargo:rerun-if-env-changed=PYO3_PRINT_CONFIG
--- stderr
error: The configured Python interpreter version (3.14) is newer than PyO3's maximum supported version (3.13)
= help: please check if an updated version of PyO3 is available. Current version: 0.23.5
= help: The free-threaded build of CPython does not support the limited API so this check cannot be suppressed.
warning: build failed, waiting for other jobs to finish...
💥 maturin failed
Caused by: Failed to build a native library through cargo
Caused by: Cargo build finished with "exit status: 101": `env -u CARGO PYO3_BUILD_EXTENSION_MODULE="1" PYO3_ENVIRONMENT_SIGNATURE="cpython-3.14-64bit" PYO3_PYTHON="/home/x/.threaded/bin/python" PYTHON_SYS_EXECUTABLE="/home/x/.threaded/bin/python" "cargo" "rustc" "--features" "pyo3/extension-module" "--message-format" "json-render-diagnostics" "--manifest-path" "/tmp/pip-install-haqniebn/hf-xet_cf9de919019944f9af86b1a64876b431/hf_xet/Cargo.toml" "--release" "--lib"`
Rust not found, installing into a temporary directory
Error: command ['maturin', 'pep517', 'build-wheel', '-i', '/home/x/.threaded/bin/python', '--compatibility', 'off'] returned non-zero 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 hf-xet
Failed to build hf-xet
error: failed-wheel-build-for-install
× Failed to build installable wheels for some pyproject.toml based projects
╰─> hf-xet
```
apparently, supporting this is current not planned (?) https://github.com/huggingface/xet-core/issues/512
Current fallback is installing older versions of huggingface_hub and transformers to bypass this issue (f.e. transformers 4.49.0, huggingface_hub 0.31.4)
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Steps to reproduce the issue:
1. Setup a new virtual environment with Python 3.14 via `python3.14t -m venv .venv`
2. Install latest transformers via `.venv/bin/pip install transformers`
### Expected behavior
Install should just work (?)
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PR_kwDOCUB6oc6sFgSU
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🌐 [i18n-KO] Translated sam_hq.md to Korean
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# What does this PR do?
Translated the sam_hq.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 (번역 누락/중복 검사)
- [x] Grammar Check (맞춤법 검사)
- [x] Review or Add new terms to glossary (용어 확인 및 추가)
- [x] Check Inline TOC (e.g. `[[lowercased-header]]`)
- [x] Check live-preview for gotchas (live-preview로 정상작동 확인)
## Who can review? (Initial)
@jungnerd, @yijun-lee, @Kim-Ju-won, @FacerAin, @judy-choi, @AhnJoonSung, @maximizemaxwell, @nsbg
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I_kwDOCUB6oc7PoSNO
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Transformers 4.57.0 declares compatibility with Python>=3.9 but is only compatible with >=3.10
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[] | 2025-10-04T09:08:52
| 2025-10-23T12:17:36
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CONTRIBUTOR
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### System Info
- `transformers` version: 4.57.0
- Platform: macOS-15.7-arm64-arm-64bit
- Python version: 3.9.6
- 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?): 2.8.0 (NA)
- 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>
### Who can help?
_No response_
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
1. Use Python 3.9 and do `pip install -U "transformers[torch]"`
2. `from transformers import Trainer`
Error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<frozen importlib._bootstrap>", line 1055, in _handle_fromlist
File "/omitted/.venv/lib/python3.9/site-packages/transformers/utils/import_utils.py", line 2317, in __getattr__
module = self._get_module(self._class_to_module[name])
File "/omitted/.venv/lib/python3.9/site-packages/transformers/utils/import_utils.py", line 2347, in _get_module
raise e
File "/omitted/.venv/lib/python3.9/site-packages/transformers/utils/import_utils.py", line 2345, in _get_module
return importlib.import_module("." + module_name, self.__name__)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/omitted/.venv/lib/python3.9/site-packages/transformers/trainer.py", line 311, in <module>
class Trainer:
File "/omitted/.venv/lib/python3.9/site-packages/transformers/trainer.py", line 5593, in Trainer
def _get_num_items_in_batch(self, batch_samples: list, device: torch.device) -> int | None:
TypeError: unsupported operand type(s) for |: 'type' and 'NoneType'
```
### Expected behavior
Currently, Transformers [requires Python>=3.9](https://pypi.org/project/transformers/4.57.0/) but only works with Python>=3.10
I see two possible solutions:
- Python 3.9 is reaching its End Of Life this month, so I think it can be dropped
- Modify the implementations to remove syntaxes that are only supported in >=3.10 (such as `|` instead of `Union`)
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PR_kwDOCUB6oc6sE59l
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Add Setup section to README for dev dependencies and doc-builder
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NONE
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Title:
Add Setup section for installing dev dependencies and doc-builder
Description:
This PR adds a Setup section to docs/README.md that guides contributors on installing the necessary development dependencies and the Hugging Face doc-builder tool. This makes it easier for new contributors to generate and preview documentation locally.
Motivation & Context:
Improves contributor onboarding with clear setup instructions.
Ensures the documentation can be built locally without errors.
Checklist:
Improves documentation.
Follows contributor guidelines.
No new tests are required (documentation-only change).
Who can review:
Any contributor familiar with documentation or the docs setup.
@zucchini-nlp tagging for prioritising review
|
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PR_kwDOCUB6oc6sDN5q
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make some ut cases pass on xpu w/ latest torch
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make below 12 cases pass on XPU w/ latest torch.
> tests/models/llava/test_modeling_llava.py::LlavaForConditionalGenerationIntegrationTest::test_pixtral
> tests/models/llava/test_modeling_llava.py::LlavaForConditionalGenerationIntegrationTest::test_pixtral_4bit
> tests/models/llava/test_modeling_llava.py::LlavaForConditionalGenerationIntegrationTest::test_pixtral_batched
> tests/models/aya_vision/test_modeling_aya_vision.py::AyaVisionIntegrationTest::test_small_model_integration_batched_generate
> tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_4b_batch
> tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_4b_batch_crops
> tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_4b_multiimage
> tests/models/glm4v/test_processor_glm4v.py::Glm4vProcessorTest::test_apply_chat_template_video_frame_sampling
> tests/models/internvl/test_modeling_internvl.py::InternVLLlamaIntegrationTest::test_llama_small_model_integration_batched_generate
> tests/models/internvl/test_modeling_internvl.py::InternVLQwen2IntegrationTest::test_qwen2_small_model_integration_batched_generate
> tests/models/llava_onevision/test_modeling_llava_onevision.py::LlavaOnevisionForConditionalGenerationIntegrationTest::test_small_model_integration_test_multi_image
> tests/models/llava_onevision/test_modeling_llava_onevision.py::LlavaOnevisionForConditionalGenerationIntegrationTest::test_small_model_integration_test_multi_image_nested
@SunMarc @ydshieh , pls help review, thx very much.
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I_kwDOCUB6oc7Pllwo
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is there a bug in group_videos_by_shape for qwenvl video preprocessiong?
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### System Info
in src/transformers/video_utils.py,
group_videos_by_shape
grouped_videos = {shape: torch.stack(videos, dim=0) for shape, videos in grouped_videos.items()}, where each video is of shape BTCHW. This will create a new dimension.
However, in qwenvl video preprocess
batch_size, grid_t, channel = patches.shape[:3]
It does not consider the additional dimension created in group_videos_by_shape
I think we should use torch.cat, not torch.stack?
@yonigozlan @molbap
### 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
running video preprocessing with list of video inputs, each with different shape
### Expected behavior
run without error
|
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PR_kwDOCUB6oc6sCaDl
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Add `logits_to_keep` to many older CausalLM models
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# What does this PR do?
Fixes #40984
Adds `logits_to_keep` to many (older) `ForCausalLM` models that inherit from `GenerationMixin`.
Also consistently renames to `loss` and `logits`, and removes some code for float casting and mapping labels to the logits' device for models where that is already handled by the loss function (e.g. `gpt_neo`).
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR. @Rocketknight1
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PR_kwDOCUB6oc6sCVnR
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fix(packaging): remove leftover optax extra (align with #41030, fixes #41331)
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## What does this PR do?
Removes the leftover `optax` extra from Transformers packaging.
- `optax` was intentionally removed in #41030, but the cherry-pick for v4.57.0 was incomplete.
- As a result, the lock file contained `optax (<empty>)`, breaking Poetry parsing (#41331).
- This PR deletes the stale `optax` entry from `setup.py`, cleans up `pyproject.toml`, and regenerates `dependency_versions_table.py`.
## Fixes
Fixes #41331
## Before submitting
- [x] Removed stale dependency (`optax`)
- [x] Verified `deps_table_update` runs successfully
- [x] Followed contribution guidelines
## Who can review?
@ArthurZucker @Cyrilvallez
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PR_kwDOCUB6oc6sBdkM
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[WIP]: Add DeepseekVLV2 Model
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Fixes #36111
This is a draft PR for adding deepseek_vl_v2 to the transformers library.
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PR_kwDOCUB6oc6sBZ8W
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fix asr ut failures
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4 tests failed with AttributeError: 'AudioDecoder' object has no attribute 'copy':
`pytest -rA tests/pipelines/test_pipelines_automatic_speech_recognition.py::AutomaticSpeechRecognitionPipelineTests::test_speculative_decoding_whisper_non_distil
pytest -rA tests/test_pipeline_mixin.py::AutomaticSpeechRecognitionPipelineTests::test_speculative_decoding_whisper_non_distil
pytest -rA tests/pipelines/test_pipelines_automatic_speech_recognition.py::AutomaticSpeechRecognitionPipelineTests::test_whisper_prompted
pytest -rA tests/test_pipeline_mixin.py::AutomaticSpeechRecognitionPipelineTests::test_whisper_prompted`
after fixing, they all passed
@SunMarc @ydshieh , pls help review, thx very much.
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### System Info
**Description**
After upgrading to transformers 4.57.0, Poetry fails to generate the lock file or install dependencies, showing:
`Could not parse version constraint: <empty>`
**Steps to reproduce**
1. Create a new Poetry project:
```
poetry new test-transformers
cd test-transformers
poetry add transformers==4.57.0
```
2. Run: `poetry install`
3. Error occurs.
**Observed behavior**
- Poetry lock file contains: `optax (<empty>)`
- Installation fails with version parsing error.
**Expected behavior**
- Lock file should correctly parse extras.
- Installation should succeed like previous version (e.g., 4.56.0).
**Workaround**
```
Pin transformers to 4.56.0:
poetry add transformers==4.56.0
```
**Environment**
OS: Windows 11
Python: 3.12.0
Poetry: 2.2.1
**Additional context**
It seems that the metadata for extra optax in 4.57.0 is empty or invalid. This breaks PEP 508 compliance and causes Poetry to fail parsing the version constraints.
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
1. Create a new Poetry project:
```
poetry new test-transformers
cd test-transformers
poetry add transformers==4.57.0
```
2. Run: `poetry install`
3. Error occurs.
**Observed behavior**
- Poetry lock file contains: `optax (<empty>)`
- Installation fails with version parsing error.
### Expected behavior
**Expected behavior**
- Lock file should correctly parse extras.
- Installation should succeed like previous version (e.g., 4.56.0).
**Workaround**
```
Pin transformers to 4.56.0:
poetry add transformers==4.56.0
```
**Workaround**
```
Pin transformers to 4.56.0:
poetry add transformers==4.56.0
```
**Environment**
OS: Windows 11
Python: 3.12.0
Poetry: 2.2.1
**Additional context**
It seems that the metadata for extra optax in 4.57.0 is empty or invalid. This breaks PEP 508 compliance and causes Poetry to fail parsing the version constraints.
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PR_kwDOCUB6oc6sAxC_
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|
Unskip and fix offline mode tests, use HF_HUB_OFFLINE, make hermetic
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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 #41311
## 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.
https://github.com/huggingface/transformers/issues/41311
- [ ] 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: @zach-huggingface @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @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_kwDOCUB6oc6sAk0X
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|
Fix GIL=0 segfault and Add GIL=0 compat for regex paths
|
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# What does this PR do?
Adds (Full) Regex and (Partial) Tokenization GIL=0 free-threading support. Tested up to Python 3.14T.
In simple terms, Transformers code that relies on regex will segfault under true concurrency. I have confirmed with the regex maintainer that the latest GIL=0–compatible regex package is not GIL=0 safe. This has been corroborated by the submitted unit test in this PR, which demonstrates the segfault. Regex caches and reuses compiled patterns internally and executes them, which makes it inherently unsafe without the GIL.
The core idea is not to make the entire Transformers library GIL=0 safe all at once, but rather to adapt it piece by piece until everything is as GIL=0 compatible as possible.
This PR wraps existing regex calls into a thread-locked, protected serial execution pipeline. Files that import regex only need to adjust their import path to `from util.safe import regex` to minimize migration pain.
```diff
- import regex as re
+ from ...utils.safe import regex as re
```
The current safe wrapper is modular and can be extended to cover additional modules in the future as needed or as issues are discovered.
Since Transformers evolves more rapidly than most packages, introducing a local wrapper may be a pragmatic step while waiting for an upstream fix, which may be delayed. Additionally, many if not most Python APIs are not inherently thread-safe, so applying local wrappers is often necessary beyond just external packages.”
Two new unit test files are included:
A non-crashing test suite that proves the code works under GIL=0 with thread load.
A crash-demonstration test that proves regex code paths segfault without the added protection under thread load.
[utils/safe.py](https://github.com/huggingface/transformers/compare/main...Qubitium:transformers:regex-thread-safety?expand=1#diff-a7a62ce35afa10519218152098da57fe8ad5012b0068cfcb8e7ced98084e1517)
[tests/utils/test_safe.py](https://github.com/huggingface/transformers/compare/main...Qubitium:transformers:regex-thread-safety?expand=1#diff-c36421043b36005fb56f58552a69701e7694997a64c1083816b888494f2666ac)
[tests/utils/test_safe_crash.py](https://github.com/huggingface/transformers/compare/main...Qubitium:transformers:regex-thread-safety?expand=1#diff-2c867bbc58cad710595119fe34ae6389ba7f088f799714b2e7c6e93e72893bc1)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?
## Who can review?
@ArthurZucker @itazap @SunMarc @Cyrilvallez @gante @ydshieh @stevhliu
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PR_kwDOCUB6oc6r_X1G
| 41,328
|
Fix incorrect assignment in `update_device_map` for GPTQ quantizer
|
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# What does this PR do?
## 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?
@SunMarc @MekkCyber
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I_kwDOCUB6oc7Phsoe
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Add DEIMv2
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### Model description
Looks like [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) could be a great addition to the library.
I don't have the bandwidth right now to work on the integration, but happy to review a PR if anyone is interested in adding the model!
### Open source status
- [x] The model implementation is available
- [x] The model weights are available
### Provide useful links for the implementation
_No response_
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PR_kwDOCUB6oc6r_Sk-
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Deprecate warmup_ratio
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# What does this PR do?
This PR deprecates `warmup_ratio`. Instead we ask the user to use `warmup_steps` instead as we now allow passing float values. This is something that was already done for a few args like `eval_steps` .
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PR_kwDOCUB6oc6r_KwG
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Fixing a typo for BLT model
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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 # (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 @
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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:
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- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
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- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
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- 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_kwDOCUB6oc6r--L6
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`JetMoe` Fix jetmoe after #40132
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# What does this PR do?
A typo got in in #40132
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PR_kwDOCUB6oc6r-4T0
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deprecate `overwrite_output_dir`
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# What does this PR do?
This PR deprecates `overwrite_output_dir`. This arg has no utility at all tbh as this was kind of replaced by `resume_from_checkpoint`. It was used to overwrite the checkpoint when we didn't want to resume the training. It was only used in examples. Sylvain also mentioned that in this thread https://discuss.huggingface.co/t/no-skipping-steps-after-loading-from-checkpoint/6880/14
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PR_kwDOCUB6oc6r-DBi
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Fix lr_scheduler_parsing
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# What does this PR do?
This PR fixes `lr_scheduler_kwargs` type to `Optional[Union[dict, str]] `as HfArgumentParser doesn't handled well args that needs to parsed into dict. For now, it is better to just follow what is done for fsdp_config for example.
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PR_kwDOCUB6oc6r95kf
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[v5] Delete left traces of feature extractor
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# What does this PR do?
After rebasing https://github.com/huggingface/transformers/pull/40793 and getting new changes on `TypedDict`, I found out that some traces from feature extractor were still left. Thanks for type validation we can catch inconsistencies easily :)
This PR goes over the deprecated properties as well and deletes them. Also it deletes `current_processor` which was also deprecated for v5
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PR_kwDOCUB6oc6r9v8w
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Security/fuyu
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# What does this PR do?
Remove a reference to a compromised github repo.
For reference, all conversion scripts are excluded from releases. https://github.com/huggingface/transformers/blob/217ff1e4ef6b006d601d0fa834c2bd68c79b66cd/utils/release.py#L135-L143
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PR_kwDOCUB6oc6r9ZOv
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|
Use torch._check instead of a test in Gemma3Model
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# What does this PR do?
``torch.export.export`` fails because of a test checking two dimension are equal. This PR replaces the test by ``torch._check``.
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PR_kwDOCUB6oc6r8gpM
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[causallm tester] automate pipeline mappings + bloom tests
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# What does this PR do?
👉 `CausalLMModelTest`-level changes:
- automate `pipeline_model_mapping` whenever possible -- when `all_model_classes` contains the default classes, then `pipeline_model_mapping` must also contain the default mappings. This should reduce room for human error 🙏
👉 `Bloom` (the original target of this PR)
- `CausalLMModelTest` refactor
- moves integration tests into the correct tester
👉 misc
- removes `get_large_model_config` in multiple model testers (this function was never being used)
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Minor security fix for `ssh-runner.yml`
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# What does this PR do?
It's minor as it could not be triggered by external people. But just to be more cautious.
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PR_kwDOCUB6oc6r76bd
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tests: add dtype/device parity check for GPT‑2
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#### Improve test reliability and coverage:
- Add a minimal dtype/device parity test to catch obvious precision/device regressions.
#### What’s changed- Added dtype/device parity test for GPT‑2:
- Compares CPU fp32 vs CUDA fp16 logits for identical weights (tolerance ≤ 5e‑3).
- Gated by `@require_torch_gpu`.
#### Files touched
- tests/models/gpt2/test_modeling_gpt2.py
#### Test plan
- Focused: `pytest -q tests/models/gpt2/test_modeling_gpt2.py::GPT2ModelTest::test_dtype_device_parity_logits_fp32_cpu_vs_fp16_cuda`
- Smoke: `pytest -q tests/models/gpt2/test_modeling_gpt2.py -k parity`
Checklist
[x] Minimal diff, follows repo conventions
[x] Tests added/updated
[x] Style/lint passes locally
[x] No runtime code changes
@ydshieh @Rocketknight1 @CyrilVallez
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[model deprecations] Define new version-based model deprecation/deletions with user warnings/exceptions
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# What does this PR do?
PR 1 out of 3 regarding model deprecation/deletions for v5
We now define a new `{model type: last version where it was supported}` mapping, which we use to:
- automatically set deprecation warnings (when set version > current version)
- automatically trigger informative exceptions in AutoClasses (when set version < current version, i.e. after the corresponding model is deleted)
This should improve our messaging to the users and allow us to stop investing energy in low-usage architectures.
🤗 inspired by our [vllm](https://github.com/vllm-project/vllm/blob/ef283548f75122b0e8ce49ce2548fbb49446d7c7/vllm/model_executor/models/registry.py#L334) friends 🤗
________________
Follow-up PRs:
- 2 out of 3: Move a few models into the deprecated folder, as discussed on slack (target deletion version: 5.2, two minor versions after the first release that will have the deprecation warning)
- 3 out of 3: Delete models that were previously in the `deprecated` folder (most of them don't work in `main` in any case 👀 ). If it's not too complex, define import-time exceptions for the deleted classes (e.g. when a user tries to call `from transformers import XXXModel`, `XXXModel` being a deleted class).
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🚨 [v5] Rendundant code in nested configs
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# What does this PR do?
This PR deletes `from_xxx_config` and similar methods from everywhere and instead ensures that the config init can accept subconfigs of all formats (dict or object)
Along the way, I found that not all configs call super init in the end and thus they can't set recursively attn implementation on subconfigs. I added a test for it and fixed all cases
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Jitter noise PR
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## Issue Description
This pull request addresses a bug in the Switch Transformers architecture where the jitter noise (intended to be applied only for routing decisions) was also being unintentionally applied to the expert inputs.
Fixes : #33969
## Problem Statement
In Switch Transformers, a small amount of jitter noise is added to the inputs at routing time to ensure route diversity. However, these jittered inputs were incorrectly passed along to the experts, which contradicts the original paper’s design and led to unexpected discrepancies in outputs.
## Root Cause
It was discovered that the code used the same hidden states for both routing and expert processing. When jitter noise was enabled in training mode, it directly modified these states in place, causing the experts to receive noisy inputs.
## Implementation
1. We now clone the original hidden states before applying jitter noise.
2. A separate copy is used exclusively for computing router logits and probabilities.
3. The unchanged hidden states are then fed to the experts to maintain the original semantics.
## Screenshot
<img width="1293" alt="Screenshot 2025-01-23 at 1 14 26 AM" src="https://github.com/user-attachments/assets/2d46438d-6344-4994-aa18-1e14376597e7" />
## Test Cases
1. **test_router_training_mode**
• Objective: Ensures that jitter noise is only applied during training.
• Checks that outputs differ between consecutive runs (due to noise) but original inputs remain unchanged.
2. **test_router_jitter_noise_separation **
• Objective: Verifies that jitter noise affects only the router’s internal computations and not the expert inputs.
• Confirms the logits differ when jitter is applied, while the main input stays the same.
3. **test_expert_inputs_consistency**
• Objective: Asserts that all expert inputs remain consistent, even when jitter is applied during training.
• Uses a forward hook on the first expert to capture its inputs across multiple runs and compares them.
With these changes and test additions, we ensure that Switch Transformers adhere to the original design while preserving backward compatibility and correctness.
**cc :** @ArthurZucker
original pr refrence : #35847
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tests: unskip and fix offline mode test using HF_HUB_OFFLINE + hermetic cache warmup
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# What does this PR do?
The offline test was skipped and brittle due to reliance on TRANSFORMERS_OFFLINE and pre-warmed caches. Standardize on HF_HUB_OFFLINE and make the test self-contained and deterministic.
Fixes #41311
Changes:
- Unskip `test_offline_mode`.
- Implement two-step subprocess approach:
- Step 1: Warm cache online by loading config, model, tokenizer, and a small pipeline.
- Step 2: Run offline with `HF_HUB_OFFLINE=1` and a socket.socket monkey-patch to fail on any network access; load everything from local cache.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?
- Updated `tests/utils/test_offline.py::OfflineTests::test_offline_mode` to be hermetic (two-step subprocess: cache warmup online, then offline with `HF_HUB_OFFLINE=1` and socket mock).
### How to validate
Run: `pytest -q tests/utils/test_offline.py::OfflineTests::test_offline_mode`
@Cyrilvallez
@Rocketknight1
@ydshieh
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I_kwDOCUB6oc7PdcbV
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Unskip and fix test_offline_mode by using HF_HUB_OFFLINE and hermetic cache warmup
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`tests/utils/test_offline.py::OfflineTests::test_offline_mode` was skipped and relied on `TRANSFORMERS_OFFLINE`. Update to use `HF_HUB_OFFLINE=1` and make the test hermetic by warming the cache in a first subprocess run, then running offline with socket mocking to forbid network access.
### Affected area:
File: `tests/utils/test_offline.py`
Test: `OfflineTests::test_offline_mode`
### Problem:
Test was `@unittest.skip` with a TODO.
Behavior depended on environment set after import and on external cache state.
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v4.57.0 Branch
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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.
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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),
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:
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- continuous batching: @remi-or @ArthurZucker @McPatate
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- trainer: @zach-huggingface @SunMarc
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- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
Integrations:
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- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @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_kwDOCUB6oc6r6eRj
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Fix jamba
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# What does this PR do?
Jamba was mostly destroyed in https://github.com/huggingface/transformers/pull/40132, this PR fixes it!
Slow tests are good
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PR_kwDOCUB6oc6r6Ydv
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Subconfig is a class attribute
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# What does this PR do?
As per title, it should be a class attribute for vision-only models. VLMs already use class attribute. If it is a property, it is unavailable before we init the class. Having it as a class attribute will be needed for integrating type validation and transforming all configs to a dataclass
Also cleaned up some duplicate code. An attribute map already maps those keys, so an extra property is not needed
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Update torch minimum version to 2.3
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CONTRIBUTOR
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# What does this PR do?
PyTorch 2.9 is released. Looking forward to lift the PT minimum version again.
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TiledFusedLogitsLoss (LigerFusedLinearCrossEntropy, FLCE) Flag in `from_pretrained()`
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### Feature request
Add a native `tiled_fused_logits_loss` flag in `from_pretrained()` that enables memory-efficient cross entropy computation across different backends.
Example API:
```python
from_pretrained(..., tiled_fused_logits_loss=Union[bool, List["auto", "liger-kernel", "deepspeed"])
```
Supported options could include:
- `"liger-kernel"` : use [Liger Kernel](https://github.com/linkedin/liger-kernel) (triton)
- `"deepspeed"` : use [DeepSpeed/ALST implementation](https://github.com/deepspeedai/DeepSpeed/blob/16c1bf429f10db3e681b49fac37d641909aad69d/deepspeed/runtime/sequence_parallel/ulysses_sp.py#L898) (pure pytorch), but still performing the same memory savings.
- `True`, `"auto"` : automatically try to use the most efficient solution (liger for now)
- `False` (default) : no change
If the chosen backend is unavailable or unsupported, Transformers can gracefully fall back to the PyTorch implementation.
(Optional) Remind users that they won't be able to retrieve full logits when enabling `tiled_fused_logits_loss`.
### Motivation
Manifesting logits and cross entropy loss computation is often the largest memory bottleneck during training when working with long sequence, large batch size and large vocab size.
Recent paper on long sequence training: https://arxiv.org/abs/2506.13996 shows sequence tiling can massively reduce memory footprint, enabling longer sequence and larger batch sizes.
<img width="633" height="362" alt="Image" src="https://github.com/user-attachments/assets/a39ec6ee-61ee-43d3-bc58-2524d9b1dd73" />
> In Figure 3 we show two plots: (left) without Sequence Tiling the loss calculation we see peak memory usage is around 50 GiB compared to (right) after updating the loss calculation to use Sequence Tiling we see the peak memory usage drops to 36 GiB which results in a
> 28% memory reduction
<img width="507" height="239" alt="Image" src="https://github.com/user-attachments/assets/b0c9ba25-72f7-488b-94b9-c17b23576d56" />
Table 1 shows that "Tiled Logits & Loss" alone enables training with much longer sequence length.
I'm aware that Transformers already includes some `liger_*` training arguments, but those are backend-specific.
I believe it's a great time to have a standard native flag that all backend (liger, ds) could rely on, or make it a built-in feature which can avoid monkey patching modeling codes, leading to a huge maintenance overhead.
### More context about native support
To apply tiling sequence techniques for fusing logits and loss computation, we need to modify the code in `XXXForCausalLM.forward()` or `XXXForConditionalGeneration.forward()`. I'm not sure whether such replacement can be easily achieved by [HF/kernels](https://github.com/huggingface/kernels).
The modificiation itself is pretty straight-forward. The core logic is just replacing
```python
self.loss_fn(self.lm_head(hidden_states), labels, ...)
```
with
```python
tiled_fused_logits_loss(hidden_states, self.lm_head.weight, labels, ...)
```
plus some rearrangements of `logits_to_keep`, `attention_mask`, etc.
I think modularizing the change and making it a built-in feature can benefit users a lot, so they don't have to wait for the patch for unsupported models.
### Your contribution
I'm happy to help with making the PR, just want to know your opinions beforehand.
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Remove unnecessary list comprehension
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# What does this PR do?
Fix unnecessary list/tuple comprehension in code, other tiny fixes are also included.
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PR_kwDOCUB6oc6r2OzB
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Fix equality-vs-assignment bug in GptqHfQuantizer.update_device_map
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Fixes a typo in `GptqHfQuantizer.update_device_map` where an equality comparison (`==`) was used instead of an assignment (`=`). Due to this, `device_map` was never updated to CUDA when using `auto-gptq` (which does not support CPU), leaving the model on CPU contrary to the intent expressed in the comment.
- File: [src/transformers/quantizers/quantizer_gptq.py](https://github.com/huggingface/transformers/blob/e54defcfc265a1cc4f62edbf977747a0a443c812/src/transformers/quantizers/quantizer_gptq.py)
- Function: `GptqHfQuantizer.update_device_map`
Before:
```python
if not is_gptqmodel_available() and device_map in ("cpu", {"": torch.device("cpu")}):
device_map == {"": 0}
```
After:
```python
if not is_gptqmodel_available() and device_map in ("cpu", {"": torch.device("cpu")}):
device_map = {"": 0}
```
This change makes the code match the intended behavior: when `gptqmodel` is not available and the model would otherwise be placed on CPU, move to CUDA device map (`{"": 0}`).
## Who can review?
- quantization (bitsandbytes, autogpt, gptqmodel): @SunMarc @MekkCyber
Anyone in the community is also welcome to review.
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Fix README.md error when installing from source
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# What does this PR do?
When running the `uv pip install .[torch]` in some shell, e.g., zsh, an error occurs `zsh: no matches found: .[torch]`. This PR fixed it by adding `''`.
## 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?
## CC
@stevhliu
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Fix HfArgumentParser parsing for Union[dict, str] fields (e.g., --lr_scheduler_kwargs)
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…scheduler_kwargs)
# What does this PR do?
This PR fixes the bug where `Union[dict, str]` fields, such as `--lr_scheduler_kwargs` in `TrainingArguments`, could not be parsed correctly from the command line.
**Root Cause:**
The issue was introduced when the type of `lr_scheduler_kwargs` changed from `Optional[Union[dict[str, Any], str]]` to `Union[dict[str, Any], str]`. `HfArgumentParser` previously filtered out `str` from Union types when `None` was not present, which caused `argparse` to attempt parsing the argument as a `dict` directly, leading to errors when passing JSON strings via CLI.
**Fix:**
* Modified `src/transformers/hf_argparser.py` to detect `Union[dict, str]` types and keep the type as `str` for `argparse`.
* Conversion from JSON string → `dict` now happens later in `TrainingArguments.post_init_`.
* Maintains backward compatibility with `Optional[Union[dict, str]]` fields.
**Tests added:**
* `test_17_union_dict_str_parsing()`: verifies basic Union[dict, str] CLI parsing.
* `test_18_lr_scheduler_kwargs_parsing()`: regression test specifically for the `lr_scheduler_kwargs` issue.
Fixes the behavior reported in [#41296](https://github.com/huggingface/transformers/issues/41296).
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Fixes # (issue)
## 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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to it if that's the case.
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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).
- [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
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I_kwDOCUB6oc7PUtZB
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HTTP proxies via env variables no longer working in the main branch of transformers
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[] | 2025-10-02T17:22:47
| 2025-10-16T06:31:35
| 2025-10-16T02:18:26
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CONTRIBUTOR
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### System Info
The problem was encountered when trying to use the main branch of the transformers repo which depends on the package `huggingface-hub==1.0.0.rc2` with an HTTP proxy supplied by standard environment variables (`HTTP_PROXY`, `HTTPS_PROXY`, etc). This worked before this dependency upgrade was introduced.
This PR mentions that HTTP proxies are now supposed to be configured via environment variables, so it seems it should be supported: https://github.com/huggingface/transformers/pull/40889
This is also officially supported by `httpx`, which is the new http library introduced in the PR: https://www.python-httpx.org/environment_variables/#proxies
However, it seems like for some reason `httpx` will ignore the proxy if a custom `Transport` is used: https://github.com/encode/httpx/blob/c0b46ebf4cf724d813c8595602e6a5b59aef5177/httpx/_client.py#L685
`huggingface_hub` provides a custom `Transport` to `httpx`, so it seems like it is not possible to use a proxy using environment variables using the default transport configured here: https://github.com/huggingface/huggingface_hub/blob/f3334dd2da172093264dcc7a681d6eea14c7793d/src/huggingface_hub/utils/_http.py#L133C1-L141C6
It seems like this dependency update broke using HTTP proxies completely with `transformers` because of the removal of the proxy arguments and the environment variable setting no longer working.
A way to workaround for this can be done using the `set_client_factory` functions in `huggingface_hub`, but it would preferable if this could be done transparently by the library if the environment variable(s) for proxies are present which is how it worked before.
Workaround:
```python
import os
import httpx
from huggingface_hub import set_client_factory, set_async_client_factory, HfHubTransport, constants
from huggingface_hub.utils._http import default_client_factory, default_async_client_factory
def env_proxy_client_factory():
return httpx.Client(
transport=HfHubTransport(),
follow_redirects=True,
timeout=httpx.Timeout(constants.DEFAULT_REQUEST_TIMEOUT, write=60.0),
proxy=os.environ.get("HTTP_PROXY"),
)
def async_env_proxy_client_factory():
return httpx.AsyncClient(
transport=HfHubTransport(),
follow_redirects=True,
timeout=httpx.Timeout(constants.DEFAULT_REQUEST_TIMEOUT, write=60.0),
proxy=os.environ.get("HTTP_PROXY"),
)
set_client_factory(env_proxy_client_factory)
set_async_client_factory(async_env_proxy_client_factory)
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
```
### Who can help?
cc @Wauplin @ArthurZucker @ydshieh
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
This only happens in my internal dev server setup which uses proxy, and the proxy no longer works so there is network error
`python -c "from transformers import AutoModelForCausalLM; model = AutoModelForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')"`
or
`hf download TinyLlama/TinyLlama-1.1B-Chat-v1.0`
Got error:
```
httpcore.ConnectError: [Errno 101] Network is unreachable
```
### Expected behavior
we should be able to download models successfully, with internet proxy.
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PR_kwDOCUB6oc6rze84
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Standardize `PretrainedConfig` to `PreTrainedConfig`
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[] | 2025-10-02T16:51:41
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MEMBER
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# What does this PR do?
As per the title! This is very very long due.
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PR_kwDOCUB6oc6rzboy
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copied changes from soghomon-b:add-eval-step-limit-31561
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# What does this PR do?
<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
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<!-- Remove if not applicable -->
closes #31561
## Before submitting
- [no ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [yes ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [yes ] 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.
closes #31561
- [ no] 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).
There seems to be no documentation for TrainingArgument. I added a line in the training_args.py to explain the new argument.
- [yes ] Did you write any new necessary tests?
test_number_of_eval_samples_set and test_number_of_eval_samples_unset in tests/trainer/test_trainer.py
## 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**.
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Models:
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- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc
- chat templates: @Rocketknight1
Integrations:
- deepspeed: HF Trainer/Accelerate: @muellerzr
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
HF projects:
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- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
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- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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PR_kwDOCUB6oc6rzaae
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fix resample in asr pipeline
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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.
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<!-- Remove if not applicable -->
Fixes duplicated line in ASR pipeline that passed `in_sampling_rate` twice, causing `F.resample` to no-op.
<!-- 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
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- 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: @zach-huggingface @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
Integrations:
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- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
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PR_kwDOCUB6oc6ryxQT
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ENH: Add support for LoRA hotswapping
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# What does this PR do?
LoRA hotswapping has been available in PEFT since 0.15.0. There is already a diffusers
integration (https://github.com/huggingface/diffusers/pull/9453), but the transformers integration was still missing this feature. This PR remedies this. It sticks closely to the diffusers PR, both implementation-wise and API-wise.
Hotswapping allows to swap different LoRA adapters in-place instead of loading multiple adapters and switching between them. Not only can this be advantageous to safe memory and potentially for quicker loading, the biggest advantage is that if the model is compiled, we can hotswap without triggering recompilation (loading a separate adapter would require recompilation).
There are some caveats to using this feature, most notably that only LoRA is supported. This was fine for diffusers, as it only works with LoRA, but the transformers integration works with other PEFT methods too. However, LoRA should be by far the most common method, so this should be fine for now. This and other caveats have been documented.
Note that testing is not super deep, there could be more edge cases being tested. However, as this is mainly about calling PEFT functionality, which is extensively tested in PEFT, I focused the tests mostly on the integration, with only a couple of tests for the functionality itself (namely the tests that call `_check_model_hotswap`).
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?
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I_kwDOCUB6oc7PTHvk
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Parsing error with --lr-scheduler-kwargs: invalid dict value
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NONE
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### System Info
- `transformers` version: 4.57.0.dev0
- Platform: Linux-5.10.134-008.18.kangaroo.al8.x86_64-x86_64-with-glibc2.39
- Python version: 3.12.7+gc
- Huggingface_hub version: 1.0.0.rc2
- Safetensors version: 0.5.3
- Accelerate version: 1.7.0
- Accelerate config: not found
- DeepSpeed version: 0.16.9+ali
- PyTorch version (accelerator?): 2.6.0+ali.7.post2.ppu1.5.2.cu126 (CUDA)
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: PPU-ZW810E
### Who can help?
When using the `--lr-scheduler-kwargs` option with a dict argument, such as `'{"min_lr": 1e-06}'`, the parser reports the following error:
```text
test.py: error: argument --lr_scheduler_kwargs/--lr-scheduler-kwargs: invalid dict value: '{"min_lr": 1e-06}'
```
It appears that this error was introduced in https://github.com/huggingface/transformers/commit/a61fc6a0b985548c681df89c15f8053dafe322bf (title: "Fix typing of train_args"). It works as expected when reversing the data type of `lr_scheduler_kwargs` back to `Optional[Union[dict[str, Any], str]]`.
### Information
- [x] 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
You can simply reproduce it with a short script, such as:
```python
from transformers import TrainingArguments, HfArgumentParser
parser = HfArgumentParser((TrainingArguments,))
training_args, = parser.parse_args_into_dataclasses()
```
and then run it with `python test.py --lr-scheduler-kwargs '{"min_lr": 1e-06}'`
It will report the following error:
```
usage: test.py [-h] [--output_dir OUTPUT_DIR] [--overwrite_output_dir [OVERWRITE_OUTPUT_DIR]] [--do_train [DO_TRAIN]] [--do_eval [DO_EVAL]]
[--do_predict [DO_PREDICT]] [--eval_strategy {no,steps,epoch}] [--prediction_loss_only [PREDICTION_LOSS_ONLY]] [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE] [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE] [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--eval_accumulation_steps EVAL_ACCUMULATION_STEPS] [--eval_delay EVAL_DELAY] [--torch_empty_cache_steps TORCH_EMPTY_CACHE_STEPS] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2] [--adam_epsilon ADAM_EPSILON] [--max_grad_norm MAX_GRAD_NORM] [--num_train_epochs NUM_TRAIN_EPOCHS]
...
[--eval_use_gather_object [EVAL_USE_GATHER_OBJECT]] [--average_tokens_across_devices [AVERAGE_TOKENS_ACROSS_DEVICES]]
[--no_average_tokens_across_devices]
test.py: error: argument --lr_scheduler_kwargs/--lr-scheduler-kwargs: invalid dict value: '{"min_lr": 1e-06}'
```
### Expected behavior
It's expected that the parser parses the string `'{"min_lr": 1e-06}'` as a dictionary.
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AutoAWQ tests
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# What does this PR do?
Fixes AutoAwq tests
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PR_kwDOCUB6oc6ryVoD
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[v5] Remove deprecated `use_auth_token`
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# What does this PR do?
Remove `use_auth_token` from code and docs.
|
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PR_kwDOCUB6oc6ryLpq
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Tiny Cleanup - Removed duplicate class field definition's
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# What does this PR do?
Tiny cleanup. Used [Ruff-PIE794](https://docs.astral.sh/ruff/rules/duplicate-class-field-definition/). Will be happy to add the rule to [existing rules](https://github.com/huggingface/transformers/blob/e4930d6bde027ed21fd3dd91dbbfdc95e0221dd4/pyproject.toml#L36)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@ydshieh @Rocketknight1
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PR_kwDOCUB6oc6ryCM9
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Fix mxfp4 dequantization
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# What does this PR do?
https://github.com/huggingface/transformers/pull/41222 changed the signature of a caller in TP without updating the function calls, so `dequantization` is currently broken. This fixes it.
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Add-Deimv2
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# What does this PR do?
This PR introduces an initial DEIMv2 object detection integration that uses the AutoBackbone API to reuse DINOv3/ViT backbones without duplicating backbone code. It adds configuration, modeling, image processing, minimal tests, and documentation, paving the way for full STA/decoder and Dense O2O parity in follow-up
<!--
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 #41211
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] 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.
<!-- 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
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- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
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- trainer: @zach-huggingface @SunMarc
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- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
Integrations:
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- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @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_kwDOCUB6oc6rxywC
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Build doc in 2 jobs: `en` and `other languages`
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COLLABORATOR
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# What does this PR do?
See https://github.com/huggingface/transformers/pull/41286#issuecomment-3361383120
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PR_kwDOCUB6oc6rxsbS
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Add modular detector
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CONTRIBUTOR
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# What does this PR do?
Adds a new util to parse a new `modeling_*` file, whether or not it is from `transformers`, and lists the closest possible matches from existing models in the library using embeddings from Qwen3.
# Why?
Because most models should be able to be defined as `modular`s from each other. It's not exactly the case yet, see [this space](https://huggingface.co/spaces/Molbap/transformers-modular-refactor) to see the existing links between models and the ones yet to be included.
<img width="1888" height="838" alt="image" src="https://github.com/user-attachments/assets/3e4aa954-6a9c-4818-a6a3-fb3bcef7f81a" />
Also, there are more than 400 models. We cannot remember where is what. Hopefully this will accelerate development/porting of new models into a `modular` format and the modernization of older, non-modular models.
# How do I use it?
If you're adding a new model to `transformers` and have already some form of runnable `modeling` file, the only action is (with a sufficiently powerful machine) to run (here from https://github.com/huggingface/transformers/pull/39534 that adds beit3 model) from the `transformers` repository:
```bash
python utils/modular_model_detector.py --modeling-file /path/to/your/modeling_file.py
```
will get you the following (excerpt):
```bash
stuff.py::Beit3ImageTextMatchingOutput:
embedding:
blip_2::Blip2ImageTextMatchingModelOutput (0.9994)
chinese_clip::ChineseCLIPOutput (0.9818)
metaclip_2::MetaClip2Output (0.9817)
owlv2::Owlv2Output (0.9817)
aimv2::Aimv2Output (0.9817)
jaccard:
owlv2::Owlv2Output (0.9667)
metaclip_2::MetaClip2Output (0.9667)
altclip::AltCLIPOutput (0.9667)
owlvit::OwlViTOutput (0.9667)
blip::BlipOutput (0.9667)
intersection:
metaclip_2::MetaClip2Output
owlv2::Owlv2Output
stuff.py::Beit3MLP:
embedding:
efficientloftr::EfficientLoFTRMLP (0.9717)
seggpt::SegGptMlp (0.9650)
mgp_str::MgpstrMlp (0.9645)
vitpose_backbone::VitPoseBackboneMLP (0.9640)
granitemoehybrid::GraniteMoeHybridMLP (0.9634)
jaccard:
chinese_clip::ChineseCLIPTextSelfOutput (0.5294)
convbert::ConvBertSelfOutput (0.5294)
bert::BertSelfOutput (0.5294)
roformer::RoFormerSelfOutput (0.5294)
layoutlmv3::LayoutLMv3SelfOutput (0.5294)
intersection:
stuff.py::Beit3FeedForwardNetwork:
embedding:
prophetnet::ProphetNetFeedForward (0.9764)
dab_detr::DabDetrDecoderLayerFFN (0.9729)
kosmos2::Kosmos2TextFFN (0.9698)
kosmos2_5::Kosmos2_5TextFFN (0.9698)
parakeet::ParakeetEncoderFeedForward (0.9677)
jaccard:
groupvit::GroupViTMLP (0.4898)
convbert::ConvBertOutput (0.4600)
chinese_clip::ChineseCLIPTextOutput (0.4565)
bert::BertOutput (0.4565)
roformer::RoFormerOutput (0.4565)
intersection:
....
```
From experience: Jaccard-based indices are lower in value because they punish more harshly token differences but are still useful. Embeddings scores calibration is a bit effy - a score below 0.90 is definitely on the lower end. The intersection exists to guide the user a bit more.
In any case, the command above will take care of downloading the precomputed indices from [the corresponding hub dataset](https://huggingface.co/datasets/hf-internal-testing/transformers_code_embeddings) - we will need to setup a scheduled job to do it from time to time (like weekly?).
Should you want to recompute the index, simply run
```bash
python utils/modular_model_detector.py --build
```
Where you can change the model ID to try other models for instance. HF members can also push to the dataset, should you find better embeddings/matches.
This is a relatively minimal API, I didn't want to over-engineer it. Compared to the space above where I link models together, the main difference is that here I'm linking `functions`, ` methods` and `Classes` together rather than whole models.
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PR_kwDOCUB6oc6rw_nd
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Diabled FA on AMD docker
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COLLABORATOR
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In a recent PR #41069 we enabled `flash-attention` in the AMD docker by adding a step to build it from source. But it turns out this is taking too long to be done on our CI runners, so this PR disables it until we find a faster installation process.
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PR_kwDOCUB6oc6rw-Gr
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[v5] remove load_in_4bit and load_in_8bit
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# What does this PR do?
This PR removes `load_in_4bit` and `load_in_8bit` deprecated arguments for v5. This is something that I didn't want to do in a minor release as this might be breaking for a lot of users.
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PR_kwDOCUB6oc6rwxhk
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[docs] remove references to recently deleted classes in non-`en` docs (onnx, feature processors)
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# What does this PR do?
See title. These are causing our doc builder to crash.
_______________________
how to locally test the doc builder:
1. clone and install the doc builder
2. run `doc-builder build transformers docs/source/ja/ --language ja --clean` (replace `ja` by the target language of your choice)
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PR_kwDOCUB6oc6rwq0B
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feat: use `aws-highcpu-32-priv` for amd docker img build
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change the runner group to something more powerful, amd docker image build is very greedy in resources
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Use canonical get_size_with_aspect_ratio (with max_size) from transformers.image_transforms to fix #37939
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Removed max_size parameter from ConditionalDetrImageProcessor
to simplify image resizing logic and fix #37939.
Used canonical get_size_with_aspect_ratio (with max_size) from transformers.image_transforms
Tested with pytest suite and all tests are passed.
Happy to be guided further for improvement.
Thanks
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PR_kwDOCUB6oc6rwSJS
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[v5] Bump min version of bitsandbytes to 0.46.1
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# What does this PR do?
This PR cleans bitsandbytes integration as we bump the min version. With the multi-backend integrated in bitsanbytes for a few versions, I think we can safely bump to 0.46.1 as vllm did. I also removed a bunch as deprecated code
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PR_kwDOCUB6oc6rvsvV
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Fix - remove deprecated args checking in deepspeed intergrations
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# What does this PR do?
`adafactor` training args removed in this pr [#41017](https://github.com/huggingface/transformers/pull/41017).
This PR remove adafactor checking in the deepspeed intergrations.
<!--
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.
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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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<!-- Remove if not applicable -->
Fixes # (issue)
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
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[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
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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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Models:
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PR_kwDOCUB6oc6ru65s
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Adding kernels tests
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# What does this PR do?
PR to add kernel tests and ci
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PR_kwDOCUB6oc6rubef
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Use max/min
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# What does this PR do?
Some code can directly use `max/min` to avoid sorting.
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PR_kwDOCUB6oc6ruYJp
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Fix unnecessary single-item container checks
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[] | 2025-10-02T09:06:32
| 2025-10-02T12:39:46
| 2025-10-02T12:35:11
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# What does this PR do?
Simplify container tests.
|
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I_kwDOCUB6oc7POYR0
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Segmentation Fault with Qwen2 on Multi-GPU (device_map='auto')
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[] | 2025-10-02T08:48:12
| 2025-10-03T15:46:25
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### System Info
**Library versions**
- `transformers`: 4.40.1
- `accelerate`: 0.29.3
- `torch`: 2.1.2+cu121
- `bitsandbytes`: 0.41.3
- `CUDA`: 12.1
- `Python`: 3.10
**Hardware**
- GPUs: 2x NVIDIA RTX 3090 (24GB)
- Driver: NVIDIA 535.171.04
### Who can help?
**Description**
When attempting to load the `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B` model (a `qwen2` model type) using `device_map="auto"` on a multi-GPU system, a `Segmentation fault (core dumped)` occurs immediately after the model weights are loaded.
**The bug persists even when 4-bit quantization (`bitsandbytes`) and PEFT (LoRA) are completely disabled.** This strongly suggests the issue is not with those libraries, but with a core incompatibility between the Qwen2 model's remote code implementation and `accelerate`'s `device_map` functionality.
**Minimal Reproducible Code**
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# This is sufficient to trigger the crash
model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.float16 # Using float16 to even attempt loading
)
print("Model loading finished. The script crashes right after this.")
# The script never reaches this point
```
**Expected behavior**
The script should either load the model successfully or fail with a clear `CUDA out of memory` error if the model is too large.
**Actual behavior**
The script prints `Loading checkpoint shards... 100%` and then immediately exits with `Segmentation fault (core dumped)`.
This bug prevents any form of multi-GPU inference or training with Qwen2 models using `device_map` on this hardware configuration.
### 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
import os
import yaml
import argparse
import torch
import warnings
import importlib.metadata
# 忽略一些不影响结果的警告
warnings.filterwarnings("ignore")
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
TrainingArguments,
Trainer,
DataCollatorForSeq2Seq,
)
def main(config_path: str):
# --- 1. 加载配置 ---
with open(config_path, 'r') as f:
config = yaml.safe_load(f)
model_cfg = config['model_params']
data_cfg = config['data_params']
lora_cfg = config['lora_params']
training_cfg = config['training_params']
logging_cfg = config.get('logging_params', {})
optim_cfg = config.get('optim_params', {})
print("--- 配置加载完毕 ---")
# --- 2. 检查并升级关键库版本 ---
print("--- 正在检查关键库版本... ---")
try:
# 使用 importlib.metadata 进行更可靠的版本检查
bitsandbytes_version = importlib.metadata.version("bitsandbytes")
peft_version = importlib.metadata.version("peft")
accelerate_version = importlib.metadata.version("accelerate")
print(f"bitsandbytes version: {bitsandbytes_version}")
print(f"peft version: {peft_version}")
print(f"accelerate version: {accelerate_version}")
except importlib.metadata.PackageNotFoundError as e:
print(f"错误: 无法找到包的版本: {e}。请确保库已在当前环境中正确安装。")
return
except Exception as e:
print(f"检查版本时发生未知错误: {e}")
return
# --- 3. 加载Tokenizer ---
tokenizer = AutoTokenizer.from_pretrained(
model_cfg['model_dir'],
trust_remote_code=True,
local_files_only=True
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
print("--- Tokenizer加载完毕 ---")
# --- 4. [关键修改] 加载4-bit量化模型 ---
# 使用fp16作为计算类型，这在3090上更稳定
compute_dtype = torch.float16
# bnb_config = BitsAndBytesConfig(
# load_in_4bit=True,
# bnb_4bit_quant_type="nf4",
# bnb_4bit_compute_dtype=compute_dtype,
# bnb_4bit_use_double_quant=True,
# )
print("--- 正在加载模型 (已禁用量化，预期会OOM)... ---")
model = AutoModelForCausalLM.from_pretrained(
model_cfg['model_dir'],
# quantization_config=bnb_config,
device_map="auto", # 保持模型并行
trust_remote_code=True,
local_files_only=True,
attn_implementation="sdpa"
# attn_implementation="flash_attention_2"
# use_flash_attention_2=True, # [关键新增] 使用Flash Attention 2节省显存并加速
)
# # 修复一个已知的bug，当使用梯度检查点时需要设置
# model.config.pretraining_tp = 1
# model = prepare_model_for_kbit_training(model)
# peft_config = LoraConfig(**lora_cfg, task_type="CAUSAL_LM")
# model = get_peft_model(model, peft_config)
# model.print_trainable_parameters()
# print("--- 4-bit量化与LoRA模型准备完毕 ---")
# --- 5. 加载和预处理数据集 ---
def preprocess_function(examples):
# 假设数据格式是 {"messages": [...]}
prompts = [tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=False) for msgs in examples["messages"]]
inputs = tokenizer(prompts, max_length=data_cfg['max_seq_length'], truncation=True, padding=False)
# 标签也需要被截断
labels = tokenizer(prompts, max_length=data_cfg['max_seq_length'], truncation=True, padding=False)
inputs['labels'] = labels['input_ids']
return inputs
dataset = load_dataset("json", data_files=data_cfg['train_file'], split="train")
processed_dataset = dataset.map(preprocess_function, batched=True, remove_columns=dataset.column_names)
print("--- 数据集处理完毕 ---")
# --- 6. [关键修改] 配置训练参数 ---
trainer_args = TrainingArguments(
output_dir=model_cfg['output_dir'],
per_device_train_batch_size=training_cfg['per_device_train_batch_size'],
gradient_accumulation_steps=training_cfg['gradient_accumulation_steps'],
# 使用8位分页优化器
optim=optim_cfg.get('optim', 'paged_adamw_8bit'),
learning_rate=training_cfg['learning_rate'],
weight_decay=training_cfg['weight_decay'],
num_train_epochs=training_cfg['num_train_epochs'],
lr_scheduler_type=training_cfg['lr_scheduler_type'],
warmup_ratio=training_cfg['warmup_ratio'],
max_grad_norm=training_cfg['max_grad_norm'],
# 混合精度与梯度检查点
fp16=True, # 明确使用fp16
bf16=False,
gradient_checkpointing=training_cfg['use_gradient_checkpointing'],
# 日志与保存
logging_steps=logging_cfg.get('logging_steps', 10),
save_strategy=logging_cfg.get('save_strategy', 'epoch'),
save_steps=logging_cfg.get('save_steps', 500),
save_total_limit=2,
# 禁用tf32，有时它会与4-bit量化冲突
tf32=False,
report_to="wandb" if logging_cfg.get('wandb_project') else "none",
run_name=f"qwen-qlora-{os.path.basename(model_cfg['output_dir'])}"
)
# --- 7. 初始化并开始训练 ---
trainer = Trainer(
model=model,
args=trainer_args,
train_dataset=processed_dataset,
tokenizer=tokenizer,
data_collator=DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True),
)
# 修复 `use_cache=True` 与梯度检查点的冲突
if training_cfg['use_gradient_checkpointing']:
model.config.use_cache = False
print("--- 开始训练 ---")
trainer.train()
# --- 8. 保存最终模型 ---
print("--- 训练完成，正在保存最终模型... ---")
trainer.save_model(os.path.join(model_cfg['output_dir'], "final_checkpoint"))
print("--- 最终模型已保存 ---")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, required=True, help="Path to the config.yaml file")
args = parser.parse_args()
main(args.config)
### Expected behavior
---
name: "Bug Report: Segmentation Fault with Qwen2 on Multi-GPU (device_map='auto')"
---
**Library versions**
- `transformers`: 4.40.1
- `accelerate`: 0.29.3
- `torch`: 2.1.2+cu121
- `bitsandbytes`: 0.41.3
- `CUDA`: 12.1
- `Python`: 3.10
**Hardware**
- GPUs: 2x NVIDIA RTX 3090 (24GB)
- Driver: NVIDIA 535.171.04
**Description**
When attempting to load the `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B` model (a `qwen2` model type) using `device_map="auto"` on a multi-GPU system, a `Segmentation fault (core dumped)` occurs immediately after the model weights are loaded.
**The bug persists even when 4-bit quantization (`bitsandbytes`) and PEFT (LoRA) are completely disabled.** This strongly suggests the issue is not with those libraries, but with a core incompatibility between the Qwen2 model's remote code implementation and `accelerate`'s `device_map` functionality.
**Minimal Reproducible Code**
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# This is sufficient to trigger the crash
model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.float16 # Using float16 to even attempt loading
)
print("Model loading finished. The script crashes right after this.")
# The script never reaches this point
```
**Expected behavior**
The script should either load the model successfully or fail with a clear `CUDA out of memory` error if the model is too large.
**Actual behavior**
The script prints `Loading checkpoint shards... 100%` and then immediately exits with `Segmentation fault (core dumped)`.
This bug prevents any form of multi-GPU inference or training with Qwen2 models using `device_map` on this hardware configuration.
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PR_kwDOCUB6oc6rtNir
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Add processor and intergration test for qwen3vl
|
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# What does this PR do?
- support aux loss in qwen3vlmoe
- update qwen3vl processor test since the Qwen3-VL-235B-A22B is released!
- fix multi-video inference
- add intergration test for 30a3 (skip for now)
# Who can review?
- text models: @ArthurZucker @Cyrilvallez
- multimodal models: @zucchini-nlp
|
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| 41,276
|
Add dinov3 autobackbone
|
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| 2025-10-28T05:42:54
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This PR implements DINOv3 support for AutoBackbone.
What's Implemented:
DINOv3ViTConfig: Complete configuration class with all DINOv3-specific parameters
DINOv3ViTModel: Full model implementation with RoPE embeddings, register tokens, and SwiGLU MLP
DINOv3ViTBackbone: AutoBackbone integration with multi-scale feature extraction
AutoBackbone Integration: Added to MODEL_FOR_BACKBONE_MAPPING_NAMES in modeling_auto.py
Facebook API Compatibility: get_intermediate_layers method matching Facebook Research implementation
Multi-scale Features: Support for object detection and segmentation downstream tasks
Fixes #40323
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PR_kwDOCUB6oc6rsEnf
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fix asr pipeline ut failures
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4 ASR cases failed on transformers
> pytest -rA tests/pipelines/test_pipelines_automatic_speech_recognition.py::AutomaticSpeechRecognitionPipelineTests::test_pipeline_assisted_generation
> pytest -rA tests/test_pipeline_mixin.py::AutomaticSpeechRecognitionPipelineTests::test_pipeline_assisted_generation
> pytest -rA tests/pipelines/test_pipelines_automatic_speech_recognition.py::AutomaticSpeechRecognitionPipelineTests::test_torch_whisper_batched
> pytest -rA tests/test_pipeline_mixin.py::AutomaticSpeechRecognitionPipelineTests::test_torch_whisper_batched
@SunMarc @ydshieh , pls help review, thx very much
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I_kwDOCUB6oc7PLCLF
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does DeepSeek-V3.2-Exp have update？
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cannot find DeepSeek-V3.2-Exp
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PR_kwDOCUB6oc6rql8O
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fix(quantization): Skip weight initialization for quantized models
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## Description
This Pull Request fixes a `RuntimeError` that occurs when loading `llmcompressor` W8A8 quantized models (e.g., `RedHatAI/Qwen2.5-VL-7B-Instruct-quantized.w8a8`) due to an attempt to initialize `int8` weights using `torch.nn.init.normal_()`, which only supports floating-point dtypes.
The issue was identified in `modeling_utils.py` within the `_initialize_missing_keys` method. When `is_quantized` is `True`, the `else` branch was still calling `self.initialize_weights()`, leading to the `RuntimeError`.
## Proposed Change
Added a conditional check `if not is_quantized:` before the call to `self.initialize_weights()` in the `else` branch of the `_initialize_missing_keys` method. This ensures that weight initialization is skipped for quantized models, as their weights are either already defined or will be loaded from a pretrained state dictionary, making the initialization redundant and problematic.
## Related Issue
Closes #39366
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feat: Add HRM Model
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# What does this PR do?
Adds the new Hierarchical Reasoning Model.
Fixes #41271
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [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.
@ArthurZucker @Cyrilvallez hoping you can take a look. The model is currently implemented as a text model though further research hints it may be helpful for understanding images as well.
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Add Hierarchical Reasoning Model
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### Model description
The Hierarchical Reasoning Model (HRM) is showing promise in reasoning tasks. I have been working on cleaning up the [original codebase](https://github.com/sapientinc/HRM) and decided I would also integrate it into the Transformers library so a larger audience could benefit from this project.
Below is a snippet from the team's paper which best describes the model.
```abstract
... Inspired by the hierarchical and multi-timescale processing in the human brain, we propose the Hierarchical Reasoning Model (HRM), a novel recurrent architecture that attains significant computational depth while maintaining both training stability and efficiency.
HRM executes sequential reasoning tasks in a single forward pass without explicit supervision of the intermediate process, through two interdependent recurrent modules: a high-level module responsible for slow, abstract planning, and a low-level module handling rapid, detailed computations.
With only 27 million parameters, HRM achieves exceptional performance on complex reasoning tasks using only 1000 training samples. The model operates without pre-training or CoT data, yet achieves nearly perfect performance on challenging tasks including complex Sudoku puzzles and optimal path finding in large mazes.
Furthermore, HRM outperforms much larger models with significantly longer context windows on the Abstraction and Reasoning Corpus (ARC), a key benchmark for measuring artificial general intelligence capabilities. These results underscore HRM’s potential as a transformative advancement toward universal computation and general-purpose reasoning systems.
```
I have a PR ready to close out this issue.
### Open source status
- [x] The model implementation is available
- [x] The model weights are available
### Provide useful links for the implementation
# Authors
- @imoneoi
- @btoo
- @raincchio
- @liamnorm
- @WC-william
- @zbloss - not an original author but I've implemented a refactor to the original codebase.
# Weights
- [sapientinc/HRM-checkpoint-ARC-2](https://huggingface.co/sapientinc/HRM-checkpoint-ARC-2)
- [sapientinc/HRM-checkpoint-sudoku-extreme](https://huggingface.co/sapientinc/HRM-checkpoint-sudoku-extreme)
- [sapientinc/HRM-checkpoint-maze-30x30-hard](https://huggingface.co/sapientinc/HRM-checkpoint-maze-30x30-hard)
# Original Implementation
- Code: https://github.com/sapientinc/HRM
- Paper: https://arxiv.org/abs/2506.21734
# Relevant Incoming Changes
- I've got a PR currently open in their repo that improves readability and organizes the code. This may be helpful to read through if it has not been merged into main yet: https://github.com/sapientinc/HRM/pull/89
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# What does this PR do?
<!--
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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Fixes #40984
adds logits_to_keep argument to the `BioGptForSequenceClassification`
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR. @Rocketknight1
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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Please tag fewer than 3 people.
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- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
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- CIs: @ydshieh
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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_kwDOCUB6oc6rnOou
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Italian translation for README.md
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This PR simply adds an Italian translation of `README.md` to the `i18n` folder and links it in the other available translations, including the `README.md` file itself.
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PR_kwDOCUB6oc6rm0Zu
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🚨 Bump to Python 3.10 and rework how we check 3rd-party libraries existence
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# What does this PR do?
Python 3.9 is officially EOL starting this month, so let's bump it! Took the opportunity of the new features available in 3.10 to simplify quite a bit our library checks
Note that it is breaking BC a bit as I removed some old and unused library checks that were public members
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PR_kwDOCUB6oc6rl6VC
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Remove SageMakerTrainer
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# What does this PR do?
It is replaced by Trainer.
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PR_kwDOCUB6oc6rl4Ld
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docs: update bitsandbytes platform support
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# What does this PR do?
Updates the quantization docs to reflect that bitsandbytes now provides official support for CPU, Intel XPU, and Intel Gaudi. See the [v0.48.0 release notes](https://github.com/bitsandbytes-foundation/bitsandbytes/releases/tag/0.48.0) for more information.
Additionally I keep the yellow icon in the quantization overview section for AMD ROCm and add it for Apple Silicon, as these platforms have preliminary support when built from source.
## 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).
- [x] Did you write any new necessary tests?
## Who can review?
@SunMarc @MekkCyber @IlyasMoutawwakil
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PR_kwDOCUB6oc6rlzjF
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:rotating_light: [`Attention Masks`] Bidirectional masks for encoder and encoder-decoder models
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Introduces attention masks for encoder and encoder-decoder models, aka full bidirectional masks, fully compatible with our interface system. Some exceptions, like autoformer, have been ignored for now + there are probably a lot more models that have some weird update patterns but these are imo the most important (related) models here, i.e. Bert and Bart models.
Breaking changes:
- Executorch now also needs a mask workaround due to vmapping in our general interface
- 3D masks are no longer supported (looking at you grounding dino models :eye:) - they just needed to expand to the head dim as well...
- Torch fx will no longer work for these models, ig vmapping again (?)
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PR_kwDOCUB6oc6rlzIU
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Use regex defailed flags
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# What does this PR do?
This PR uses full names of regex flags for better code readability.
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PR_kwDOCUB6oc6rlwTq
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Remove some previous team members from allow list of triggering Github Actions
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# What does this PR do?
Better security practice.
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PR_kwDOCUB6oc6rlpUp
| 41,262
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add peft team members to issue/pr template
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| 2025-10-01T17:27:00
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COLLABORATOR
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# What does this PR do?
PEFT go brrrrr
|
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PR_kwDOCUB6oc6rlpBu
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Remove `test_initialization`
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# What does this PR do?
As per the title. The test is very old and not useful anymore.
We have the much more recent and accurate `test_can_init_all_missing_weights` I added some time ago to actually test that all weights are correctly taken into account in `_init_weights`.
What distribution is then used on those weights is not really important to be tested, and way too hard for a general test as it can be anything. This can be seen by the fact that the test is basically skipped/overwritten for more than half the models in the lib, which is a *strong* sign that the test is quite badly designed to start with 🙃
cc @ArthurZucker @gante
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| 2025-10-02T11:45:09
| 2025-10-02T11:45:09
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NONE
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# What does this PR do?
<!--
Congratulations! You've made it this far! You're not quite done yet though.
Fixes #41244
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 #41244
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] 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.
<!-- 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: @zach-huggingface @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
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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| 3,474,076,467
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PR_kwDOCUB6oc6rlaH1
| 41,259
|
Unify is_torchvision_v2_available with is_torchvision_available
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| 2025-10-02T12:05:59
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# What does this PR do?
This PR fixes the remaining files, a follow-up of #41227 .
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PR_kwDOCUB6oc6rlV7M
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Fix pylint generator warnings
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# What does this PR do?
As the title says..
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PR_kwDOCUB6oc6rlQlk
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Fix typos and clarify docstring in DocumentQuestionAnswering pipeline
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Improved clarity and fixed minor typos in the docstring of the DocumentQuestionAnswering pipeline.
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PR_kwDOCUB6oc6rlI8p
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path validation for security reason
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[] | 2025-10-01T15:04:06
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# What does this PR do?
See the comment in file change tab
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PR_kwDOCUB6oc6rlF3B
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fix async client for transformers chat
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# What does this PR do?
This PR fixes the async client for transformers chat. There were some error due to the latest version of `huggingface-hub` since we migrated to httpx and i'm not sure why the previous code actually worked ...
### Traceback
```
Traceback (most recent call last): File "/admin/home/marc/miniconda3/envs/test/bin/transformers", line 8, in <module> sys.exit(main()) File "/admin/home/marc/transformers/src/transformers/commands/transformers_cli.py", line 48, in main service.run() File "/admin/home/marc/transformers/src/transformers/commands/chat.py", line 665, in run asyncio.run(self._inner_run()) File "/admin/home/marc/miniconda3/envs/test/lib/python3.10/asyncio/runners.py", line 44, in run return loop.run_until_complete(main) File "/admin/home/marc/miniconda3/envs/test/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete return future.result() File "/admin/home/marc/transformers/src/transformers/commands/chat.py", line 747, in _inner_run model_output = await interface.stream_output(stream) File "/admin/home/marc/transformers/src/transformers/commands/chat.py", line 134, in stream_output async for token in await stream: File "/admin/home/marc/miniconda3/envs/test/lib/python3.10/site-packages/huggingface_hub/inference/_generated/_async_client.py", line 956, in chat_completion data = await self._inner_post(request_parameters, stream=stream) File "/admin/home/marc/miniconda3/envs/test/lib/python3.10/site-packages/huggingface_hub/inference/_generated/_async_client.py", line 282, in _inner_post response = await self.exit_stack.enter_async_context( File "/admin/home/marc/miniconda3/envs/test/lib/python3.10/contextlib.py", line 619, in enter_async_context result = await _cm_type.__aenter__(cm) File "/admin/home/marc/miniconda3/envs/test/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/admin/home/marc/miniconda3/envs/test/lib/python3.10/site-packages/httpx/_client.py", line 1583, in stream response = await self.send( File "/admin/home/marc/miniconda3/envs/test/lib/python3.10/site-packages/httpx/_client.py", line 1616, in send raise RuntimeError("Cannot send a request, as the client has been closed.") RuntimeError: Cannot send a request, as the client has been closed.
```
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PR_kwDOCUB6oc6rk_-K
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docs: Add Hacktoberfest banner, Contributing and License sections to README
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Closes #41246 and #41244
## What does this PR do?
Adds missing sections to the README:
- Contributing Guidelines section with link to CONTRIBUTING.md and Code of Conduct
- License section with link to LICENSE file
Adds:
- Hacktoberfest 2025 banner to the top.
## Why?
Makes the project more professional and contributor-friendly by following open-source best practices. These sections help new contributors understand how to get involved and clarify the project's licensing terms. It also allows Hacktoberfest participants to more easily understand that this repository is a part of Hacktoberfest.
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PR_kwDOCUB6oc6rjsXt
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transformers serve quantization docs + some api fixes for bitsandbytes
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# What does this PR do?
This PR adds some docs for transformers serve x quantization and fixes the quantization api for bitsandbytes.
Following PR will add more support for other methods and I will add a section in each quantization method to show what is the preferred setup (compile ? dtype ?) when using serve !
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PR_kwDOCUB6oc6rjK3U
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FIX: Bug in PEFT integration delete_adapter method
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# What does this PR do?
The main content of this PR is to fix a bug in the `delete_adapter` method of the `PeftAdapterMixin`. Previously, it did not take into account auxiliary modules from PEFT, e.g. those added by `modules_to_save`. This PR fixes this oversight.
Note that the PR uses a new functionality from PEFT that exposes integration functions like delete_adapter. Those will be contained in the next PEFT release, 0.18.0 (yet unreleased). Therefore, the bug is only fixed when users have a PEFT version fulfilling this requirement. I ensured that with old PEFT versions, the integration still works the same as previously. The newly added test for this is skipped if the PEFT version is too low.
(Note: I tested locally with that the test will pass with PEFT 0.18.0)
While working on this, I also cleaned up the following:
- The `active_adapter` property has been deprecated for more than 2 years (#26407). It is safe to remove it now.
- There were numerous small errors or outdated pieces of information in the docstrings, which have been addressed.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?
|
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Add deepseek 3.2 exp
|
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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.
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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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<!-- 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: @zach-huggingface @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
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_kwDOCUB6oc6rifFP
| 41,250
|
Validate config attributes
|
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MEMBER
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# What does this PR do?
As per title. Continues from https://github.com/huggingface/transformers/pull/40793 and supersedes https://github.com/huggingface/transformers/pull/36534/files
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PR_kwDOCUB6oc6rh9am
| 41,249
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[`Flex Attn`] Fix lse x attention sinks logic
|
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| 2025-10-02T15:49:45
| 2025-10-02T15:49:40
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We always return lse except for CPU #40352 (it simply isn't supported in torch yet)
Let's rather raise a proper error for this than encountering a verbose torch exception
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PR_kwDOCUB6oc6rh0CQ
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[`v5`] Sync Bert and Bart eager attention
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Syncs the eager attentions if those model groups to have less intertwined versions of essentially the same; sync goes from bert as being the primary source
NOTE: Since it has a hard dependency on #41170, it should only come with v5 as well - do we still use the labels or just go for it since we have v4.57?
cc @zucchini-nlp
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I_kwDOCUB6oc7PAfFR
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Custom loss function for Qwen 2.5 VL 7b training
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[] | 2025-10-01T10:45:47
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/raid/akhila/conda_envs/mswift/lib/python3.10/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
here there is self.loss_function under Qwen2_5_VLForConditionalGeneration class. I want to add a custom loss function which should be accessed from config. I am doing SFT training using mswift
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I_kwDOCUB6oc7O_R8y
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|
Add Contribution Guidelines and License Section in README
|
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[] | 2025-10-01T09:09:19
| 2025-10-05T18:48:32
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NONE
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Description
Currently, the README.md does not have a Contribution section and the License section is either missing/not clearly visible.
Adding these two sections will make the project more professional, contributor-friendly, and compliant with open-source standards.
🎯 Acceptance Criteria
Contribution section added in README.md
License section added in README.md
Link to CONTRIBUTING.md and LICENSE file included
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I_kwDOCUB6oc7O_Q6d
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Add Pull Request (PR) Template
|
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[] | 2025-10-01T09:08:09
| 2025-10-14T04:46:14
| null |
NONE
| null | null | null | null |
Currently, the repository does not have a Pull Request (PR) template. This makes it harder for contributors to provide consistent information when opening PRs.
Proposed Solution:
Add a PR template file at .github/pull_request_template.md.
The template should include:
Description of changes
Related issues (Fixes #issue)
Screenshots (if applicable)
Type of change (Bug fix, Feature, Enhancement, Documentation)
Checklist (self-review, code style, documentation update)
Benefits:
Ensures all PRs have consistent structure.
Helps maintainers review PRs faster.
Improves contributor experience and professionalism of the project.
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I_kwDOCUB6oc7O_QMT
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Add hacktoberfest-2025 Banner to README
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| 2025-10-04T06:31:58
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NONE
| null | null | null | null |
The repository currently does not display any hacktoberfest-2025 banner in the README. Adding the banner will help highlight the project’s participation in hacktoberfest-2025 and increase visibility.
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PR_kwDOCUB6oc6rgWiW
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Resolve remote custom module path warnings
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[] | 2025-10-01T08:44:09
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CONTRIBUTOR
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# What does this PR do?
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<!-- Remove if not applicable -->
Fixes https://github.com/huggingface/transformers/pull/40809#issuecomment-3354623703
## 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.
- [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
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Models:
- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
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- graph models: @clefourrier
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- model loading (from pretrained, etc): @CyrilVallez
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Integrations:
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cc @Rocketknight1 @st81
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