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2 changes: 1 addition & 1 deletion docs/source/en/community_integrations/sglang.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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4 changes: 2 additions & 2 deletions docs/source/en/community_integrations/tensorrt-llm.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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# TensorRT-LLM

[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) is optimizes LLM inference on NVIDIA GPUs. It compiles models into a TensorRT engine with in-flight batching, paged KV caching, and tensor parallelism. [AutoDeploy](https://nvidia.github.io/TensorRT-LLM/torch/auto_deploy/auto-deploy.html) accepts Transformers models without requiring any changes. It automatically converts the model to an optimized runtime.
[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) optimizes LLM inference on NVIDIA GPUs. It compiles models into a TensorRT engine with in-flight batching, paged KV caching, and tensor parallelism. [AutoDeploy](https://nvidia.github.io/TensorRT-LLM/torch/auto_deploy/auto-deploy.html) accepts Transformers models without requiring any changes. It automatically converts the model to an optimized runtime.

Pass a model id from the Hub to [build_and_run_ad.py](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/auto_deploy/build_and_run_ad.py) to run a Transformers model.

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2 changes: 1 addition & 1 deletion docs/source/en/community_integrations/torchtitan.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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2 changes: 1 addition & 1 deletion docs/source/en/community_integrations/trl.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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2 changes: 1 addition & 1 deletion docs/source/en/community_integrations/unsloth.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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2 changes: 1 addition & 1 deletion docs/source/en/community_integrations/vllm.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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2 changes: 1 addition & 1 deletion docs/source/en/internal/rope_utils.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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2 changes: 1 addition & 1 deletion docs/source/en/internal/time_series_utils.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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2 changes: 1 addition & 1 deletion docs/source/en/internal/tokenization_utils.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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2 changes: 1 addition & 1 deletion docs/source/en/internal/trainer_utils.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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2 changes: 1 addition & 1 deletion docs/source/en/kernel_doc/writing_kernels.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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2 changes: 1 addition & 1 deletion docs/source/en/main_classes/quantization.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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2 changes: 1 addition & 1 deletion docs/source/en/main_classes/text_generation.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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6 changes: 3 additions & 3 deletions docs/source/en/main_classes/tokenizer.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the
Rust library [🤗 Tokenizers](https://github.com/huggingface/tokenizers). The "Fast" implementations allows:
Rust library [🤗 Tokenizers](https://github.com/huggingface/tokenizers). The "Fast" implementations allow:

1. a significant speed-up in particular when doing batched tokenization and
2. additional methods to map between the original string (character and words) and the token space (e.g. getting the
Expand Down Expand Up @@ -83,7 +83,7 @@ print(vision_tokenizer.image_token, vision_tokenizer.image_token_id)

## PreTrainedTokenizerFast

The [`PreTrainedTokenizerFast`] depend on the [tokenizers](https://huggingface.co/docs/tokenizers) library. The tokenizers obtained from the 🤗 tokenizers library can be
The [`PreTrainedTokenizerFast`] depends on the [tokenizers](https://huggingface.co/docs/tokenizers) library. The tokenizers obtained from the 🤗 tokenizers library can be
loaded very simply into 🤗 transformers. Take a look at the [Using tokenizers from 🤗 tokenizers](../fast_tokenizers) page to understand how this is done.

[[autodoc]] PreTrainedTokenizerFast
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2 changes: 1 addition & 1 deletion docs/source/en/main_classes/trainer.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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4 changes: 2 additions & 2 deletions docs/source/en/main_classes/video_processor.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# Video Processor

A **Video Processor** is a utility responsible for preparing input features for video models, as well as handling the post-processing of their outputs. It provides transformations such as resizing, normalization, and conversion into PyTorch. Along ith transformations the `VideoProcessor` class handles video decoding from local paths or URLs (requires [`torchcodec`](https://pypi.org/project/torchcodec/)) and frame sampling according to model-specific strategies.
A **Video Processor** is a utility responsible for preparing input features for video models, as well as handling the post-processing of their outputs. It provides transformations such as resizing, normalization, and conversion into PyTorch. Along with transformations the `VideoProcessor` class handles video decoding from local paths or URLs (requires [`torchcodec`](https://pypi.org/project/torchcodec/)) and frame sampling according to model-specific strategies.

The video processor extends the functionality of image processors by allowing Vision Large Language Models (VLMs) to handle videos with a distinct set of arguments compared to images. It serves as the bridge between raw video data and the model, ensuring that input features are optimized for the VLM.

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10 changes: 5 additions & 5 deletions docs/source/en/model_doc/qwen2_5_omni.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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Expand All @@ -34,7 +34,7 @@ The abstract from the technical report is the following:

- Use [`Qwen2_5OmniForConditionalGeneration`] to generate audio and text output. To generate only one output type, use [`Qwen2_5OmniThinkerForConditionalGeneration`] for text-only and [`Qwen2_5OmniTalkersForConditionalGeneration`] for audio-only outputs.
- Audio generation with [`Qwen2_5OmniForConditionalGeneration`] supports only single batch size at the moment.
- In case out out-of-memory errors hwen working with video input, decrease `processor.max_pixels`. By default the maximum is set to a very arge value and high resolution visuals will not be resized, unless resolution exceeds `processor.max_pixels`.
- In case out-of-memory errors when working with video input, decrease `processor.max_pixels`. By default the maximum is set to a very large value and high resolution visuals will not be resized, unless resolution exceeds `processor.max_pixels`.
- The processor has its own [`~ProcessorMixin.apply_chat_template`] method to convert chat messages to model inputs.

## Usage example
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"role": "user",
"content": [
{"type": "video", "video": "/path/to/video.mp4"},
{"type": "text", "text": "What cant you hear and see in this video?"},
{"type": "text", "text": "What can't you hear and see in this video?"},
],
},
]
Expand Down Expand Up @@ -124,7 +124,7 @@ conversations = [
"role": "user",
"content": [
{"type": "video", "video": "/path/to/video.mp4"},
{"type": "text", "text": "What cant you hear and see in this video?"},
{"type": "text", "text": "What can't you hear and see in this video?"},
],
},
]
Expand Down Expand Up @@ -230,7 +230,7 @@ conversation4 = [
{"type": "image", "path": "/path/to/image.jpg"},
{"type": "video", "path": "/path/to/video.mp4"},
{"type": "audio", "path": "/path/to/audio.wav"},
{"type": "text", "text": "What are the elements can you see and hear in these medias?"},
{"type": "text", "text": "What are the elements can you see and hear in these media?"},
],
}
]
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4 changes: 2 additions & 2 deletions docs/source/en/model_doc/qwen2_5_vl.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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```

- Use Qwen2.5-VL for a mixed batch of inputs (images, videos, text). Add labels when handling multiple images or videos for better reference
as show below.
as shown below.

```python
import torch
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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4 changes: 2 additions & 2 deletions docs/source/en/model_doc/qwen2_moe.md
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

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[Qwen2MoE](https://huggingface.co/papers/2407.10671) is a Mixture-of-Experts (MoE) variant of [Qwen2](./qwen2), available as a base model and an aligned chat model. It uses SwiGLU activation, group query attention and a mixture of sliding window attention and full attention. The tokenizer can also be adapted to multiple languages and codes.

The MoE architecture uses upcyled models from the dense language models. For example, Qwen1.5-MoE-A2.7B is upcycled from Qwen-1.8B. It has 14.3B parameters but only 2.7B parameters are activated during runtime.
The MoE architecture uses upcycled models from the dense language models. For example, Qwen1.5-MoE-A2.7B is upcycled from Qwen-1.8B. It has 14.3B parameters but only 2.7B parameters are activated during runtime.

You can find all the original checkpoints in the [Qwen1.5](https://huggingface.co/collections/Qwen/qwen15-65c0a2f577b1ecb76d786524) collection.

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