* feat(models): add transformers 5.0 compatibility

Conditionally import models incompatible with transformers >= 5.0:
- dream_dllm, qwen3_dllm, llada_dllm require transformers < 5.0
- llava_onevision1_5 requires transformers < 5.0
- Dynamically update __all__ based on transformers version
- Prevents ImportError when using transformers 5.0+

* fix(train): add group_by_length for backward compatibility

Add group_by_length parameter to TrainingArguments to maintain
compatibility with existing training configurations.

* feat(deps): allow transformers >= 4.57.1

Update transformers dependency from exact version to minimum version
to support transformers 5.0+ while maintaining backward compatibility.

* style: auto-fix lint (black + isort)

* refactor(processor): replace additional_special_tokens with all_special_tokens

Use all_special_tokens for transformers >= 5.0 compatibility while
maintaining backward compatibility with transformers < 5.0.

Changes:
- Add special_tokens property to all processor classes
- Use all_special_tokens if available (transformers >= 5.0)
- Fall back to additional_special_tokens (transformers < 5.0)
- Add <|im_start|> and <||im_end|> tokens to special_tokens list
- Cache special_tokens as instance attribute for performance

Affected processors:
- AeroDataProcessor (base class)
- BaseQwen2_5_DataProcessor (inherits from AeroDataProcessor)
- Qwen2VLDataProcessor
- Qwen2DataProcessor
- LLaVADataProcessor
- LLaVAVideoDataProcessor (inherits from LLaVADataProcessor)
- NanovlmDataProcessor
- Qwen3_VLDataProcessor (inherits from BaseQwen2_5_DataProcessor)

* style: auto-fix lint (black + isort)

* refactor(processor): unify apply_chat_template usage

Use processor.apply_chat_template with tokenize=True consistently
across all processors instead of mixing with processor.tokenizer calls.

Changes:
- aero_processor: use processor.apply_chat_template(tokenize=True)[0]
- base_qwen2_5_processor: use processor.apply_chat_template(tokenize=True)[0]
- qwen2_vl_processor: use processor.apply_chat_template(tokenize=True)
- qwen3_vl_processor: use processor.apply_chat_template(tokenize=True)[0]

This ensures all processors return token IDs directly during data
preparation, improving consistency and reducing confusion.

* feat(models): add common_ops for transformer-agnostic rope index

Extract rope index calculation functions into common_ops/rope.py to
ensure consistent behavior across transformers versions.

Changes:
- Add common_ops/rope.py with qwen2_5_vl_rope_index and qwen3_vl_get_rope_index
- Update qwen2_5_vl_ops.py to use qwen2_5_vl_rope_index
- Update qwen3_vl_ops.py to use qwen3_vl_get_rope_index
- Update qwen3_vl_moe_ops.py to use qwen3_vl_get_rope_index

This ensures rope index calculations remain stable even when transformers
internal implementations change.

* fix(utils): add B200/B300 GPU FLOPS support

Add NVIDIA B200/B300 GPU FLOPS (2.25e15) to get_device_flops()
to fix MFU calculation returning 0 on B200 GPUs.

Previously, unknown GPU types returned inf FLOPS, causing MFU
to always be 0.

* Lint

* fix(models): qwen2_5_vl transformers 5.0 compatibility

- Fix vision_model variable reference in liger kernel patch
- Support nested text_config in lce_forward
- Handle rope_scaling/rope_parameters for transformers 5.0+
- Add qwen2_5_vl to FlopsCounter model type mapping

* refactor(processor): use DataUtilities.apply_chat_template for transformers 5.0 compatibility

- Add apply_chat_template utility method to DataUtilities
- Handles dict-like return values (BatchEncoding) with use_key param
- Handles nested list wrapping from some processors
- Update all processors to use unified method

* feat(launch): add filter_training_args for transformers 5.0 compatibility

Filter unsupported TrainingArguments parameters by inspecting
transformers.TrainingArguments.__init__ signature, avoiding errors
from deprecated or removed parameters in newer versions.

* fix(models): add parse_visual_output for transformers 5.0 compatibility

Visual model methods (get_image_features, get_video_features, visual())
may return tuples OR dataclass objects (BaseModelOutputWithPooling,
BaseModelOutputWithDeepstackFeatures) in transformers 5.0+.

Add parse_visual_output() to transparently handle both return types.

* [feat] Support Qwen3_5 Training (#143)

* [feat] Support Qwen3_5 Training

* style: auto-fix lint (black + isort)

* [feat] Support Qwen3.5 Training

* optimize qwen3.5 dataset process logic

* optimize qwen3.5 dataset process logic

* flop function leave empty

---------

Co-authored-by: charlesswu <charlesswu@tencent.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

* fix(processor): remove duplicate special_tokens property in qwen2_vl_processor

* fix(models): remove duplicate .to() calls in qwen2_5_omni_liger

* fix(models): define input_ids_rmpad in inputs_embeds branch to avoid NameError

* refactor(models): extract parse_visual_output to common_ops/visual.py

* refactor(processor): extract special_tokens logic to DataUtilities.get_special_tokens

* style: auto-fix lint (black + isort)

* docs: add Transformers 5.0 migration guide

Add comprehensive migration guide for transformers 5.0 compatibility.
Includes compatibility matrix, installation instructions, and troubleshooting
for Qwen3.5 (requires >= 5.3.0) and legacy models (requires < 5.0.0).

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: wukeming <108406625+KemingWu@users.noreply.github.com>
Co-authored-by: charlesswu <charlesswu@tencent.com>
Co-authored-by: mwxely <yang0756@e.ntu.edu.sg>