bump hf deps (#2735) [skip ci]

* bump hf deps

* upgrade liger-kernel too

* install cce from fork for transformers fix

* fix reference to vocab size in gemma3 patch

* use padding_idx instead of pad_token_id

* remove fixed gemma3 patch

* use updated cce fork

* fix local mllama cce patches w docstring

* add test for multipack with trainer setup and fix trainer for trainer refactor upstream

* bump modal version

* guard for iterable datasetS

* mllama model arch layout changed in latest transformers

* fix batch sampler with drop_last

* fix: address upstream vlm changes for lora

* fix: update references to old lora target path

* fix: remove mllama fa2 patch due to upstream fix

* fix: lora kernel patch path for multimodal models

* fix: removed mllama from quarto

* run test for came optim on 2.6.0+

* fix fsdp2 patch and remove deprecated patch

* make sure to set sequence_parallel_degree for grpo

* Add SP test for GRPO

* add sp to grpo config for trainer

* use reward_funcs as kwarg to grpo trainer

* fix the comprehension for reward funcs

* reward funcs already passed in as args

* init sp_group right before training

* fix check for adding models to SP context

* make sure to pass args to super

* upgrade deepspeed

* use updated trl and add reasoning flags for vllm

* patch the worker

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>

tests/e2e/integrations/test_kd.py

@@ -90,7 +90,7 @@ class TestKnowledgeDistillation:
         train(cfg=cfg, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "model.safetensors").exists()
         check_tensorboard(
-            temp_dir + "/runs", "train/loss", 1.2, "Train Loss (%s) is too high"
+            temp_dir + "/runs", "train/loss", 1.4, "Train Loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(