Add experimental support for transformers>=5.0 + min torch 2.8

.github/workflows/example_tests.yml

@@ -70,7 +70,7 @@ jobs:
     uses: ./.github/workflows/_example_tests_runner.yml
     secrets: inherit
     with:
-      docker_image: "nvcr.io/nvidia/pytorch:${{ matrix.docker_image || '26.01' }}-py3"
+      docker_image: "nvcr.io/nvidia/pytorch:${{ matrix.docker_image || '26.03' }}-py3"
       example: ${{ matrix.example }}
       timeout_minutes: 30
       pip_install_extras: "[hf,dev-test]"
@@ -82,7 +82,7 @@ jobs:
     uses: ./.github/workflows/_example_tests_runner.yml
     secrets: inherit
     with:
-      docker_image: "nvcr.io/nvidia/pytorch:${{ matrix.docker_image || '26.01' }}-py3"
+      docker_image: "nvcr.io/nvidia/pytorch:${{ matrix.docker_image || '26.03' }}-py3"
       example: ${{ matrix.example }}
       timeout_minutes: 30
       pip_install_extras: "[hf,dev-test]"
@@ -99,7 +99,7 @@ jobs:
     uses: ./.github/workflows/_example_tests_runner.yml
     secrets: inherit
     with:
-      docker_image: "nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc5"
+      docker_image: "nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc10"
       example: ${{ matrix.example }}
       pip_install_extras: "[hf,dev-test]"
       runner: linux-amd64-gpu-rtxpro6000-latest-1
@@ -113,7 +113,7 @@ jobs:
     uses: ./.github/workflows/_example_tests_runner.yml
     secrets: inherit
     with:
-      docker_image: "nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc5"
+      docker_image: "nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc10"
       example: ${{ matrix.example }}
       pip_install_extras: "[hf,dev-test]"
       runner: linux-amd64-gpu-rtxpro6000-latest-2

.github/workflows/gpu_tests.yml

@@ -65,18 +65,19 @@ jobs:
           - example: gpu
             timeout: 45
             container_image: pytorch:26.01-py3
+            # tests/gpu/_extensions/test_onnx_extensions.py fails for newer containers until https://github.com/tbenthompson/cppimport/pull/98
           - example: gpu-megatron
             timeout: 45
             container_image: pytorch:26.01-py3
           - example: gpu-trtllm
             timeout: 30
-            container_image: tensorrt-llm/release:1.3.0rc5
+            container_image: tensorrt-llm/release:1.3.0rc10
     runs-on: linux-amd64-gpu-rtxpro6000-latest-1
     timeout-minutes: ${{ matrix.timeout }}
     container: &gpu_container
       image: nvcr.io/nvidia/${{ matrix.container_image }}
       env:
-        GIT_DEPTH: 1000 # For correct version for tests/gpu/torch/quantization/plugins/test_megatron.py
+        GIT_DEPTH: 1000 # For correct version
         PIP_CONSTRAINT: "" # Disable pip constraint for upgrading packages
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
     steps: &gpu_steps

.github/workflows/unit_tests.yml

@@ -38,7 +38,7 @@ jobs:
       - uses: actions/checkout@v6
       - uses: ./.github/actions/ubuntu-setup
       - name: Run unit tests
-        run: pip install tox && COV_ARGS="--cov" tox -e py312-torch210-tf_latest-unit
+        run: pip install tox && COV_ARGS="--cov" tox -e py312-torch211-tf_latest-unit
       - name: Upload coverage reports to Codecov
         uses: codecov/codecov-action@v5
         with:
@@ -65,6 +65,7 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 30
     strategy:
+      fail-fast: false
       matrix:
         py: [10, 11, 13]
     steps:
@@ -73,15 +74,16 @@ jobs:
         with:
           python-version: "3.${{ matrix.py }}"
       - name: Run unit tests
-        run: pip install tox && tox -e py3${{ matrix.py }}-torch210-tf_latest-unit
+        run: pip install tox && tox -e py3${{ matrix.py }}-torch211-tf_latest-unit
   multi-torch:
     if: github.event_name == 'pull_request'
     needs: [linux]
     runs-on: ubuntu-latest
     timeout-minutes: 30
     strategy:
+      fail-fast: false
       matrix:
-        torch: [26, 27, 28, 29]
+        torch: [28, 29, 210]
     steps:
       - uses: actions/checkout@v6
       - uses: ./.github/actions/ubuntu-setup
@@ -93,13 +95,14 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 30
     strategy:
+      fail-fast: false
       matrix:
         tf: [min]
     steps:
       - uses: actions/checkout@v6
       - uses: ./.github/actions/ubuntu-setup
       - name: Run unit tests
-        run: pip install tox && tox -e py312-torch210-tf_${{ matrix.tf }}-unit
+        run: pip install tox && tox -e py312-torch211-tf_${{ matrix.tf }}-unit
   launcher:
     if: github.event_name == 'pull_request'
     needs: [linux]
@@ -123,6 +126,7 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 30
     strategy:
+      fail-fast: false
       matrix:
         test-env: [onnx, torch]
     steps:

CHANGELOG.rst

@@ -1,5 +1,6 @@
-NVIDIA Model Optimizer Changelog
-================================
+Changelog
+=========
+
 0.44 (2026-05-xx)
 ^^^^^^^^^^^^^^^^^
 
@@ -25,6 +26,8 @@ NVIDIA Model Optimizer Changelog
 **Misc**
 
 - [Security] Changed the default of ``weights_only`` to ``True`` in ``torch.load`` for secure checkpoint loading. If you need to load a checkpoint that requires unpickling arbitrary objects, first register the class in ``torch.serialization.add_safe_globals([cls])`` before loading. Added :meth:`safe_save <modelopt.torch.utils.serialization.safe_save>` and :meth:`safe_load <modelopt.torch.utils.serialization.safe_load>` API to save and load checkpoints securely.
+- Bump minimum required PyTorch version to 2.8.
+- [Experimental] Add support for transformers>=5.0. Unified Hugging Face checkpoint export for quantized checkpoints may not work for MoE models with transformers>=5.0 yet.
 
 0.43 (2026-04-09)
 ^^^^^^^^^^^^^^^^^

docs/source/getting_started/_installation_for_Linux.rst

@@ -16,7 +16,7 @@ Latest Model Optimizer (``nvidia-modelopt``) currently has the following system
 +-------------------------+-----------------------------+
 | CUDA                    |  12.x, 13.x                 |
 +-------------------------+-----------------------------+
-| PyTorch                 |  >=2.6                      |
+| PyTorch                 |  >=2.8                      |
 +-------------------------+-----------------------------+
 | TensorRT-LLM (Optional) |  >=1.0                      |
 +-------------------------+-----------------------------+

examples/gpt-oss/configs/sft_full.yaml

@@ -16,7 +16,7 @@ per_device_train_batch_size: 2
 per_device_eval_batch_size: 2
 gradient_accumulation_steps: 2
 max_length: 4096
-warmup_ratio: 0.03
+warmup_steps: 0.03 # use warmup_ratio if using transformers<5.0
 lr_scheduler_type: cosine_with_min_lr
 lr_scheduler_kwargs:
   min_lr_rate: 0.1
@@ -30,6 +30,6 @@ eval_steps: 8
 dataset_test_split: test
 
 # ModelOpt Quantization Parameters
-quant_cfg:  # Examples: MXFP4_MLP_WEIGHT_ONLY_CFG, NVFP4_MLP_WEIGHT_ONLY_CFG, NVFP4_MLP_ONLY_CFG
-            # For the full list of supported configs, do: mtq.config.choices
+quant_cfg: # Examples: MXFP4_MLP_WEIGHT_ONLY_CFG, NVFP4_MLP_WEIGHT_ONLY_CFG, NVFP4_MLP_ONLY_CFG
+  # For the full list of supported configs, do: mtq.config.choices
 calib_size: 128

examples/gpt-oss/configs/sft_lora.yaml

@@ -21,7 +21,7 @@ lora_alpha: 16
 lora_dropout: 0.0
 lora_target_modules: all-linear
 max_length: 4096
-warmup_ratio: 0.03
+warmup_steps: 0.03 # use warmup_ratio if using transformers<5.0
 lr_scheduler_type: cosine_with_min_lr
 lr_scheduler_kwargs:
   min_lr_rate: 0.1
@@ -35,6 +35,6 @@ eval_steps: 8
 dataset_test_split: test
 
 # ModelOpt Quantization Parameters
-quant_cfg:  # Examples: MXFP4_MLP_WEIGHT_ONLY_CFG, NVFP4_MLP_WEIGHT_ONLY_CFG, NVFP4_MLP_ONLY_CFG
-            # For the full list of supported configs, do: mtq.config.choices
+quant_cfg: # Examples: MXFP4_MLP_WEIGHT_ONLY_CFG, NVFP4_MLP_WEIGHT_ONLY_CFG, NVFP4_MLP_ONLY_CFG
+  # For the full list of supported configs, do: mtq.config.choices
 calib_size: 128

examples/gpt-oss/convert_oai_mxfp4_weight_only.py

@@ -122,11 +122,7 @@ def create_parser():
     parser = create_parser()
     args = parser.parse_args()
 
-    kwargs = {
-        "device_map": "auto",
-        "torch_dtype": "auto",
-        "trust_remote_code": args.trust_remote_code,
-    }
+    kwargs = {"device_map": "auto", "dtype": "auto", "trust_remote_code": args.trust_remote_code}
     if args.lora_path:
         assert args.model_path is None, "You can only specify lora_path or model_path, not both."
         model_path = args.base_path

examples/gpt-oss/qat-finetune-transformers.ipynb

@@ -207,7 +207,7 @@
     "    per_device_eval_batch_size=1,\n",
     "    gradient_accumulation_steps=2,\n",
     "    max_length=4096,\n",
-    "    warmup_ratio=0.03,\n",
+    "    warmup_steps=0.03,  # use warmup_ratio if using transformers<5.0\n",
     "    eval_strategy=\"steps\",\n",
     "    eval_on_start=True,\n",
     "    logging_steps=10,\n",

examples/gpt-oss/requirements.txt

@@ -1,5 +1,3 @@
 kernels>=0.9.0
-torch>2.7.1
 trackio
-transformers>=4.55.0
 trl>=0.21.0

examples/gpt-oss/sft.py

@@ -72,7 +72,7 @@ def main(script_args, training_args, model_args, quant_args):
         "revision": model_args.model_revision,
         "trust_remote_code": model_args.trust_remote_code,
         "attn_implementation": model_args.attn_implementation,
-        "torch_dtype": getattr(model_args, "dtype", "bfloat16"),
+        "dtype": getattr(model_args, "dtype", "bfloat16"),
         "use_cache": not training_args.gradient_checkpointing,
     }
 

examples/llm_autodeploy/run_auto_quantize.py

@@ -132,7 +132,7 @@ def modelopt_ptq(
 ) -> torch.nn.Module:
     """Quantize the model with modelopt."""
     model = AutoModelForCausalLM.from_pretrained(
-        model_path, trust_remote_code=trust_remote_code, torch_dtype="auto", device_map="auto"
+        model_path, trust_remote_code=trust_remote_code, dtype="auto", device_map="auto"
     )
     model.eval()
 

examples/llm_distill/requirements.txt

@@ -1,4 +1,3 @@
 pyarrow
 torchao>=0.14.1
-transformers<5.0
 trl>=0.23.0

examples/llm_eval/modeling.py

@@ -187,7 +187,7 @@ def load(self):
                 args.update(device_map="auto")
             if self.load_8bit:
                 args.update(device_map="auto", load_in_8bit=True)
-            args.update(torch_dtype=getattr(torch, self.dtype) if self.dtype != "auto" else "auto")
+            args.update(dtype=getattr(torch, self.dtype) if self.dtype != "auto" else "auto")
             if self.attn_implementation:
                 args["attn_implementation"] = self.attn_implementation
             self.model = AutoModelForSeq2SeqLM.from_pretrained(
@@ -246,7 +246,7 @@ def load(self):
                 args.update(device_map="auto")
             if self.load_8bit:
                 args.update(device_map="auto", load_in_8bit=True)
-            args.update(torch_dtype=getattr(torch, self.dtype) if self.dtype != "auto" else "auto")
+            args.update(dtype=getattr(torch, self.dtype) if self.dtype != "auto" else "auto")
             if self.attn_implementation:
                 args["attn_implementation"] = self.attn_implementation
             self.model = AutoModelForCausalLM.from_pretrained(
@@ -327,7 +327,7 @@ def load(self):
                 args.update(device_map="auto")
             if self.load_8bit:
                 args.update(device_map="auto", load_in_8bit=True)
-            args.update(torch_dtype=getattr(torch, self.dtype) if self.dtype != "auto" else "auto")
+            args.update(dtype=getattr(torch, self.dtype) if self.dtype != "auto" else "auto")
             self.model = LlamaForCausalLM.from_pretrained(self.model_path, **args)
             print_gpu_utilization()
             if self.lora_path:

examples/llm_ptq/README.md

@@ -115,7 +115,7 @@ Please reference our [framework scripts](#framework-scripts) and our [docs](http
 | Kimi K2 | - | - | - | - | ✅ |
 | MiniMax M2.1 | - | - | - | - | ✅ |
 | T5 | ✅ | ✅ | ✅ | ✅ | - |
-| Whisper | ✅ | ❌ | ❌ | ❌ | - |
+| Whisper<sup>9</sup> | ✅ | ❌ | ❌ | ❌ | - |
 | Nemotron-3 | ✅ | ❌ | ❌ | ❌ | ✅ |
 
 > *This is a subset of the models supported. For the full list please check the [TensorRT-LLM support matrix](https://nvidia.github.io/TensorRT-LLM/reference/precision.html#support-matrix)*
@@ -127,7 +127,8 @@ Please reference our [framework scripts](#framework-scripts) and our [docs](http
 > *<sup>5.</sup>A selective set of the popular models are internally tested. The actual model support list may be longer. NVFP4 inference requires Blackwell GPUs and TensorRT-LLM v0.17 or later* \
 > *<sup>6.</sup>Some models currently support export to HF format only.* \
 > *<sup>7.</sup>[PTQ for DeepSeek](../deepseek/README.md)* \
-> *<sup>8.</sup>GLM-4.7 has MTP (Multi-Token Prediction) layers that are automatically loaded and excluded from quantization.*
+> *<sup>8.</sup>GLM-4.7 has MTP (Multi-Token Prediction) layers that are automatically loaded and excluded from quantization.* \
+> *<sup>9.</sup>Running Whisper model with transformers>=5.0 requires [torchcodec](https://github.com/meta-pytorch/torchcodec?tab=readme-ov-file#installing-cuda-enabled-torchcodec) and other system packages (e.g. ffmpeg).*
 
 > *The accuracy loss after PTQ may vary depending on the actual model and the quantization method. Different models may have different accuracy loss and usually the accuracy loss is more significant when the base model is small. If the accuracy after PTQ is not meeting the requirement, please try either modifying [hf_ptq.py](./hf_ptq.py) and disabling the KV cache quantization or using the [QAT](./../llm_qat/README.md) instead. For NVFP4 quantization specifically, we recommend `nvfp4_mlp_only`, `nvfp4_experts_only`, or `nvfp4_omlp_only` to achieve higher accuracy by restricting quantization to the MLP/expert layers (and optionally the `o_proj` layer) while keeping the attention QKV projections unquantized.*
 

examples/llm_ptq/example_utils.py

@@ -583,7 +583,7 @@ def get_model(
     model_kwargs = config_kwargs.copy()
     # Don't set torch_dtype for VILA models as they handle it explicitly in their builder
     if "vila" not in ckpt_path.lower():
-        model_kwargs.setdefault("torch_dtype", "auto")
+        model_kwargs.setdefault("dtype", "auto")
 
     if "vila" in ckpt_path.lower():
         hf_vila = AutoModel.from_pretrained(
@@ -634,7 +634,7 @@ def has_pack_quantized_config(config):
                     ckpt_path,
                     device_map="auto",
                     trust_remote_code=trust_remote_code,
-                    torch_dtype="auto",
+                    dtype="auto",
                 )
         else:
             architecture = hf_config.architectures[0]
@@ -666,7 +666,7 @@ def has_pack_quantized_config(config):
                 model_kwargs2 = model_kwargs.copy()
                 if auto_model_module not in [AutoModelForCausalLM, AutoModel]:
                     model_kwargs2.pop("trust_remote_code", None)
-                model_kwargs2["torch_dtype"] = torch_dtype
+                model_kwargs2["dtype"] = torch_dtype
                 model_kwargs2.pop("max_memory", None)
                 model = from_config(hf_config, **model_kwargs2)
 

examples/llm_ptq/multinode_ptq.py

@@ -149,9 +149,7 @@ def load_and_prepare_model(
         Tuple of (prepared_model, model_type, original_architectures, calibration_dataloader)
     """
     model = AutoModelForCausalLM.from_pretrained(
-        model_path,
-        torch_dtype="auto",
-        trust_remote_code=trust_remote_code,
+        model_path, dtype="auto", trust_remote_code=trust_remote_code
     )
     model.eval()
     model_type = get_model_type(model)

examples/llm_ptq/requirements.txt

@@ -2,5 +2,6 @@ compressed-tensors==0.12.0
 fire
 flash-attn>=2.6.0
 rouge_score>=0.1.2
+transformers<5.0
 transformers_stream_generator
 zstandard

examples/llm_qat/launch.sh

@@ -165,7 +165,7 @@ CMD="accelerate launch --config-file accelerate_config/$CONFIG_FILE $FSDP_ARGS \
     --save_total_limit 2 \
     --learning_rate $LR \
     --weight_decay 0.0 \
-    --warmup_ratio 0.1 \
+    --warmup_steps 0.1 \
     --lr_scheduler_type linear \
     --logging_steps 1 \
     --report_to tensorboard \

examples/llm_qat/main.py

@@ -174,9 +174,7 @@ def train():
         print_rank_0(f"Last checkpoint detected: {last_checkpoint}")
 
     model = transformers.AutoModelForCausalLM.from_pretrained(
-        model_args.model_name_or_path,
-        cache_dir=training_args.cache_dir,
-        torch_dtype=torch.bfloat16,
+        model_args.model_name_or_path, cache_dir=training_args.cache_dir, dtype=torch.bfloat16
     )
     model.generation_config.do_sample = True
     tokenizer = transformers.AutoTokenizer.from_pretrained(
@@ -231,7 +229,7 @@ def train():
         teacher_model = transformers.AutoModelForCausalLM.from_pretrained(
             model_args.teacher_model,
             cache_dir=training_args.cache_dir,
-            torch_dtype=torch.bfloat16,
+            dtype=torch.bfloat16,
         )
         distill_config = {
             "teacher_model": teacher_model,

examples/llm_qat/notebooks/QAT_QAD_Walkthrough.ipynb

@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "0bf60614-99a0-48b0-85a8-1d88cd7c72ba",
    "metadata": {},
    "outputs": [],
@@ -290,7 +290,7 @@
     "    per_device_eval_batch_size=1,\n",
     "    gradient_accumulation_steps=2,\n",
     "    max_length=4096,\n",
-    "    warmup_ratio=0.03,\n",
+    "    warmup_steps=0.03,  # use warmup_ratio if using transformers<5.0\n",
     "    eval_strategy=\"steps\",\n",
     "    eval_on_start=True,\n",
     "    logging_steps=50,\n",

examples/llm_sparsity/attention_sparsity/hf_sa.py

@@ -143,10 +143,7 @@ def main(args):
     # No need to specify attn_implementation here — mtsa.sparsify() sets it
     # automatically ("eager" for pytorch backend, "modelopt_triton" for triton).
     model = AutoModelForCausalLM.from_pretrained(
-        args.pyt_ckpt_path,
-        attn_implementation="eager",
-        torch_dtype="auto",
-        device_map="auto",
+        args.pyt_ckpt_path, attn_implementation="eager", dtype="auto", device_map="auto"
     )
     tokenizer = AutoTokenizer.from_pretrained(args.pyt_ckpt_path)