[fix] fit neat_packing & mrope model packing (#10283)

Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>

.github/workflows/tests.yml

@@ -35,15 +35,12 @@ jobs:
         transformers:
           - ""
         include:  # test backward compatibility
-          - python: "3.11"
-            os: "ubuntu-latest"
-            transformers: "4.51.0"
-          - python: "3.11"
-            os: "ubuntu-latest"
-            transformers: "4.53.0"
           - python: "3.11"
             os: "ubuntu-latest"
             transformers: "4.55.0"
+          - python: "3.11"
+            os: "ubuntu-latest"
+            transformers: "4.57.1"
 
     runs-on: ${{ matrix.os }}
 

data/dataset_info.json

@@ -236,6 +236,13 @@
     "ms_hub_url": "AI-ModelScope/sharegpt_gpt4",
     "formatting": "sharegpt"
   },
+  "sgsc_b2b_entities": {
+    "hf_hub_url": "Nooxus-AI/NOO-Verified-Global-Entities",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages"
+    }
+  },
   "ultrachat_200k": {
     "hf_hub_url": "HuggingFaceH4/ultrachat_200k",
     "ms_hub_url": "AI-ModelScope/ultrachat_200k",

docker/docker-npu/Dockerfile

@@ -1,6 +1,6 @@
 # https://hub.docker.com/r/ascendai/cann/tags
 
-ARG BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-910b-ubuntu22.04-py3.11
+ARG BASE_IMAGE=quay.io/ascend/cann:8.5.1-910b-ubuntu22.04-py3.11
 FROM ${BASE_IMAGE}
 
 # Installation arguments
@@ -33,9 +33,11 @@ RUN pip config set global.index-url "${PIP_INDEX}" && \
 COPY . /app
 
 # Install torch-npu
-RUN pip uninstall -y torch torchvision torchaudio && \
+RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh
-    pip install --no-cache-dir "torch==2.7.1" "torch-npu==2.7.1" "torchvision==0.22.1" "torchaudio==2.7.1" --index-url "${PYTORCH_INDEX}" && \
+RUN pip uninstall -y torch torchvision torchaudio
-    pip install --no-cache-dir -e . --no-build-isolation && \
+RUN pip install --no-cache-dir -r requirements/npu.txt --index-url "${PYTORCH_INDEX}"
+RUN pip install --no-cache-dir -r requirements/deepspeed.txt
+RUN pip install --no-cache-dir -e . --no-build-isolation && \
     pip install --no-cache-dir -r requirements/metrics.txt --no-build-isolation
 
 # Set up volumes

docker/docker-npu/docker-compose.yml

@@ -33,7 +33,7 @@ services:
       dockerfile: ./docker/docker-npu/Dockerfile
       context: ../..
       args:
-        BASE_IMAGE: quay.io/ascend/cann:8.3.rc2-a3-ubuntu22.04-py3.11
+        BASE_IMAGE: quay.io/ascend/cann:8.5.1-a3-ubuntu22.04-py3.11
         PIP_INDEX: https://pypi.org/simple
     container_name: llamafactory-a3
     image: llamafactory:npu-a3

examples/train_lora/qwen3_lora_sft_ray.yaml

@@ -28,12 +28,7 @@ save_only_model: false
 report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
 
 ### ray
-ray_run_name: qwen3_4b_sft_lora
-ray_storage_path: ./saves
 ray_num_workers: 4  # Number of GPUs to use.
-placement_strategy: PACK
-resources_per_worker:
-  GPU: 1
 # ray_init_kwargs:
 #   runtime_env:
 #     env_vars:

pyproject.toml

@@ -40,7 +40,7 @@ dependencies = [
     "torch>=2.4.0",
     "torchvision>=0.19.0",
     "torchaudio>=2.4.0",
-    "transformers>=4.51.0,<=5.2.0,!=4.52.0,!=4.57.0",
+    "transformers>=4.55.0,<=5.2.0,!=4.52.0,!=4.57.0",
     "datasets>=2.16.0,<=4.0.0",
     "accelerate>=1.3.0,<=1.11.0",
     "peft>=0.18.0,<=0.18.1",

requirements/npu.txt

@@ -1,4 +1,4 @@
 torch==2.7.1
-torch-npu==2.7.1
+torch-npu==2.7.1.post2
 torchvision==0.22.1
 torchaudio==2.7.1

scripts/megatron_merge.py

@@ -71,6 +71,7 @@ def convert(
     pipeline_model_parallel_size: int = 1,
     expert_model_parallel_size: int = 1,
     virtual_pipeline_model_parallel_size: int | None = None,
+    moe_grouped_gemm: bool | None = None,
 ):
     """Convert checkpoint between MCA and HuggingFace formats.
 
@@ -84,6 +85,10 @@ def convert(
         pipeline_model_parallel_size: Pipeline model parallel size
         expert_model_parallel_size: Expert model parallel size
         virtual_pipeline_model_parallel_size: Virtual pipeline model parallel size
+        moe_grouped_gemm: Use grouped gemm for MoE experts. When enabled, expert
+            weights are stored in a flattened format (linear_fc1.weight0, weight1, ...)
+            rather than per-expert format (local_experts.0.linear_fc1.weight, ...).
+            Must match the format used when saving the checkpoint.
     """
     if bf16 and fp16:
         raise ValueError("bf16 and fp16 cannot be both True.")
@@ -97,8 +102,9 @@ def convert(
             pipeline_model_parallel_size=pipeline_model_parallel_size,
             expert_model_parallel_size=expert_model_parallel_size,
             virtual_pipeline_model_parallel_size=virtual_pipeline_model_parallel_size,
+            moe_grouped_gemm=moe_grouped_gemm,
+            transformer_impl="transformer_engine",  # hard code here since we default using te for training
         )
-
         convert_checkpoint_to_mca(
             checkpoint_path,
             output_path,

src/llamafactory/api/chat.py

@@ -88,7 +88,10 @@ def _process_request(
 
     if request.messages[0].role == Role.SYSTEM:
         content = request.messages.pop(0).content
-        system = content[0].text if isinstance(content, list) else content
+        if isinstance(content, list):
+            system = content[0].text if content else ""
+        else:
+            system = content
     else:
         system = None
 

src/llamafactory/data/collator.py

@@ -15,6 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
 import inspect
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Literal, Optional
@@ -25,7 +26,7 @@ import torch.nn.functional as F
 from peft import PeftModel
 from transformers import DataCollatorForSeq2Seq
 
-from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER
+from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, MROPE_MODELS
 from ..extras.packages import is_pillow_available
 
 
@@ -39,6 +40,56 @@ if TYPE_CHECKING:
     from .template import Template
 
 
+def _slice_mm_inputs_for_sample(
+    mm_inputs: dict[str, Any],
+    batch_imglens: list[int],
+    batch_vidlens: list[int],
+    batch_idx: int,
+    images_per_subseq: Optional[list[int]] = None,
+    videos_per_subseq: Optional[list[int]] = None,
+    subseq_idx: Optional[int] = None,
+) -> dict[str, Any]:
+    r"""Slice mm_inputs for one batch sample, optionally for a single sub-sequence when packing.
+
+    image_grid_thw / video_grid_thw have shape [num_items, 3]. Indices for sample batch_idx
+    are batch_imglens[batch_idx] images and batch_vidlens[batch_idx] videos. When subseq_idx
+    is given, further restrict to that sub-seq's counts via packed_*_counts.
+    has_dummy_image=True means only batch[0] will be concated with fake image and no multimodal data.
+    """
+    image_start_idx = sum(batch_imglens[:batch_idx])
+    image_end_idx = sum(batch_imglens[: batch_idx + 1])
+    video_start_idx = sum(batch_vidlens[:batch_idx])
+    video_end_idx = sum(batch_vidlens[: batch_idx + 1])
+
+    if subseq_idx is not None and images_per_subseq is not None:
+        image_start_idx += sum(images_per_subseq[:subseq_idx])
+        image_end_idx = image_start_idx + images_per_subseq[subseq_idx]
+
+    if subseq_idx is not None and videos_per_subseq is not None:
+        video_start_idx += sum(videos_per_subseq[:subseq_idx])
+        video_end_idx = video_start_idx + videos_per_subseq[subseq_idx]
+
+    sliced_mm_inputs: dict[str, Any] = {}
+    key_to_slice_meta = {
+        "image_grid_thw": (image_start_idx, image_end_idx, True),
+        "video_grid_thw": (video_start_idx, video_end_idx, True),
+        "second_per_grid_ts": (video_start_idx, video_end_idx, False),  # qwen2.5vl
+        "video_second_per_grid": (video_start_idx, video_end_idx, False),  # qwen omni
+    }
+
+    for key, (start_idx, end_idx, assign_none_when_empty) in key_to_slice_meta.items():
+        if key not in mm_inputs:
+            continue
+
+        mm_value = mm_inputs[key]
+        if mm_value is not None and end_idx > start_idx:
+            sliced_mm_inputs[key] = mm_value[start_idx:end_idx]
+        elif assign_none_when_empty:
+            sliced_mm_inputs[key] = None
+
+    return sliced_mm_inputs
+
+
 def prepare_4d_attention_mask(attention_mask_with_indices: "torch.Tensor", dtype: "torch.dtype") -> "torch.Tensor":
     r"""Expand 2d attention mask to 4d attention mask.
 
@@ -106,9 +157,154 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
         else:
             self.get_rope_func = None
 
+    def _compute_rope_position_ids(
+        self, features: dict[str, "torch.Tensor"], mm_inputs: dict[str, Any]
+    ) -> None:
+        r"""Compute position_ids and rope_deltas via get_rope_func for VLMs."""
+        rope_index_kwargs = {
+            "input_ids": features["input_ids"],
+            "image_grid_thw": mm_inputs.get("image_grid_thw"),
+            "video_grid_thw": mm_inputs.get("video_grid_thw"),
+            "attention_mask": (features["attention_mask"] >= 1).float(),
+        }
+        if features["attention_mask"].sum() == 0:
+            features["position_ids"] = torch.zeros((3, *features["input_ids"].shape))
+            features["rope_deltas"] = torch.zeros(features["input_ids"].shape[0])
+            return
+
+        if "mm_token_type_ids" in inspect.signature(self.get_rope_func).parameters:
+            image_token_id = getattr(self.model.config, "image_token_id", None)
+            video_token_id = getattr(self.model.config, "video_token_id", None)
+            if image_token_id is not None or video_token_id is not None:
+                mm_token_type_ids = torch.zeros_like(features["input_ids"])
+                if image_token_id is not None:
+                    mm_token_type_ids[features["input_ids"] == image_token_id] = 1
+                if video_token_id is not None:
+                    mm_token_type_ids[features["input_ids"] == video_token_id] = 2
+                rope_index_kwargs["mm_token_type_ids"] = mm_token_type_ids
+
+        if "second_per_grid_ts" in mm_inputs:  # for qwen2vl
+            rope_index_kwargs["second_per_grid_ts"] = mm_inputs.get("second_per_grid_ts")
+        elif "video_second_per_grid" in mm_inputs:  # for qwen2.5 omni
+            rope_index_kwargs["second_per_grids"] = mm_inputs.get("video_second_per_grid")
+
+        if getattr(self.model.config, "model_type", None) in ["qwen2_5_omni_thinker", "qwen3_omni_moe_thinker"]:
+            rope_index_kwargs["use_audio_in_video"] = getattr(self.processor, "use_audio_in_video", False)
+            feature_attention_mask = mm_inputs.get("feature_attention_mask", None)
+            if feature_attention_mask is not None:  # FIXME: need to get video image lengths
+                audio_feature_lengths = torch.sum(feature_attention_mask, dim=1)
+                rope_index_kwargs["audio_seqlens"] = audio_feature_lengths  # prepare for input
+
+            features["position_ids"], rope_deltas = self.get_rope_func(**rope_index_kwargs)
+            features["rope_deltas"] = rope_deltas - (1 - rope_index_kwargs["attention_mask"]).sum(
+                dim=-1
+            ).unsqueeze(-1)
+        else:  # for qwen vl
+            features["position_ids"], features["rope_deltas"] = self.get_rope_func(**rope_index_kwargs)
+
+    def _compute_rope_position_ids_with_packing(
+        self,
+        features: dict[str, "torch.Tensor"],
+        mm_inputs: dict[str, Any],
+        packing_params_list: list[dict[str, Any] | None],
+        batch_imglens: list[int],
+        batch_vidlens: list[int],
+        batch_audlens: list[int],
+        has_dummy_image: bool,
+    ) -> None:
+        r"""Compute position_ids and rope_deltas per sample (or per sub-sequence when packed), then merge and validate."""
+        bsz = features["input_ids"].size(0)
+        seq_len = features["input_ids"].size(1)
+        all_position_ids: list[torch.Tensor] = []
+        all_rope_deltas: list[torch.Tensor] = []
+
+        if has_dummy_image:
+            # for [0, seq_len] = [0, unpadded_length + right_padding_length + fake_input_ids_len + collator_padding_length]
+            # FIXME: maybe right_padding_length is large, with improper max_cutoff_len
+            unpadded_length = int(features["attention_mask"][0].bool().sum().item())
+            right_padding_length = int((packing_params_list[0] or {}).get("right_padding_length") or 0)
+            fake_input_padding_length = max(0, seq_len - unpadded_length - right_padding_length)
+            dummy_image_right_padding_mrope = torch.zeros((3, bsz, fake_input_padding_length))
+            dummy_image_right_padding_attention_mask = torch.zeros((bsz, fake_input_padding_length))
+            assert self.tokenizer.padding_side == "right", "padding_side should be right when fake image is injected"
+            dummy_mm_inputs = copy.deepcopy(mm_inputs)
+
+        for sample_idx in range(bsz):
+            sample_packing = (packing_params_list[sample_idx] or {}) if sample_idx < len(packing_params_list) else {}
+            sequence_boundaries = sample_packing.get("sequence_boundaries")
+            num_sub_seqs = (len(sequence_boundaries) - 1) if sequence_boundaries and len(sequence_boundaries) > 1 else 1
+            image_subseq_ids = sample_packing.get("image_subseq_ids") or []
+            video_subseq_ids = sample_packing.get("video_subseq_ids") or []
+            images_per_subseq = (
+                [image_subseq_ids.count(i) for i in range(num_sub_seqs)] if image_subseq_ids and num_sub_seqs > 1 else None
+            )
+            videos_per_subseq = (
+                [video_subseq_ids.count(i) for i in range(num_sub_seqs)] if video_subseq_ids and num_sub_seqs > 1 else None
+            )
+            if has_dummy_image:
+                mm_inputs = {}
+
+            if num_sub_seqs <= 1:
+                sample_features = {
+                    "input_ids": features["input_ids"],
+                    "attention_mask": features["attention_mask"][sample_idx : sample_idx + 1],
+                }
+                mm_inputs_for_sample = _slice_mm_inputs_for_sample(
+                    mm_inputs, batch_imglens, batch_vidlens, sample_idx=sample_idx
+                )
+                self._compute_rope_position_ids(sample_features, mm_inputs_for_sample)
+                all_position_ids.append(sample_features["position_ids"])
+                all_rope_deltas.append(sample_features["rope_deltas"])
+            else:
+                # when we do packing, don't need rope_deltas when training.
+                sample_position_ids: list[torch.Tensor] = []
+                for subseq_idx in range(num_sub_seqs):
+                    subseq_start = sequence_boundaries[subseq_idx]
+                    subseq_end = sequence_boundaries[subseq_idx + 1]
+                    subseq_features = {
+                        "input_ids": features["input_ids"][sample_idx : sample_idx + 1, subseq_start:subseq_end],
+                        "attention_mask": features["attention_mask"][sample_idx : sample_idx + 1, subseq_start:subseq_end],
+                    }
+                    mm_inputs_for_subseq = _slice_mm_inputs_for_sample(
+                        mm_inputs,
+                        batch_imglens,
+                        batch_vidlens,
+                        sample_idx,
+                        images_per_subseq,
+                        videos_per_subseq,
+                        subseq_idx
+                    )
+                    self._compute_rope_position_ids(subseq_features, mm_inputs_for_subseq)
+                    sample_position_ids.append(subseq_features["position_ids"])
+                all_position_ids.append(torch.cat(sample_position_ids, dim=-1))
+
+        batch_dim_for_position_ids = 1 if all_position_ids[0].dim() == 3 else 0
+
+        features["position_ids"] = torch.cat(all_position_ids, dim=batch_dim_for_position_ids)
+        if has_dummy_image:
+            mm_inputs = dummy_mm_inputs
+
+        expected_position_ids_shape = (bsz, seq_len) if all_position_ids[0].dim() == 2 else (
+            all_position_ids[0].size(0),
+            bsz,
+            seq_len,
+        )
+        # Check if position_ids shape matches expected shape.
+        # for further usage, we should padding to the right when some padding token on the right.
+        if has_dummy_image:
+            features["position_ids"] = torch.cat([features["position_ids"], dummy_image_right_padding_mrope], dim=-1)
+            features["attention_mask"] = torch.cat([features["attention_mask"], dummy_image_right_padding_attention_mask], dim=-1)
+
+        if features["position_ids"].shape != expected_position_ids_shape:
+            raise ValueError(
+                "Merged position_ids shape mismatch: "
+                f"got {features['position_ids'].shape}, expected {expected_position_ids_shape}."
+            )
+
     def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         batch_images, batch_videos, batch_audios = [], [], []
         batch_imglens, batch_vidlens, batch_audlens, batch_input_ids = [], [], [], []
+        packing_params_list: list[dict[str, Any] | None] = []
         for feature in features:
             images = feature.pop("images", None) or []
             videos = feature.pop("videos", None) or []
@@ -120,8 +316,10 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
             batch_vidlens.append(len(videos))
             batch_audlens.append(len(audios))
             batch_input_ids.append(feature["input_ids"])
+            packing_params_list.append(feature.pop("packing_params", None))
 
         fake_input_ids = []
+        has_dummy_image = False
         if (
             self.template.mm_plugin.image_token is not None and sum(batch_imglens) == 0 and sum(batch_vidlens) == 0
         ):  # avoid process hanging in zero3/fsdp case
@@ -137,6 +335,7 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
             fake_input_ids.extend(_fake_input_ids)
             batch_images = fake_images
             batch_imglens[0] = 1
+            has_dummy_image = True
 
         if (
             self.template.mm_plugin.audio_token is not None and sum(batch_audlens) == 0
@@ -183,57 +382,50 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
 
         features: dict[str, torch.Tensor] = super().__call__(features)
 
+        bsz, seq_len = features["input_ids"].shape[:2]
+        model_type = getattr(self.model.config, "model_type", None) if self.model is not None else None
+        is_omni = model_type in [
+            "qwen2_5_omni_thinker",
+            "qwen3_omni_moe_thinker",
+        ]
+
         if self.get_rope_func is not None:
-            rope_index_kwargs = {
+            # for mmrope situation, we should calculate position_ids and rope_deltas per sample.
-                "input_ids": features["input_ids"],
+            # When neat_packing is on, each sample has packing_params; None means no packing for that sample.
-                "image_grid_thw": mm_inputs.get("image_grid_thw"),
+            boundaries_list = [
-                "video_grid_thw": mm_inputs.get("video_grid_thw"),
+                p.get("sequence_boundaries") if p is not None else None for p in packing_params_list
-                "attention_mask": (features["attention_mask"] >= 1).float(),
+            ]
-            }
+            has_packing = any(b is not None and len(b) > 2 for b in boundaries_list)
-            if "mm_token_type_ids" in inspect.signature(self.get_rope_func).parameters:
+            if has_dummy_image and has_packing:
-                image_token_id = getattr(self.model.config, "image_token_id", None)
+                # FIXME: too tricky, need to be refactored
-                video_token_id = getattr(self.model.config, "video_token_id", None)
+                features["has_dummy_image"] = True
-                if image_token_id is not None or video_token_id is not None:
-                    mm_token_type_ids = torch.zeros_like(features["input_ids"])
-                    if image_token_id is not None:
-                        mm_token_type_ids[features["input_ids"] == image_token_id] = 1
-                    if video_token_id is not None:
-                        mm_token_type_ids[features["input_ids"] == video_token_id] = 2
-                    rope_index_kwargs["mm_token_type_ids"] = mm_token_type_ids
-            if "second_per_grid_ts" in mm_inputs:  # for qwen2vl
-                rope_index_kwargs["second_per_grid_ts"] = mm_inputs.get("second_per_grid_ts")
-            elif "video_second_per_grid" in mm_inputs:  # for qwen2.5 omni
-                rope_index_kwargs["second_per_grids"] = mm_inputs.get("video_second_per_grid")
 
-            if getattr(self.model.config, "model_type", None) in ["qwen2_5_omni_thinker", "qwen3_omni_moe_thinker"]:
+            # When fake image/audio was injected, sequence_boundaries no longer match the tensor; use non-packing path.
-                rope_index_kwargs["use_audio_in_video"] = getattr(self.processor, "use_audio_in_video", False)
+            if not has_packing:
-                feature_attention_mask = mm_inputs.get("feature_attention_mask", None)
+                self._compute_rope_position_ids(features, mm_inputs)
-                if feature_attention_mask is not None:  # FIXME: need to get video image lengths
+            else:
-                    audio_feature_lengths = torch.sum(feature_attention_mask, dim=1)
+                if is_omni:
-                    rope_index_kwargs["audio_seqlens"] = audio_feature_lengths  # prepare for input
+                    raise RuntimeError("Omni models are not supported for packed sequences for now.")
 
-                features["position_ids"], rope_deltas = self.get_rope_func(**rope_index_kwargs)
+                self._compute_rope_position_ids_with_packing(
-                features["rope_deltas"] = rope_deltas - (1 - rope_index_kwargs["attention_mask"]).sum(
+                    features,
-                    dim=-1
+                    mm_inputs,
-                ).unsqueeze(-1)
+                    packing_params_list,
-            else:  # for qwen vl
+                    batch_imglens,
-                features["position_ids"], features["rope_deltas"] = self.get_rope_func(**rope_index_kwargs)
+                    batch_vidlens,
+                    batch_audlens,
+                    has_dummy_image,
+                )
+
+            # For transformers compatibility, after https://github.com/huggingface/transformers/issues/39400
+            if features["position_ids"].dim() == 3:
+                features["position_ids"] = torch.cat(
+                    [features["position_ids"][0].unsqueeze(0), features["position_ids"]], dim=0
+                )
 
         if (
             self.model is not None
-            and getattr(self.model.config, "model_type", None)
+            and getattr(self.model.config, "model_type", None) in MROPE_MODELS
-            in [
-                "glm4v",
-                "glm_ocr",
-                "Keye",
-                "qwen2_vl",
-                "qwen2_5_vl",
-                "qwen2_5_omni_thinker",
-                "qwen3_omni_moe_thinker",
-                "qwen3_5",
-                "qwen3_vl",
-                "qwen3_vl_moe",
-            ]
             and ("position_ids" not in features or features["position_ids"].dim() != 3)
         ):
             raise ValueError(f"{self.model.config.model_type} requires 3D position ids for mrope.")
@@ -261,12 +453,51 @@ class SFTDataCollatorWith4DAttentionMask(MultiModalDataCollatorForSeq2Seq):
     block_diag_attn: bool = False
     attn_implementation: Literal["eager", "sdpa", "flash_attention_2"] = "eager"
     compute_dtype: "torch.dtype" = torch.float32
+    neat_packing: bool = False
+
+    def __post_init__(self):
+        super().__post_init__()
+        if self.neat_packing and self.attn_implementation == "flash_attention_2":
+            if self.model is not None and getattr(self.model.config, "model_type", None) in ["qwen3_5", "qwen3_5_moe", "gpt_oss"]:
+                raise ValueError("Neat packing is not supported for qwen3_5, qwen3_5_moe, gpt_oss models for now.")
+
+    @staticmethod
+    def _unpad_packed_features(features: dict[str, Any]) -> None:
+        r"""Trim padded positions for packed FA2 batches."""
+        attention_mask = features.get("attention_mask")
+        if not torch.is_tensor(attention_mask) or attention_mask.dim() != 2 or attention_mask.size(0) != 1:
+            return
+
+        seq_len = attention_mask.size(1)
+        non_padding_indices = torch.nonzero(attention_mask[0] != 0, as_tuple=False).flatten()
+        if non_padding_indices.numel() == seq_len:
+            return
+
+        keys_on_seq_dim_1 = {"input_ids", "labels", "attention_mask", "token_type_ids"}
+        for key, value in list(features.items()):
+            if not torch.is_tensor(value):
+                continue
+
+            if key == "position_ids" and value.size(-1) == seq_len:
+                features[key] = value.index_select(-1, non_padding_indices)
+            elif key == "cross_attention_mask" and value.dim() >= 2 and value.size(0) == 1 and value.size(1) == seq_len:
+                features[key] = value.index_select(1, non_padding_indices)
+            elif key in keys_on_seq_dim_1 and value.dim() == 2 and value.size(0) == 1 and value.size(1) == seq_len:
+                features[key] = value.index_select(1, non_padding_indices)
 
     def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         features = super().__call__(features)
+        has_dummy_image = features.pop("has_dummy_image", False)
         if self.block_diag_attn and self.attn_implementation != "flash_attention_2":
             features["attention_mask"] = prepare_4d_attention_mask(features["attention_mask"], self.compute_dtype)
 
+        if self.neat_packing and self.attn_implementation == "flash_attention_2": # FIXME compatibility fa3/fa4
+            assert features["input_ids"].shape[0] == 1, "bsz should be 1 for neat packing"
+            if not has_dummy_image:
+                self._unpad_packed_features(features)
+
+            features["attention_mask"] = None  # let transformers handle causal packed mask.
+
         for key, value in features.items():  # cast data dtype for paligemma
             if torch.is_tensor(value) and torch.is_floating_point(value):
                 features[key] = value.to(self.compute_dtype)

src/llamafactory/data/mm_plugin.py

@@ -27,11 +27,12 @@ from typing import TYPE_CHECKING, BinaryIO, Literal, NotRequired, Optional, Type
 import numpy as np
 import torch
 import torchaudio
-from transformers.image_utils import get_image_size, is_valid_image, to_numpy_array
+from transformers.image_utils import get_image_size, is_valid_image, make_flat_list_of_images, to_numpy_array
 from transformers.models.mllama.processing_mllama import (
     convert_sparse_cross_attention_mask_to_dense,
     get_cross_attention_token_mask,
 )
+from transformers.video_utils import make_batched_videos
 from typing_extensions import override
 
 from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
@@ -47,13 +48,6 @@ if is_pyav_available():
     import av
 
 
-if is_transformers_version_greater_than("4.52.0"):
-    from transformers.image_utils import make_flat_list_of_images
-    from transformers.video_utils import make_batched_videos
-else:
-    from transformers.image_utils import make_batched_videos, make_flat_list_of_images
-
-
 if TYPE_CHECKING:
     from av.stream import Stream
     from numpy.typing import NDArray
@@ -161,7 +155,9 @@ class MMPluginMixin:
         video_processor: BaseImageProcessor = getattr(
             processor, "video_processor", getattr(processor, "image_processor", None)
         )
-        feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None)
+        feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None) or getattr(
+            processor, "audio_processor", None
+        )
         if len(images) != 0 and self.image_token is None:
             raise ValueError(
                 "This model does not support image input. Please check whether the correct `template` is used."
@@ -390,7 +386,9 @@ class MMPluginMixin:
                 mm_inputs.update(video_processor(videos, return_tensors="pt"))
 
         if len(audios) != 0:
-            feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None)
+            feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None) or getattr(
+                processor, "audio_processor", None
+            )
             audios = self._regularize_audios(
                 audios,
                 sampling_rate=getattr(processor, "audio_sampling_rate", 16000),
@@ -1054,7 +1052,9 @@ class MiniCPMVPlugin(BasePlugin):
                 chunk_input=True,
                 sampling_rate=getattr(processor, "audio_sampling_rate", 16000),
             )
-            audio_feature_lens = [torch.tensor(audio_feature_len) for audio_feature_len in audio_feature_lens]
+            audio_feature_lens = [
+                x.clone().detach() if isinstance(x, torch.Tensor) else torch.tensor(x) for x in audio_feature_lens
+            ]
             mm_inputs.update({"audio_features": audio_features, "audio_feature_lens": audio_feature_lens})
             if kwargs.get("ret_phs", False):
                 mm_inputs.update({"audio_phs": audio_phs})
@@ -1094,7 +1094,7 @@ class MiniCPMVPlugin(BasePlugin):
                 num_image_tokens += 1
 
             while VIDEO_PLACEHOLDER in content:
-                video_seqlen = len(mm_inputs["pixel_values"][num_video_tokens]) if self.expand_mm_tokens else 1
+                video_seqlen = len(mm_inputs["image_sizes"][num_video_tokens]) if self.expand_mm_tokens else 1
                 content = content.replace(VIDEO_PLACEHOLDER, "{{image}}" * video_seqlen, 1)
                 num_video_tokens += 1
 
@@ -1876,7 +1876,9 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
     ) -> dict[str, "torch.Tensor"]:
         image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
         video_processor: BaseVideoProcessor = getattr(processor, "video_processor", None)
-        feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None)
+        feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None) or getattr(
+            processor, "audio_processor", None
+        )
         mm_inputs = {}
         if len(images) != 0:
             images = self._regularize_images(
@@ -1981,6 +1983,7 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
                             f"Each {VIDEO_PLACEHOLDER} must be followed by an {AUDIO_PLACEHOLDER} when using audio in video."
                         )
 
+                    position_id_per_seconds: int = getattr(processor, "position_id_per_seconds", 25)
                     audio_t_index = torch.arange(audio_lengths[num_audio_tokens])
                     video_t_index = (
                         torch.arange(video_grid_thw[num_video_tokens][0])
@@ -1992,9 +1995,9 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
                         )
                         .flatten()
                         * mm_inputs["video_second_per_grid"][num_video_tokens]
-                        * 25  # FIXME hardcode of position_id_per_seconds=25
+                        * position_id_per_seconds
                     ).long()
-                    t_ntoken_per_chunk = 50  # FIXME hardcode: [25 * 2]
+                    t_ntoken_per_chunk = position_id_per_seconds * 2
                     video_chunk_indices = processor.get_chunked_index(video_t_index, t_ntoken_per_chunk)
                     audio_chunk_indices = processor.get_chunked_index(audio_t_index, t_ntoken_per_chunk)
                     placeholder_string = ""

src/llamafactory/data/processor/supervised.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from collections import defaultdict
-from dataclasses import dataclass
+from dataclasses import asdict, dataclass
 from typing import TYPE_CHECKING, Any, Optional
 
 from ...extras import logging
@@ -27,6 +27,23 @@ if TYPE_CHECKING:
 
 logger = logging.get_logger(__name__)
 
+MAX_SU_SEQ_IDX = 2**32 # maximum sub-sequence index
+
+@dataclass
+class PackingParams:
+    r"""Metadata for a packed sequence: sub-sequence boundaries and multimodal data indices.
+
+    - sequence_boundaries: cumulative token positions, e.g. [0, 100, 250, 512] means 3 sub-seqs
+      with token ranges [0,100), [100,250), [250,512). Length = num_sub_seqs + 1.
+    - image_subseq_ids / video_subseq_ids / audio_subseq_ids: for each mm item, the 0-based
+      sub-sequence index it belongs to. Length = total number of that mm type in the packed sample.
+    """
+
+    sequence_boundaries: list[int]
+    image_subseq_ids: list[int]
+    video_subseq_ids: list[int]
+    audio_subseq_ids: list[int]
+    right_padding_length: int
 
 @dataclass
 class SupervisedDatasetProcessor(DatasetProcessor):
@@ -162,10 +179,17 @@ class PackedSupervisedDatasetProcessor(SupervisedDatasetProcessor):
                 valid_num += 1
 
         model_inputs = defaultdict(list)
+        requires_packing_params = self.data_args.neat_packing
         knapsacks = greedy_knapsack(lengths, self.data_args.cutoff_len)
         for knapsack in knapsacks:
             packed_input_ids, packed_attention_masks, packed_position_ids, packed_labels = [], [], [], []
             packed_images, packed_videos, packed_audios = [], [], []
+            if requires_packing_params:
+                sequence_boundaries = [0]
+                image_subseq_ids: list[int] = []
+                video_subseq_ids: list[int] = []
+                audio_subseq_ids: list[int] = []
+
             for i, length in enumerate(knapsack):
                 index = length2indexes[length].pop()
                 packed_input_ids += batch_input_ids[index]
@@ -174,6 +198,15 @@ class PackedSupervisedDatasetProcessor(SupervisedDatasetProcessor):
                 packed_images += batch_images[index]
                 packed_videos += batch_videos[index]
                 packed_audios += batch_audios[index]
+                if requires_packing_params:
+                    n_img = len(batch_images[index])
+                    n_vid = len(batch_videos[index])
+                    n_aud = len(batch_audios[index])
+                    sequence_boundaries.append(sequence_boundaries[-1] + len(batch_input_ids[index]))
+                    image_subseq_ids.extend([i] * n_img)
+                    video_subseq_ids.extend([i] * n_vid)
+                    audio_subseq_ids.extend([i] * n_aud)
+
                 if self.data_args.neat_packing:
                     packed_attention_masks += [i + 1] * len(batch_input_ids[index])  # start from 1
                 else:
@@ -189,10 +222,23 @@ class PackedSupervisedDatasetProcessor(SupervisedDatasetProcessor):
                 else:
                     packed_attention_masks += [1] * pad_length  # more efficient flash_attn
 
+                if requires_packing_params:
+                    sequence_boundaries.append(sequence_boundaries[-1] + pad_length)
+
             if len(packed_input_ids) != self.data_args.cutoff_len + 1:
                 raise ValueError("The length of packed example should be identical to the cutoff length.")
 
             model_inputs["input_ids"].append(packed_input_ids)
+            if requires_packing_params:
+                packing_params = PackingParams(
+                    sequence_boundaries=sequence_boundaries,
+                    image_subseq_ids=image_subseq_ids or [MAX_SU_SEQ_IDX], # avoid dataset concat error
+                    video_subseq_ids=video_subseq_ids or [MAX_SU_SEQ_IDX],
+                    audio_subseq_ids=audio_subseq_ids or [MAX_SU_SEQ_IDX],
+                    right_padding_length=pad_length,
+                )
+                model_inputs["packing_params"].append(asdict(packing_params))
+
             model_inputs["attention_mask"].append(packed_attention_masks)
             model_inputs["position_ids"].append(packed_position_ids)
             model_inputs["labels"].append(packed_labels)

src/llamafactory/data/template.py

@@ -1113,7 +1113,7 @@ register_template(
 register_template(
     name="gpt_oss",
     format_user=StringFormatter(slots=["<|start|>user<|message|>{{content}}<|end|><|start|>assistant"]),
-    format_assistant=StringFormatter(slots=["{{content}}<|end|>"]),
+    format_assistant=StringFormatter(slots=["{{content}}"]),
     format_system=StringFormatter(slots=["<|start|>system<|message|>{{content}}<|end|>"]),
     default_system="You are ChatGPT, a large language model trained by OpenAI.",
     thought_words=("<|channel|>analysis<|message|>", "<|end|><|start|>assistant<|channel|>final<|message|>"),

src/llamafactory/extras/constants.py

@@ -69,12 +69,28 @@ MCA_SUPPORTED_MODELS = {
     "qwen3",
     "qwen3_moe",
     "qwen3_next",
+    "qwen3_5",
+    "qwen3_5_moe",
 }
 
 METHODS = ["full", "freeze", "lora", "oft"]
 
 MOD_SUPPORTED_MODELS = {"bloom", "falcon", "gemma", "llama", "mistral", "mixtral", "phi", "starcoder2"}
 
+MROPE_MODELS = {
+    "glm4v",
+    "glm_ocr",
+    "Keye",
+    "qwen2_vl",
+    "qwen2_5_vl",
+    "qwen2_5_omni_thinker",
+    "qwen3_omni_moe_thinker",
+    "qwen3_vl",
+    "qwen3_vl_moe",
+    "qwen3_5",
+    "qwen3_5_moe",
+}
+
 MULTIMODAL_SUPPORTED_MODELS = set()
 
 PEFT_METHODS = {"lora", "oft"}

src/llamafactory/extras/misc.py

@@ -94,7 +94,7 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
 
 def check_dependencies() -> None:
     r"""Check the version of the required packages."""
-    check_version("transformers>=4.51.0,<=5.2.0")
+    check_version("transformers>=4.55.0,<=5.2.0")
     check_version("datasets>=2.16.0,<=4.0.0")
     check_version("accelerate>=1.3.0,<=1.11.0")
     check_version("peft>=0.18.0,<=0.18.1")

src/llamafactory/hparams/parser.py

@@ -33,7 +33,7 @@ from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_availab
 from ..extras import logging
 from ..extras.constants import CHECKPOINT_NAMES, EngineName
 from ..extras.misc import check_dependencies, check_version, get_current_device, is_env_enabled
-from ..extras.packages import is_mcore_adapter_available, is_transformers_version_greater_than
+from ..extras.packages import is_mcore_adapter_available
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments
@@ -394,9 +394,6 @@ def get_train_args(args: dict[str, Any] | list[str] | None = None) -> _TRAIN_CLS
     if model_args.use_kt and is_deepspeed_zero3_enabled():
         raise ValueError("KTransformers is incompatible with DeepSpeed ZeRO-3.")
 
-    if data_args.neat_packing and is_transformers_version_greater_than("4.53.0"):
-        raise ValueError("Neat packing is incompatible with transformers>=4.53.0.")
-
     _set_env_vars()
     _verify_model_args(model_args, data_args, finetuning_args)
     _check_extra_dependencies(model_args, finetuning_args, training_args)
@@ -470,7 +467,7 @@ def get_train_args(args: dict[str, Any] | list[str] | None = None) -> _TRAIN_CLS
         training_args.resume_from_checkpoint is None
         and training_args.do_train
         and os.path.isdir(training_args.output_dir)
-        and not training_args.overwrite_output_dir
+        and not getattr(training_args, "overwrite_output_dir", False) # for mca training args and transformers >= 5.0
         and can_resume_from_checkpoint
     ):
         last_checkpoint = get_last_checkpoint(training_args.output_dir)

src/llamafactory/model/model_utils/packing.py

@@ -37,7 +37,6 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-from typing import TYPE_CHECKING
 
 import torch
 import torch.nn.functional as F
@@ -45,10 +44,6 @@ import torch.nn.functional as F
 from ...extras import logging
 
 
-if TYPE_CHECKING:
-    from ...hparams import ModelArguments
-
-
 logger = logging.get_logger(__name__)
 
 
@@ -105,13 +100,3 @@ def get_unpad_data(attention_mask: "torch.Tensor") -> tuple["torch.Tensor", "tor
     max_seqlen_in_batch = seqlens_in_batch.max().item()
     cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
     return indices, cu_seqlens, max_seqlen_in_batch
-
-
-def configure_packing(model_args: "ModelArguments", is_trainable: bool) -> None:
-    if not is_trainable or not model_args.block_diag_attn:
-        return
-
-    import transformers.modeling_flash_attention_utils
-
-    transformers.modeling_flash_attention_utils._get_unpad_data = get_unpad_data
-    logger.info_rank0("Using block diagonal attention for sequence packing without cross-attention.")

src/llamafactory/model/model_utils/visual.py

@@ -24,7 +24,6 @@ import transformers.models
 from transformers.activations import ACT2FN
 
 from ...extras import logging
-from ...extras.packages import is_transformers_version_greater_than
 
 
 if TYPE_CHECKING:
@@ -344,9 +343,7 @@ _register_composite_model(
     model_type="qwen2_vl",
     projector_key="visual.merger",
     vision_model_keys=["visual.patch_embed", "visual.blocks"],
-    language_model_keys=["language_model", "lm_head"]
+    language_model_keys=["language_model", "lm_head"],
-    if is_transformers_version_greater_than("4.52.0")
-    else ["model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
 )
 
@@ -355,9 +352,7 @@ _register_composite_model(
     model_type="qwen2_5_vl",
     projector_key="visual.merger",
     vision_model_keys=["visual.patch_embed", "visual.blocks"],
-    language_model_keys=["language_model", "lm_head"]
+    language_model_keys=["language_model", "lm_head"],
-    if is_transformers_version_greater_than("4.52.0")
-    else ["model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
 )
 

src/llamafactory/model/patcher.py

@@ -30,7 +30,6 @@ from .model_utils.embedding import resize_embedding_layer
 from .model_utils.kv_cache import configure_kv_cache
 from .model_utils.longlora import configure_longlora
 from .model_utils.moe import add_z3_leaf_module, configure_moe
-from .model_utils.packing import configure_packing
 from .model_utils.quantization import configure_quantization
 from .model_utils.rope import configure_rope
 from .model_utils.valuehead import prepare_valuehead_model
@@ -142,7 +141,6 @@ def patch_config(
     configure_quantization(config, tokenizer, model_args, is_trainable, init_kwargs)
     configure_moe(config, model_args, is_trainable)
     configure_visual_model(config)
-    configure_packing(model_args, is_trainable)
     configure_kv_cache(config, model_args, is_trainable)
 
     if getattr(config, "model_type", None) == "qwen":

src/llamafactory/train/callbacks.py

@@ -228,7 +228,7 @@ class LogCallback(TrainerCallback):
         if (
             args.should_save
             and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
-            and args.overwrite_output_dir
+            and getattr(args, "overwrite_output_dir", False)
         ):
             logger.warning_rank0_once("Previous trainer log in this folder will be deleted.")
             os.remove(os.path.join(args.output_dir, TRAINER_LOG))

src/llamafactory/train/mca/workflow.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 
 import functools
+import json
+import os
 from collections.abc import Sequence
 from copy import deepcopy
 from typing import TYPE_CHECKING, Any, Optional
@@ -77,20 +79,25 @@ def _data_collator_wrapper(data_collator: Any):
 
 def _check_model_support(model_args: "ModelArguments"):
     from transformers import AutoConfig as HfAutoConfig
+    if os.path.exists(os.path.join(model_args.model_name_or_path, "mca_config.json")): # load from mcore ckpt
+        mca_config = json.load(open(os.path.join(model_args.model_name_or_path, "mca_config.json")))
+        model_type = mca_config.get("hf_model_type", None)
+    else:
+        config = HfAutoConfig.from_pretrained(
+            model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+        )
+        model_type = config.model_type
 
-    config = HfAutoConfig.from_pretrained(
+    if model_type not in MCA_SUPPORTED_MODELS:
-        model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
-    )
-    if config.model_type not in MCA_SUPPORTED_MODELS:
         raise ValueError(
-            f"Model {config.model_type} is not supported by mcore_adapter."
+            f"Model {model_type} is not supported by mcore_adapter."
             "You can try to upgrade mcore_adapter to the latest version for more supported models."
         )
 
 
 def _freeze_model_parameters(model: Any, finetuning_args: "FinetuningArguments"):
     """Freeze model parameters for qwen_vl series models based on finetuning arguments."""
-    if getattr(model.config, "hf_model_type", None) not in ["qwen2_vl", "qwen2_5_vl", "qwen3_vl", "qwen3_vl_moe"]:
+    if getattr(model.config, "hf_model_type", None) not in ["qwen2_vl", "qwen2_5_vl", "qwen3_vl", "qwen3_vl_moe", "qwen3_5", "qwen3_5_moe"]:
         return
 
     params_to_freeze = []

src/llamafactory/train/sft/workflow.py

@@ -65,6 +65,7 @@ def run_sft(
         pad_to_multiple_of=8 if training_args.do_train else None,  # for shift short attention
         label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
         block_diag_attn=model_args.block_diag_attn,
+        neat_packing=data_args.neat_packing,
         attn_implementation=getattr(model.config, "_attn_implementation", None),
         compute_dtype=model_args.compute_dtype,
         **tokenizer_module,

src/llamafactory/train/trainer_utils.py

@@ -50,9 +50,9 @@ if is_apollo_available():
 
 if is_ray_available():
     import ray
-    from ray.util.state import list_nodes
     from ray.util.placement_group import PlacementGroup, placement_group
     from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
+    from ray.util.state import list_nodes
 
 
 if TYPE_CHECKING:

src/llamafactory/v1/core/utils/rendering.py

@@ -91,7 +91,11 @@ class Renderer:
         self.processor = processor
 
     def render_messages(
-        self, messages: list[Message], tools: str | None = None, is_generate: bool = False
+        self,
+        messages: list[Message],
+        tools: str | None = None,
+        is_generate: bool = False,
+        enable_thinking: bool = False,
     ) -> ModelInput:
         """Apply template to messages and convert them to model input.
 
@@ -99,6 +103,7 @@ class Renderer:
             messages (list[Message]): The messages to render.
             tools (str | None, optional): The tools to use. Defaults to None.
             is_generate (bool, optional): Whether to render for generation. Defaults to False.
+            enable_thinking (bool, optional): Whether to enable thinking mode for generation. Defaults to False.
 
         Returns:
             ModelInput: The rendered model input.
@@ -108,7 +113,9 @@ class Renderer:
         else:
             from ...plugins.model_plugins.rendering import RenderingPlugin
 
-            return RenderingPlugin(self.template).render_messages(self.processor, messages, tools, is_generate)
+            return RenderingPlugin(self.template).render_messages(
+                self.processor, messages, tools, is_generate, enable_thinking
+            )
 
     def parse_message(self, generated_text: str) -> Message:
         """Parse a message in the template format.

src/llamafactory/v1/plugins/model_plugins/rendering.py

@@ -12,224 +12,45 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import json
+import importlib
-import re
 
-from ...utils.constants import IGNORE_INDEX
+from ...utils import logging
-from ...utils.helper import get_tokenizer
 from ...utils.plugin import BasePlugin
-from ...utils.types import Message, ModelInput, Processor, ToolCall
+from ...utils.types import Message, ModelInput, Processor
+
+
+logger = logging.get_logger(__name__)
 
 
 class RenderingPlugin(BasePlugin):
+    _attempted_template_imports: set[str] = set()
+
+    def _ensure_template_imported(self) -> None:
+        if self.name is None or self.name in self._attempted_template_imports:
+            return
+
+        full_module_name = f"{__package__}.templates.{self.name}"
+        self._attempted_template_imports.add(self.name)
+        try:
+            importlib.import_module(full_module_name)
+        except Exception as exc:
+            logger.warning(f"[Template Registry] Failed to import {full_module_name}: {exc}")
+
+    def __getitem__(self, method_name: str):
+        self._ensure_template_imported()
+        return super().__getitem__(method_name)
+
     def render_messages(
         self,
         processor: Processor,
         messages: list[Message],
         tools: str | None = None,
         is_generate: bool = False,
+        enable_thinking: bool = False,
     ) -> ModelInput:
         """Render messages in the template format."""
-        return self["render_messages"](processor, messages, tools, is_generate)
+        return self["render_messages"](processor, messages, tools, is_generate, enable_thinking)
 
     def parse_messages(self, generated_text: str) -> Message:
         """Parse messages in the template format."""
         return self["parse_messages"](generated_text)
-
-
-def _update_model_input(
-    processor: Processor,
-    input_ids: list[int],
-    labels: list[int],
-    loss_weights: list[int],
-    temp_str: str,
-    temp_weight: float,
-) -> str:
-    """Update model input with temporary string."""
-    if not temp_str:
-        return ""
-
-    tokenizer = get_tokenizer(processor)
-    temp_ids = tokenizer.encode(temp_str, add_special_tokens=False)
-    input_ids.extend(temp_ids)
-    loss_weights.extend([temp_weight] * len(temp_ids))
-    if temp_weight > 1e-6:
-        labels.extend(temp_ids)
-    else:
-        labels.extend([IGNORE_INDEX] * len(temp_ids))
-
-    return ""
-
-
-@RenderingPlugin("qwen3_nothink").register("render_messages")
-def render_qwen3_nothink_messages(
-    processor: Processor,
-    messages: list[Message],
-    tools: str | None = None,
-    is_generate: bool = False,
-) -> ModelInput:
-    """Render messages in the Qwen3 nothink template format.
-
-    See https://huggingface.co/spaces/huggingfacejs/chat-template-playground?modelId=Qwen/Qwen3-4B-Instruct-2507
-    """
-    input_ids, labels, loss_weights = [], [], []
-    temp_str, temp_weight = "", 0.0
-    if tools:
-        temp_str += "<|im_start|>system\n"
-        if messages[0]["role"] == "system":
-            for content in messages[0]["content"]:
-                if content["type"] == "text":
-                    temp_str += content["value"]
-                else:
-                    raise ValueError(f"Unsupported content type: {content['type']}")
-
-            temp_str += "\n\n"
-            temp_weight = messages[0].get("loss_weight", 0.0)
-
-        temp_str += (
-            "# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
-            "You are provided with function signatures within <tools></tools> XML tags:\n<tools>"
-        )
-        try:
-            tools = json.loads(tools)
-        except json.JSONDecodeError:
-            raise ValueError(f"Invalid tools format: {str(tools)}.")
-
-        if not isinstance(tools, list):
-            tools = [tools]
-
-        for tool in tools:
-            temp_str += "\n" + json.dumps(tool, ensure_ascii=False)
-
-        temp_str += (
-            "\n</tools>\n\nFor each function call, return a json object with function name "
-            'and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{"name": '
-            '<function-name>, "arguments": <args-json-object>}\n</tool_call><|im_end|>\n'
-        )
-    elif messages[0]["role"] == "system":
-        temp_str += "<|im_start|>system\n"
-        for content in messages[0]["content"]:
-            if content["type"] == "text":
-                temp_str += content["value"]
-            else:
-                raise ValueError(f"Unsupported content type: {content['type']}")
-
-        temp_str += "<|im_end|>\n"
-        temp_weight = messages[0].get("loss_weight", 0.0)
-
-    temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
-
-    for turn_idx, message in enumerate(messages):
-        if message["role"] == "user" or (message["role"] == "system" and turn_idx != 0):
-            temp_str += "<|im_start|>" + message["role"] + "\n"
-            for content in message["content"]:
-                if content["type"] == "text":
-                    temp_str += content["value"]
-                else:
-                    raise ValueError(f"Unsupported content type: {content['type']}")
-
-            temp_str += "<|im_end|>\n"
-            temp_weight = message.get("loss_weight", 0.0)
-        elif message["role"] == "assistant":
-            temp_str += "<|im_start|>" + message["role"] + "\n"
-            for val_idx, content in enumerate(message["content"]):
-                if content["type"] == "text":
-                    temp_str += content["value"]
-                elif content["type"] == "reasoning":
-                    temp_str += "<thinking>\n" + content["value"] + "\n</thinking>\n\n"  # avoid using special tokens
-                elif content["type"] == "tool_call":
-                    if val_idx != 0 and message["content"][val_idx - 1]["type"] in ["text", "tool_call"]:
-                        temp_str += "\n"
-
-                    try:
-                        tool_call: ToolCall = json.loads(content["value"])
-                    except json.JSONDecodeError:
-                        raise ValueError(f"Invalid tool call format: {content['value']}.")
-
-                    temp_str += (
-                        '<tool_call>\n{"name": "'
-                        + tool_call["name"]
-                        + '", "arguments": '
-                        + json.dumps(tool_call["arguments"], ensure_ascii=False)
-                        + "}\n</tool_call>"
-                    )
-
-                else:
-                    raise ValueError(f"Unsupported content type: {content['type']}")
-
-            temp_str += "<|im_end|>\n"
-            temp_weight = message.get("loss_weight", 1.0)
-        elif message["role"] == "tool":
-            if turn_idx == 0 or messages[turn_idx - 1]["role"] != "tool":
-                temp_str += "<|im_start|>user"
-
-            temp_str += "\n<tool_response>\n"
-            for content in message["content"]:
-                if content["type"] == "text":
-                    temp_str += content["value"]
-                else:
-                    raise ValueError(f"Unsupported content type: {content['type']}")
-
-            temp_str += "\n</tool_response>"
-            if turn_idx == len(messages) - 1 or messages[turn_idx + 1]["role"] != "tool":
-                temp_str += "<|im_end|>\n"
-
-            temp_weight = message.get("loss_weight", 0.0)
-
-        temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
-
-    if is_generate:
-        temp_str += "<|im_start|>assistant\n"
-        temp_weight = 0.0
-
-    temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
-
-    attention_mask = [1] * len(input_ids)
-    return ModelInput(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        labels=labels,
-        loss_weights=loss_weights,
-    )
-
-
-@RenderingPlugin("qwen3_nothink").register("parse_message")
-def parse_qwen3_nothink_message(generated_text: str) -> Message:
-    """Parse a message in the Qwen3 nothink template format. Supports interleaved reasoning and tool calls.
-
-    Args:
-        generated_text (str): The generated text in the Qwen3 nothink template format.
-
-    Returns:
-        Message: The parsed message.
-    """
-    pattern = re.compile(r"<(thinking|tool_call)>\s*(.*?)\s*</\1>\s*", re.DOTALL)
-    content = []
-    last_end = 0
-    for match in pattern.finditer(generated_text):
-        start, end = match.span()
-        if start > last_end:
-            text = generated_text[last_end:start].strip()
-            if text:
-                content.append({"type": "text", "value": text})
-
-        tag_type = match.group(1)
-        tag_value = match.group(2).strip()
-        if tag_type == "thinking":
-            content.append({"type": "reasoning", "value": tag_value.strip()})
-        elif tag_type == "tool_call":
-            try:
-                json.loads(tag_value.strip())
-            except json.JSONDecodeError:
-                raise ValueError(f"Invalid tool call format: {tag_value.strip()}.")
-
-            content.append({"type": "tool_call", "value": tag_value.strip()})
-
-        last_end = end
-
-    if last_end < len(generated_text):
-        text = generated_text[last_end:].strip()
-        if text:
-            content.append({"type": "text", "value": text})
-
-    return Message(role="assistant", content=content)

src/llamafactory/v1/plugins/model_plugins/templates/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on 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.

src/llamafactory/v1/plugins/model_plugins/templates/qwen3.py

@@ -0,0 +1,259 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on 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.
+
+import json
+import re
+
+from ....utils.constants import IGNORE_INDEX
+from ....utils.helper import get_tokenizer
+from ....utils.types import Message, ModelInput, Processor, ToolCall
+from ..rendering import RenderingPlugin
+
+
+def _update_model_input(
+    processor: Processor,
+    input_ids: list[int],
+    labels: list[int],
+    loss_weights: list[int],
+    temp_str: str,
+    temp_weight: float,
+) -> str:
+    """Update model input with temporary string."""
+    if not temp_str:
+        return ""
+
+    tokenizer = get_tokenizer(processor)
+    temp_ids = tokenizer.encode(temp_str, add_special_tokens=False)
+    input_ids.extend(temp_ids)
+    loss_weights.extend([temp_weight] * len(temp_ids))
+    if temp_weight > 1e-6:
+        labels.extend(temp_ids)
+    else:
+        labels.extend([IGNORE_INDEX] * len(temp_ids))
+
+    return ""
+
+
+def _concat_text_content(message: Message) -> str:
+    """Concatenate text fields in a message."""
+    message_text = ""
+    for content in message["content"]:
+        if content["type"] == "text":
+            message_text += content["value"]
+        else:
+            raise ValueError(f"Unsupported content type: {content['type']}")
+
+    return message_text
+
+
+def _get_last_query_index(messages: list[Message]) -> int:
+    """Find the last user query index, excluding wrapped tool responses."""
+    last_query_index = len(messages) - 1
+    for idx in range(len(messages) - 1, -1, -1):
+        message = messages[idx]
+        if message["role"] != "user":
+            continue
+
+        user_text = ""
+        is_plain_text = True
+        for content in message["content"]:
+            if content["type"] != "text":
+                is_plain_text = False
+                break
+            user_text += content["value"]
+
+        if not is_plain_text:
+            continue
+
+        if not (user_text.startswith("<tool_response>") and user_text.endswith("</tool_response>")):
+            last_query_index = idx
+            break
+
+    return last_query_index
+
+
+def _split_assistant_content(message: Message) -> tuple[str, str, list[ToolCall]]:
+    """Split assistant message into text, reasoning and tool calls."""
+    text_content = ""
+    reasoning_content = ""
+    tool_calls: list[ToolCall] = []
+
+    for content in message["content"]:
+        if content["type"] == "text":
+            text_content += content["value"]
+        elif content["type"] == "reasoning":
+            reasoning_content += content["value"]
+        elif content["type"] == "tool_call":
+            try:
+                tool_call: ToolCall = json.loads(content["value"])
+            except json.JSONDecodeError:
+                raise ValueError(f"Invalid tool call format: {content['value']}.")
+
+            tool_calls.append(tool_call)
+        else:
+            raise ValueError(f"Unsupported content type: {content['type']}")
+
+    return text_content, reasoning_content, tool_calls
+
+
+@RenderingPlugin("qwen3").register("render_messages")
+def render_qwen3_messages(
+    processor: Processor,
+    messages: list[Message],
+    tools: str | None = None,
+    is_generate: bool = False,
+    enable_thinking: bool = False,
+) -> ModelInput:
+    """Render messages in the Qwen3 template format.
+
+    See https://huggingface.co/spaces/huggingfacejs/chat-template-playground?modelId=Qwen/Qwen3-8B
+    """
+    input_ids, labels, loss_weights = [], [], []
+    temp_str, temp_weight = "", 0.0
+    if tools:
+        temp_str += "<|im_start|>system\n"
+        if messages[0]["role"] == "system":
+            temp_str += _concat_text_content(messages[0]) + "\n\n"
+            temp_weight = messages[0].get("loss_weight", 0.0)
+
+        temp_str += (
+            "# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
+            "You are provided with function signatures within <tools></tools> XML tags:\n<tools>"
+        )
+        try:
+            tools = json.loads(tools)
+        except json.JSONDecodeError:
+            raise ValueError(f"Invalid tools format: {str(tools)}.")
+
+        if not isinstance(tools, list):
+            tools = [tools]
+
+        for tool in tools:
+            temp_str += "\n" + json.dumps(tool, ensure_ascii=False)
+
+        temp_str += (
+            "\n</tools>\n\nFor each function call, return a json object with function name "
+            'and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{"name": '
+            '<function-name>, "arguments": <args-json-object>}\n</tool_call><|im_end|>\n'
+        )
+    elif messages[0]["role"] == "system":
+        temp_str += "<|im_start|>system\n" + _concat_text_content(messages[0]) + "<|im_end|>\n"
+        temp_weight = messages[0].get("loss_weight", 0.0)
+
+    temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
+    last_query_index = _get_last_query_index(messages)
+
+    for turn_idx, message in enumerate(messages):
+        if message["role"] == "user" or (message["role"] == "system" and turn_idx != 0):
+            temp_str += "<|im_start|>" + message["role"] + "\n" + _concat_text_content(message) + "<|im_end|>\n"
+            temp_weight = message.get("loss_weight", 0.0)
+        elif message["role"] == "assistant":
+            temp_str += "<|im_start|>" + message["role"] + "\n"
+
+            text_content, reasoning_content, tool_calls = _split_assistant_content(message)
+            if turn_idx > last_query_index and (turn_idx == len(messages) - 1 or reasoning_content):
+                temp_str += "<think>\n" + reasoning_content.strip("\n") + "\n</think>\n\n" + text_content.lstrip("\n")
+            else:
+                temp_str += text_content
+
+            for tool_call_idx, tool_call in enumerate(tool_calls):
+                if (tool_call_idx == 0 and text_content) or tool_call_idx > 0:
+                    temp_str += "\n"
+
+                arguments = tool_call.get("arguments")
+                if isinstance(arguments, str):
+                    arguments_str = arguments
+                else:
+                    arguments_str = json.dumps(arguments, ensure_ascii=False)
+
+                temp_str += (
+                    '<tool_call>\n{"name": "'
+                    + tool_call["name"]
+                    + '", "arguments": '
+                    + arguments_str
+                    + "}\n</tool_call>"
+                )
+
+            temp_str += "<|im_end|>\n"
+            temp_weight = message.get("loss_weight", 1.0)
+        elif message["role"] == "tool":
+            if turn_idx == 0 or messages[turn_idx - 1]["role"] != "tool":
+                temp_str += "<|im_start|>user"
+
+            temp_str += "\n<tool_response>\n" + _concat_text_content(message) + "\n</tool_response>"
+            if turn_idx == len(messages) - 1 or messages[turn_idx + 1]["role"] != "tool":
+                temp_str += "<|im_end|>\n"
+
+            temp_weight = message.get("loss_weight", 0.0)
+
+        temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
+
+    if is_generate:
+        temp_str += "<|im_start|>assistant\n"
+        temp_weight = 0.0
+        if enable_thinking is False:
+            temp_str += "<think>\n\n</think>\n\n"
+
+    temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
+
+    attention_mask = [1] * len(input_ids)
+    return ModelInput(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        labels=labels,
+        loss_weights=loss_weights,
+    )
+
+
+@RenderingPlugin("qwen3").register("parse_message")
+def parse_qwen3_message(generated_text: str) -> Message:
+    """Parse a message in the Qwen3 template format. Supports interleaved reasoning and tool calls.
+
+    Args:
+        generated_text (str): The generated text in the Qwen3 template format.
+
+    Returns:
+        Message: The parsed message.
+    """
+    pattern = re.compile(r"<(think|tool_call)>\s*(.*?)\s*</\1>\s*", re.DOTALL)
+    content = []
+    last_end = 0
+
+    for match in pattern.finditer(generated_text):
+        start, end = match.span()
+        if start > last_end:
+            text = generated_text[last_end:start].strip()
+            if text:
+                content.append({"type": "text", "value": text})
+
+        tag_type = match.group(1)
+        tag_value = match.group(2).strip()
+        if tag_type == "think":
+            content.append({"type": "reasoning", "value": tag_value.strip()})
+        elif tag_type == "tool_call":
+            try:
+                json.loads(tag_value.strip())
+            except json.JSONDecodeError:
+                raise ValueError(f"Invalid tool call format: {tag_value.strip()}.")
+
+            content.append({"type": "tool_call", "value": tag_value.strip()})
+
+        last_end = end
+
+    if last_end < len(generated_text):
+        text = generated_text[last_end:].strip()
+        if text:
+            content.append({"type": "text", "value": text})
+
+    return Message(role="assistant", content=content)

src/llamafactory/v1/plugins/model_plugins/templates/qwen3_nothink.py

@@ -0,0 +1,209 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on 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.
+
+import json
+import re
+
+from ....utils.constants import IGNORE_INDEX
+from ....utils.helper import get_tokenizer
+from ....utils.types import Message, ModelInput, Processor, ToolCall
+from ..rendering import RenderingPlugin
+
+
+def _update_model_input(
+    processor: Processor,
+    input_ids: list[int],
+    labels: list[int],
+    loss_weights: list[int],
+    temp_str: str,
+    temp_weight: float,
+) -> str:
+    """Update model input with temporary string."""
+    if not temp_str:
+        return ""
+
+    tokenizer = get_tokenizer(processor)
+    temp_ids = tokenizer.encode(temp_str, add_special_tokens=False)
+    input_ids.extend(temp_ids)
+    loss_weights.extend([temp_weight] * len(temp_ids))
+    if temp_weight > 1e-6:
+        labels.extend(temp_ids)
+    else:
+        labels.extend([IGNORE_INDEX] * len(temp_ids))
+
+    return ""
+
+
+def _concat_text_content(message: Message) -> str:
+    """Concatenate text fields in a message."""
+    message_text = ""
+    for content in message["content"]:
+        if content["type"] == "text":
+            message_text += content["value"]
+        else:
+            raise ValueError(f"Unsupported content type: {content['type']}")
+
+    return message_text
+
+
+@RenderingPlugin("qwen3_nothink").register("render_messages")
+def render_qwen3_nothink_messages(
+    processor: Processor,
+    messages: list[Message],
+    tools: str | None = None,
+    is_generate: bool = False,
+    enable_thinking: bool = False,
+) -> ModelInput:
+    """Render messages in the Qwen3 nothink template format.
+
+    See https://huggingface.co/spaces/huggingfacejs/chat-template-playground?modelId=Qwen/Qwen3-4B-Instruct-2507
+    """
+    input_ids, labels, loss_weights = [], [], []
+    temp_str, temp_weight = "", 0.0
+    if tools:
+        temp_str += "<|im_start|>system\n"
+        if messages[0]["role"] == "system":
+            temp_str += _concat_text_content(messages[0]) + "\n\n"
+            temp_weight = messages[0].get("loss_weight", 0.0)
+
+        temp_str += (
+            "# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
+            "You are provided with function signatures within <tools></tools> XML tags:\n<tools>"
+        )
+
+        try:
+            tools = json.loads(tools)
+        except json.JSONDecodeError:
+            raise ValueError(f"Invalid tools format: {str(tools)}.")
+
+        if not isinstance(tools, list):
+            tools = [tools]
+
+        for tool in tools:
+            temp_str += "\n" + json.dumps(tool, ensure_ascii=False)
+
+        temp_str += (
+            "\n</tools>\n\nFor each function call, return a json object with function name "
+            'and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{"name": '
+            '<function-name>, "arguments": <args-json-object>}\n</tool_call><|im_end|>\n'
+        )
+    elif messages[0]["role"] == "system":
+        temp_str += "<|im_start|>system\n" + _concat_text_content(messages[0]) + "<|im_end|>\n"
+        temp_weight = messages[0].get("loss_weight", 0.0)
+
+    temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
+
+    for turn_idx, message in enumerate(messages):
+        if message["role"] == "user" or (message["role"] == "system" and turn_idx != 0):
+            temp_str += "<|im_start|>" + message["role"] + "\n" + _concat_text_content(message) + "<|im_end|>\n"
+            temp_weight = message.get("loss_weight", 0.0)
+        elif message["role"] == "assistant":
+            temp_str += "<|im_start|>" + message["role"] + "\n"
+            for val_idx, content in enumerate(message["content"]):
+                if content["type"] == "text":
+                    temp_str += content["value"]
+                elif content["type"] == "reasoning":
+                    temp_str += "<thinking>\n" + content["value"] + "\n</thinking>\n\n"  # avoid using special tokens
+                elif content["type"] == "tool_call":
+                    if val_idx != 0 and message["content"][val_idx - 1]["type"] in ["text", "tool_call"]:
+                        temp_str += "\n"
+
+                    try:
+                        tool_call: ToolCall = json.loads(content["value"])
+                    except json.JSONDecodeError:
+                        raise ValueError(f"Invalid tool call format: {content['value']}.")
+
+                    temp_str += (
+                        '<tool_call>\n{"name": "'
+                        + tool_call["name"]
+                        + '", "arguments": '
+                        + json.dumps(tool_call["arguments"], ensure_ascii=False)
+                        + "}\n</tool_call>"
+                    )
+
+                else:
+                    raise ValueError(f"Unsupported content type: {content['type']}")
+
+            temp_str += "<|im_end|>\n"
+            temp_weight = message.get("loss_weight", 1.0)
+        elif message["role"] == "tool":
+            if turn_idx == 0 or messages[turn_idx - 1]["role"] != "tool":
+                temp_str += "<|im_start|>user"
+
+            temp_str += "\n<tool_response>\n" + _concat_text_content(message) + "\n</tool_response>"
+            if turn_idx == len(messages) - 1 or messages[turn_idx + 1]["role"] != "tool":
+                temp_str += "<|im_end|>\n"
+
+            temp_weight = message.get("loss_weight", 0.0)
+
+        temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
+
+    if is_generate:
+        temp_str += "<|im_start|>assistant\n"
+        temp_weight = 0.0
+        if enable_thinking:
+            raise ValueError("The qwen3_nothink template does not support thinking mode.")
+
+    temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
+
+    attention_mask = [1] * len(input_ids)
+    return ModelInput(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        labels=labels,
+        loss_weights=loss_weights,
+    )
+
+
+@RenderingPlugin("qwen3_nothink").register("parse_message")
+def parse_qwen3_nothink_message(generated_text: str) -> Message:
+    """Parse a message in the Qwen3 nothink template format. Supports interleaved reasoning and tool calls.
+
+    Args:
+        generated_text (str): The generated text in the Qwen3 nothink template format.
+
+    Returns:
+        Message: The parsed message.
+    """
+    pattern = re.compile(r"<(thinking|tool_call)>\s*(.*?)\s*</\1>\s*", re.DOTALL)
+    content = []
+    last_end = 0
+
+    for match in pattern.finditer(generated_text):
+        start, end = match.span()
+        if start > last_end:
+            text = generated_text[last_end:start].strip()
+            if text:
+                content.append({"type": "text", "value": text})
+
+        tag_type = match.group(1)
+        tag_value = match.group(2).strip()
+        if tag_type == "thinking":
+            content.append({"type": "reasoning", "value": tag_value.strip()})
+        elif tag_type == "tool_call":
+            try:
+                json.loads(tag_value.strip())
+            except json.JSONDecodeError:
+                raise ValueError(f"Invalid tool call format: {tag_value.strip()}.")
+
+            content.append({"type": "tool_call", "value": tag_value.strip()})
+
+        last_end = end
+
+    if last_end < len(generated_text):
+        text = generated_text[last_end:].strip()
+        if text:
+            content.append({"type": "text", "value": text})
+
+    return Message(role="assistant", content=content)

src/llamafactory/v1/utils/types.py

@@ -85,7 +85,7 @@ class DistributedConfig(TypedDict, total=False):
 
 
 class Content(TypedDict):
-    type: Literal["text", "reasoning", "tool_call", "image_url"]
+    type: Literal["text", "reasoning", "tool_call", "image_url", "video_url", "audio_url"]
     """Type of the content."""
     value: str
     """Value of the content."""

tests/data/test_collator.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import os
+from collections import Counter
 
 import pytest
 import torch
@@ -129,9 +130,177 @@ def test_multimodal_collator():
 
     assert batch_input.keys() == expected_input.keys()
     for k in batch_input.keys():
+        if k == "position_ids" and batch_input[k].dim() == 3 and batch_input[k].shape[0] == 4:
+            batch_input[k] = batch_input[k][1:]
+
         assert batch_input[k].eq(torch.tensor(expected_input[k])).all()
 
 
+def _make_packed_feature(
+    *,
+    packing_params: dict,
+    pad_token_id: int,
+    label_ignore_id: int,
+    fake_image: Image.Image,
+    vision_start_id: int | None = None,
+    vision_end_id: int | None = None,
+    image_pad_id: int | None = None,
+) -> dict:
+    r"""Build one packed sample using the new PackingParams schema."""
+    sequence_boundaries = packing_params["sequence_boundaries"]
+    image_subseq_ids = packing_params["image_subseq_ids"]
+    video_subseq_ids = packing_params["video_subseq_ids"]
+    audio_subseq_ids = packing_params["audio_subseq_ids"]
+    unpadded_length = packing_params["unpadded_length"]
+    right_padding_length = packing_params["right_padding_length"] # which only preserved in tests
+    cutoff_plus_one = sequence_boundaries[-1]
+    content_len = unpadded_length
+    pad_len = right_padding_length
+    assert content_len + pad_len == cutoff_plus_one
+    assert sequence_boundaries[0] == 0
+    assert sequence_boundaries[-1] == cutoff_plus_one
+
+    content_ids = list(range(100, 100 + content_len))
+    if vision_start_id is not None and vision_end_id is not None and image_pad_id is not None:
+        image_counts_by_subseq = Counter(image_subseq_ids)
+        for subseq_idx, image_count in sorted(image_counts_by_subseq.items()):
+            if subseq_idx >= len(sequence_boundaries) - 1:
+                continue
+
+            subseq_start = sequence_boundaries[subseq_idx]
+            subseq_end = sequence_boundaries[subseq_idx + 1]
+            subseq_len = subseq_end - subseq_start
+            if subseq_len < 3:
+                continue
+
+            # Build repeated image groups while preserving at least 3 tokens for each remaining image.
+            injected_tokens: list[int] = []
+            remaining = subseq_len
+            for image_idx in range(image_count):
+                remaining_images = image_count - image_idx
+                min_reserved_for_rest = 3 * (remaining_images - 1)
+                current_group_len = min(6, remaining - min_reserved_for_rest)
+                if current_group_len < 3:
+                    break
+
+                group = [vision_start_id] + [image_pad_id] * max(1, current_group_len - 2) + [vision_end_id]
+                injected_tokens.extend(group[:current_group_len])
+                remaining -= current_group_len
+
+            if injected_tokens:
+                insert_end = subseq_start + len(injected_tokens)
+                content_ids[subseq_start:insert_end] = injected_tokens
+
+    input_ids = content_ids + [pad_token_id] * pad_len
+    attention_mask = [1] * content_len + [0] * pad_len
+    labels = [label_ignore_id] * cutoff_plus_one
+
+    return {
+        "input_ids": input_ids,
+        "attention_mask": attention_mask,
+        "labels": labels,
+        "images": [fake_image] * len(image_subseq_ids),
+        "videos": [None] * len(video_subseq_ids),
+        "audios": [None] * len(audio_subseq_ids),
+        "packing_params": packing_params,
+    }
+
+
+def _make_packed_features(
+    *,
+    packing_params: dict,
+    pad_token_id: int,
+    label_ignore_id: int,
+    fake_image: Image.Image,
+    vision_start_id: int,
+    vision_end_id: int,
+    image_pad_id: int,
+) -> list[dict]:
+    r"""Build packed features from caller-provided packing_params."""
+    return [
+        _make_packed_feature(
+            packing_params=packing_params,
+            pad_token_id=pad_token_id,
+            label_ignore_id=label_ignore_id,
+            fake_image=fake_image,
+            vision_start_id=vision_start_id,
+            vision_end_id=vision_end_id,
+            image_pad_id=image_pad_id,
+        )
+    ]
+
+def _get_expected_position_ids(packing_params, get_rope_func, input_ids, attention_mask) -> torch.Tensor:
+    bound_list = packing_params["sequence_boundaries"]
+    input_ids_slices = [input_ids[bound_list[i]:bound_list[i+1]] for i in range(len(bound_list) - 1)]
+    attention_mask_slices = [attention_mask[bound_list[i]:bound_list[i+1]] for i in range(len(bound_list) - 1)]
+    img_counts_by_subseq = Counter(packing_params["image_subseq_ids"])
+    all_position_ids = []
+    for i, input_ids_slice in enumerate(input_ids_slices):
+        img_cnt = img_counts_by_subseq[i]
+        if sum(attention_mask_slices[i]) == 0:
+            continue
+
+        rope_func_kwargs = {
+            "input_ids": torch.tensor(input_ids_slice).unsqueeze(0),
+            "attention_mask": torch.tensor(attention_mask_slices[i]).unsqueeze(0),
+            "image_grid_thw": [torch.tensor([1, 4, 4])] * img_cnt,
+        }
+        position_ids, _ = get_rope_func(**rope_func_kwargs)
+        all_position_ids.append(position_ids)
+
+    return torch.cat(all_position_ids, dim=-1)
+
+
+@pytest.mark.runs_on(["cpu", "mps"])
+def test_multimodal_collator_with_packing():
+    model_args, data_args, *_ = get_infer_args(
+        {"model_name_or_path": "Qwen/Qwen2-VL-2B-Instruct", "template": "qwen2_vl"}
+    )
+    tokenizer_module = load_tokenizer(model_args)
+    template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
+    tokenizer_module["tokenizer"].padding_side = "right"
+    config = AutoConfig.from_pretrained(model_args.model_name_or_path)
+    with torch.device("meta"):
+        model = AutoModelForImageTextToText.from_config(config)
+
+    data_collator = MultiModalDataCollatorForSeq2Seq(
+        template=template,
+        model=model,
+        pad_to_multiple_of=4,
+        label_pad_token_id=IGNORE_INDEX,
+        **tokenizer_module,
+    )
+
+    tokenizer = tokenizer_module["tokenizer"]
+    packing_params = {
+        "sequence_boundaries": [0, 2, 10, 18, 28, 32],
+        "image_subseq_ids": [1, 2, 3],
+        "video_subseq_ids": [],
+        "audio_subseq_ids": [],
+        "unpadded_length": 28,
+        "right_padding_length": 4,
+    }
+    fake_image = Image.new("RGB", (64, 64), (255, 255, 255))
+    features = _make_packed_features(
+        packing_params=packing_params,
+        pad_token_id=tokenizer.pad_token_id,
+        label_ignore_id=IGNORE_INDEX,
+        fake_image=fake_image,
+        vision_start_id=tokenizer.convert_tokens_to_ids("<|vision_start|>"),
+        vision_end_id=tokenizer.convert_tokens_to_ids("<|vision_end|>"),
+        image_pad_id=tokenizer.convert_tokens_to_ids("<|image_pad|>"),
+    )
+    expected_position_ids = _get_expected_position_ids(
+        packing_params,
+        data_collator.get_rope_func,
+        features[0]["input_ids"],
+        features[0]["attention_mask"],
+    )
+    batch_input = data_collator(features) # [3, bsz, seq_len]
+    valid_len = expected_position_ids.shape[-1]
+    assert batch_input["position_ids"][1:, :, :valid_len].eq(expected_position_ids).all()
+
+
 @pytest.mark.runs_on(["cpu"])
 def test_4d_attention_mask():
     o = 0.0