[model] add llama4

requirements.txt

@@ -1,6 +1,4 @@
-transformers>=4.41.2,<=4.50.0,!=4.46.*,!=4.47.*,!=4.48.*;python_version<'3.10' and sys_platform != 'darwin'
-transformers>=4.41.2,<=4.50.0,!=4.46.*,!=4.47.*,!=4.48.0;python_version>='3.10' and sys_platform != 'darwin'
-transformers>=4.41.2,<=4.49.0,!=4.46.*,!=4.47.*,!=4.48.*;sys_platform == 'darwin'
+transformers>=4.41.2,<=4.51.0,!=4.46.*,!=4.47.*,!=4.48.0
 datasets>=2.16.0,<=3.4.1
 accelerate>=0.34.0,<=1.5.2
 peft>=0.14.0,<=0.15.0

scripts/convert_ckpt/tiny_llama4.py

@@ -0,0 +1,39 @@
+# 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.
+
+from transformers import Llama4Config, Llama4ForConditionalGeneration, Llama4TextConfig, Llama4VisionConfig
+
+
+if __name__ == "__main__":
+    vision_config = Llama4VisionConfig(
+        hidden_size=1408,
+        image_size=336,
+        intermediate_size=5632,
+        num_attention_heads=16,
+        num_hidden_layers=4,
+        vision_output_dim=4096,
+    )
+    text_config = Llama4TextConfig(
+        hidden_size=512,
+        intermediate_size=1024,
+        intermediate_size_mlp=1024,
+        num_hidden_layers=4,
+        num_attention_heads=8,
+        num_key_value_heads=2,
+        head_dim=512 // 8,
+        num_local_experts=2,
+    )
+    config = Llama4Config(vision_config=vision_config, text_config=text_config)
+    model = Llama4ForConditionalGeneration._from_config(config)
+    model.save_pretrained("tiny-llama4")

src/llamafactory/__init__.py

@@ -19,7 +19,7 @@
 
 Dependency graph:
   main:
-    transformers>=4.41.2,<=4.50.0,!=4.46.*,!=4.47.*,!=4.48.0
+    transformers>=4.41.2,<=4.51.0,!=4.46.*,!=4.47.*,!=4.48.0
     datasets>=2.16.0,<=3.4.1
     accelerate>=0.34.0,<=1.5.2
     peft>=0.14.0,<=0.15.0

src/llamafactory/data/mm_plugin.py

@@ -466,6 +466,73 @@ def get_mm_inputs(
         return mm_inputs
 
 
+@dataclass
+class Llama4Plugin(BasePlugin):
+    @override
+    def process_messages(
+        self,
+        messages: list[dict[str, str]],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: Optional["MMProcessor"],
+    ) -> list[dict[str, str]]:
+        self._validate_input(processor, images, videos, audios)
+        if self.expand_mm_tokens:
+            mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+            if "pixel_values" in mm_inputs:
+                image_height, image_width = mm_inputs["pixel_values"][0].shape[-2:]
+                num_patches_per_chunk = int(
+                    (image_height // processor.patch_size)
+                    * (image_width // processor.patch_size)
+                    // processor.downsample_ratio
+                )
+                aspect_ratios = mm_inputs.pop("aspect_ratios")
+
+        num_image_tokens = 0
+        messages = deepcopy(messages)
+        for message in messages:
+            content = message["content"]
+            placeholder_count = content.count(IMAGE_PLACEHOLDER)
+            if self.expand_mm_tokens:
+                prompt_splits = content.split(IMAGE_PLACEHOLDER)
+                new_content = []
+                for local_image_index, split_part in enumerate(prompt_splits):
+                    new_content.append(split_part)
+                    if local_image_index < placeholder_count:
+                        tokens_for_this_image = processor._prompt_split_image(
+                            aspect_ratios[num_image_tokens], num_patches_per_chunk
+                        )
+                        num_image_tokens += 1
+                        new_content.append(tokens_for_this_image)
+
+                content = "".join(new_content)
+
+            message["content"] = content
+
+        if len(images) != num_image_tokens:
+            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
+
+        return messages
+
+    @override
+    def get_mm_inputs(
+        self,
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        imglens: list[int],
+        vidlens: list[int],
+        audlens: list[int],
+        batch_ids: list[list[int]],
+        processor: Optional["MMProcessor"],
+    ) -> dict[str, Union[list[int], "torch.Tensor"]]:
+        self._validate_input(processor, images, videos, audios)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+        mm_inputs.pop("aspect_ratios", None)
+        return mm_inputs
+
+
 @dataclass
 class LlavaPlugin(BasePlugin):
     @override
@@ -1485,6 +1552,7 @@ def process_messages(
 PLUGINS = {
     "base": BasePlugin,
     "gemma3": Gemma3Plugin,
+    "llama4": Llama4Plugin,
     "llava": LlavaPlugin,
     "llava_next": LlavaNextPlugin,
     "llava_next_video": LlavaNextVideoPlugin,

src/llamafactory/data/template.py

@@ -968,6 +968,26 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
 )
 
 
+register_template(
+    name="llama4",
+    format_user=StringFormatter(
+        slots=["<|header_start|>user<|header_end|>\n\n{{content}}<|eot|><|header_start|>assistant<|header_end|>\n\n"]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|eot|>"]),
+    format_system=StringFormatter(slots=["<|header_start|>system<|header_end|>\n\n{{content}}<|eot|>"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|eot|>"], tool_format="llama3"),
+    format_observation=StringFormatter(
+        slots=[
+            "<|header_start|>ipython<|header_end|>\n\n{{content}}<|eot|><|header_start|>assistant<|header_end|>\n\n"
+        ]
+    ),
+    format_tools=ToolFormatter(tool_format="llama3"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|eot|>", "<|eom|>"],
+    mm_plugin=get_mm_plugin(name="llama4", image_token="<|image|>"),
+)
+
+
 # copied from llama3 template
 register_template(
     name="mllama",

src/llamafactory/extras/constants.py

@@ -1111,6 +1111,30 @@ def register_model_group(
 )
 
 
+register_model_group(
+    models={
+        "Llama-4-Scout-17B-16E": {
+            DownloadSource.DEFAULT: "meta-llama/Llama-4-Scout-17B-16E",
+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-4-Scout-17B-16E",
+        },
+        "Llama-4-Scout-17B-16E-Instruct": {
+            DownloadSource.DEFAULT: "meta-llama/Llama-4-Scout-17B-16E-Instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-4-Scout-17B-16E-Instruct",
+        },
+        "Llama-4-Maverick-17B-128E": {
+            DownloadSource.DEFAULT: "meta-llama/Llama-4-Maverick-17B-128E",
+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-4-Maverick-17B-128E",
+        },
+        "Llama-4-Maverick-17B-128E-Instruct": {
+            DownloadSource.DEFAULT: "meta-llama/Llama-4-Maverick-17B-128E-Instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-4-Maverick-17B-128E-Instruct",
+        },
+    },
+    template="llama4",
+    multimodal=True,
+)
+
+
 register_model_group(
     models={
         "LLaVA-1.5-7B-Chat": {

src/llamafactory/extras/misc.py

@@ -89,7 +89,7 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
 
 def check_dependencies() -> None:
     r"""Check the version of the required packages."""
-    check_version("transformers>=4.41.2,<=4.50.0,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
+    check_version("transformers>=4.41.2,<=4.51.0,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
     check_version("datasets>=2.16.0,<=3.4.1")
     check_version("accelerate>=0.34.0,<=1.5.2")
     check_version("peft>=0.14.0,<=0.15.0")

src/llamafactory/model/model_utils/checkpointing.py

@@ -79,7 +79,10 @@ def get_custom_gradient_checkpointing_func(gradient_checkpointing_func: Callable
 
     @wraps(gradient_checkpointing_func, assigned=WRAPPER_ASSIGNMENTS + ("__self__",))
     def custom_gradient_checkpointing_func(func: Callable, *args: Union["torch.Tensor", Any], **kwargs):
-        module: torch.nn.Module = func.__self__
+        if isinstance(func, partial):
+            module: torch.nn.Module = func.func.__self__
+        else:
+            module: torch.nn.Module = func.__self__
 
         has_grad = False
         if any(param.requires_grad for param in module.parameters()):

src/llamafactory/model/model_utils/visual.py

@@ -203,6 +203,12 @@ def patch_target_modules(
 )
 
 
+_register_composite_model(
+    model_type="llama4",
+    vision_model_keys=["vision_model"],
+)
+
+
 _register_composite_model(
     model_type="llava",
 )

src/llamafactory/train/ppo/trainer.py

@@ -243,7 +243,7 @@ def ppo_train(self, resume_from_checkpoint: Optional[str] = None) -> None:
             for idx in range(0, self.config.batch_size, self.config.mini_batch_size):
                 mini_batch = {
                     "input_ids": batch["input_ids"][idx : idx + self.config.mini_batch_size],
-                    "attention_mask": batch["attention_mask"][idx : idx + self.config.mini_batch_size]
+                    "attention_mask": batch["attention_mask"][idx : idx + self.config.mini_batch_size],
                 }
                 mini_batch_queries, mini_batch_responses = self.get_inputs(mini_batch)
                 mini_batch_rewards = self.get_rewards(mini_batch_queries, mini_batch_responses)

tests/version.txt

@@ -1 +1,2 @@
-0.9.3.100
+# change if test fails
+0.9.3.101