diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 84920f4b..414cfe1a 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -40,6 +40,9 @@ jobs:
- python: "3.9"
os: "ubuntu-latest"
transformers: "4.49.0"
+ - python: "3.9"
+ os: "ubuntu-latest"
+ transformers: "4.51.0"
runs-on: ${{ matrix.os }}
@@ -72,6 +75,11 @@ jobs:
run: |
python -m pip install "transformers==${{ matrix.transformers }}"
+ - name: Downgrade transformers
+ if: ${{ matrix.os == 'macos-13' }}
+ run: |
+ python -m pip install "transformers<4.52.0"
+
- name: Cache files
id: hf-hub-cache
uses: actions/cache@v4
diff --git a/README.md b/README.md
index c02b7750..2d6eeef7 100644
--- a/README.md
+++ b/README.md
@@ -266,7 +266,7 @@ Choose your path:
| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
-| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)\* | 1B/2B/8B/14B/38B/78B | intern_vl |
+| [InternVL 2.5-3](https://huggingface.co/OpenGVLab) | 1B/2B/8B/14B/38B/78B | intern_vl |
| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
@@ -292,7 +292,7 @@ Choose your path:
| [Qwen (1-2.5) (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
| [Qwen3 (MoE)](https://huggingface.co/Qwen) | 0.6B/1.7B/4B/8B/14B/32B/235B | qwen3 |
| [Qwen2-Audio](https://huggingface.co/Qwen) | 7B | qwen2_audio |
-| [Qwen2.5-Omni](https://huggingface.co/Qwen)\* | 3B/7B | qwen2_omni |
+| [Qwen2.5-Omni](https://huggingface.co/Qwen) | 3B/7B | qwen2_omni |
| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen) | 2B/3B/7B/32B/72B | qwen2_vl |
| [Seed Coder](https://huggingface.co/ByteDance-Seed) | 8B | seed_coder |
| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |
@@ -439,6 +439,7 @@ huggingface-cli login
| ------------ | ------- | --------- |
| python | 3.9 | 3.10 |
| torch | 2.0.0 | 2.6.0 |
+| torchvision | 0.15.0 | 0.21.0 |
| transformers | 4.45.0 | 4.50.0 |
| datasets | 2.16.0 | 3.2.0 |
| accelerate | 0.34.0 | 1.2.1 |
diff --git a/README_zh.md b/README_zh.md
index f9029948..31c77b75 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -268,7 +268,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
-| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)\* | 1B/2B/8B/14B/38B/78B | intern_vl |
+| [InternVL 2.5-3](https://huggingface.co/OpenGVLab) | 1B/2B/8B/14B/38B/78B | intern_vl |
| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
@@ -294,7 +294,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
| [Qwen (1-2.5) (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
| [Qwen3 (MoE)](https://huggingface.co/Qwen) | 0.6B/1.7B/4B/8B/14B/32B/235B | qwen3 |
| [Qwen2-Audio](https://huggingface.co/Qwen) | 7B | qwen2_audio |
-| [Qwen2.5-Omni](https://huggingface.co/Qwen)\* | 3B/7B | qwen2_omni |
+| [Qwen2.5-Omni](https://huggingface.co/Qwen) | 3B/7B | qwen2_omni |
| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen) | 2B/3B/7B/32B/72B | qwen2_vl |
| [Seed Coder](https://huggingface.co/ByteDance-Seed) | 8B | seed_coder |
| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |
@@ -441,6 +441,7 @@ huggingface-cli login
| ------------ | ------- | --------- |
| python | 3.9 | 3.10 |
| torch | 2.0.0 | 2.6.0 |
+| torchvision | 0.15.0 | 0.21.0 |
| transformers | 4.45.0 | 4.50.0 |
| datasets | 2.16.0 | 3.2.0 |
| accelerate | 0.34.0 | 1.2.1 |
diff --git a/data/README.md b/data/README.md
index 90503351..2ed3782a 100644
--- a/data/README.md
+++ b/data/README.md
@@ -89,7 +89,9 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
```
> [!TIP]
-> If the model has reasoning capabilities but the dataset does not contain chain-of-thought (CoT), LLaMA-Factory will automatically add empty CoT to the data. When `enable_thinking` is `True`, the empty CoT will be added to the model responses and loss computation will be considered; otherwise, it will be added to the user prompts and loss computation will be ignored. Please keep the `enable_thinking` parameter consistent during training and inference.
+> If the model has reasoning capabilities but the dataset does not contain chain-of-thought (CoT), LLaMA-Factory will automatically add empty CoT to the data. When `enable_thinking` is `True` (slow thinking), the empty CoT will be added to the model responses and loss computation will be considered; otherwise (fast thinking), it will be added to the user prompts and loss computation will be ignored. Please keep the `enable_thinking` parameter consistent during training and inference.
+>
+> If you want to train data containing CoT with slow thinking and data without CoT with fast thinking, you can set `enable_thinking` to `None`. However, this feature is relatively complicated and should be used with caution.
### Pre-training Dataset
diff --git a/data/README_zh.md b/data/README_zh.md
index f26725ca..e63eb73c 100644
--- a/data/README_zh.md
+++ b/data/README_zh.md
@@ -88,7 +88,9 @@
```
> [!TIP]
-> 如果模型本身具备推理能力，而数据集不包含思维链，LLaMA-Factory 会自动为数据添加空思维链。当 `enable_thinking` 为 `True` 时，空思维链会添加到模型回答中并且计算损失，否则会添加到用户指令中并且不计算损失。请在训练和推理时保持 `enable_thinking` 参数一致。
+> 如果模型本身具备推理能力，而数据集不包含思维链，LLaMA-Factory 会自动为数据添加空思维链。当 `enable_thinking` 为 `True` 时（慢思考），空思维链会添加到模型回答中并且计算损失，否则会添加到用户指令中并且不计算损失（快思考）。请在训练和推理时保持 `enable_thinking` 参数一致。
+>
+> 如果您希望训练包含思维链的数据时使用慢思考，训练不包含思维链的数据时使用快思考，可以设置 `enable_thinking` 为 `None`。但该功能较为复杂，请谨慎使用。
### 预训练数据集
diff --git a/requirements.txt b/requirements.txt
index 484ebae0..1faf1c07 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-transformers>=4.45.0,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
+transformers>=4.45.0,<=4.52.1,!=4.46.*,!=4.47.*,!=4.48.0,!=4.52.0
datasets>=2.16.0,<=3.6.0
accelerate>=0.34.0,<=1.7.0
peft>=0.14.0,<=0.15.2
diff --git a/setup.py b/setup.py
index 4c29ca87..6066a957 100644
--- a/setup.py
+++ b/setup.py
@@ -42,7 +42,7 @@ def get_console_scripts() -> list[str]:
extra_require = {
- "torch": ["torch>=1.13.1"],
+ "torch": ["torch>=2.0.0", "torchvision>=0.15.0"],
"torch-npu": ["torch==2.4.0", "torch-npu==2.4.0.post2", "decorator"],
"metrics": ["nltk", "jieba", "rouge-chinese"],
"deepspeed": ["deepspeed>=0.10.0,<=0.16.5"],
diff --git a/src/llamafactory/data/mm_plugin.py b/src/llamafactory/data/mm_plugin.py
index a237b907..f8598212 100644
--- a/src/llamafactory/data/mm_plugin.py
+++ b/src/llamafactory/data/mm_plugin.py
@@ -57,19 +57,11 @@ if is_transformers_version_greater_than("4.45.0"):
)
-if is_transformers_version_greater_than("4.49.0"):
- try:
- from transformers.image_utils import make_batched_videos, make_flat_list_of_images
- except ImportError:
- try:
- # If that fails, try importing from the new location
- from transformers.image_utils import make_flat_list_of_images
- from transformers.video_utils import make_batched_videos
- except ImportError:
- raise ImportError(
- "Could not import make_batched_videos and make_flat_list_of_images. "
- "In Transformers 4.52.0, make_batched_videos will be moved to transformers.video_utils."
- )
+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
+elif is_transformers_version_greater_than("4.49.0"):
+ from transformers.image_utils import make_batched_videos, make_flat_list_of_images
if TYPE_CHECKING:
diff --git a/src/llamafactory/data/template.py b/src/llamafactory/data/template.py
index aa422cab..74faea46 100644
--- a/src/llamafactory/data/template.py
+++ b/src/llamafactory/data/template.py
@@ -52,7 +52,7 @@ class Template:
efficient_eos: bool
replace_eos: bool
replace_jinja_template: bool
- enable_thinking: bool
+ enable_thinking: Optional[bool]
mm_plugin: "BasePlugin"
def encode_oneturn(
@@ -411,14 +411,17 @@ class ReasoningTemplate(Template):
for i in range(1, len(messages) - 2, 2):
messages[i]["content"] = self.remove_thought(messages[i]["content"])
+ if self.enable_thinking is False: # remove all cot
+ messages[-1]["content"] = self.remove_thought(messages[-1]["content"])
+
prompt_ids, response_ids = super().encode_oneturn(tokenizer, messages, system, tools)
if (
self.thought_words[0] not in messages[-1]["content"]
and self.thought_words[1] not in messages[-1]["content"]
- ):
- if not self.enable_thinking:
- prompt_ids = prompt_ids + self.get_thought_word_ids(tokenizer)
- else:
+ ): # add empty cot
+ if not self.enable_thinking: # do not compute loss
+ prompt_ids += self.get_thought_word_ids(tokenizer)
+ else: # do compute loss
response_ids = self.get_thought_word_ids(tokenizer) + response_ids
return prompt_ids, response_ids
@@ -431,15 +434,20 @@ class ReasoningTemplate(Template):
system: Optional[str] = None,
tools: Optional[str] = None,
) -> list[tuple[list[int], list[int]]]:
+ messages = deepcopy(messages)
+ if self.enable_thinking is False: # remove all cot
+ for i in range(1, len(messages), 2):
+ messages[i]["content"] = self.remove_thought(messages[i]["content"])
+
encoded_messages = self._encode(tokenizer, messages, system, tools)
for i in range(0, len(messages), 2):
if (
self.thought_words[0] not in messages[i + 1]["content"]
and self.thought_words[1] not in messages[i + 1]["content"]
- ):
- if not self.enable_thinking:
+ ): # add empty cot
+ if not self.enable_thinking: # do not compute loss
encoded_messages[i] += self.get_thought_word_ids(tokenizer)
- else:
+ else: # do compute loss
encoded_messages[i + 1] = self.get_thought_word_ids(tokenizer) + encoded_messages[i + 1]
return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]
@@ -463,7 +471,7 @@ def register_template(
efficient_eos: bool = False,
replace_eos: bool = False,
replace_jinja_template: bool = False,
- enable_thinking: bool = True,
+ enable_thinking: Optional[bool] = True,
mm_plugin: "BasePlugin" = get_mm_plugin(name="base"),
template_class: type["Template"] = Template,
) -> None:
diff --git a/src/llamafactory/extras/constants.py b/src/llamafactory/extras/constants.py
index a461aeee..5dc7c3f3 100644
--- a/src/llamafactory/extras/constants.py
+++ b/src/llamafactory/extras/constants.py
@@ -2566,6 +2566,14 @@ register_model_group(
DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-7B",
DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-7B",
},
+ "Qwen2.5-Omni-7B-GPTQ-Int4": {
+ DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-7B-GPTQ-Int4",
+ DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-7B-GPTQ-Int4",
+ },
+ "Qwen2.5-Omni-7B-AWQ": {
+ DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-7B-AWQ",
+ DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-7B-AWQ",
+ },
},
template="qwen2_omni",
multimodal=True,
diff --git a/src/llamafactory/extras/misc.py b/src/llamafactory/extras/misc.py
index dcc22c1b..1ad4c1cf 100644
--- a/src/llamafactory/extras/misc.py
+++ b/src/llamafactory/extras/misc.py
@@ -94,7 +94,9 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
def check_dependencies() -> None:
r"""Check the version of the required packages."""
- check_version("transformers>=4.45.0,<=4.51.3,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
+ check_version(
+ "transformers>=4.45.0,<=4.52.1,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0,!=4.52.0"
+ )
check_version("datasets>=2.16.0,<=3.6.0")
check_version("accelerate>=0.34.0,<=1.7.0")
check_version("peft>=0.14.0,<=0.15.2")
diff --git a/src/llamafactory/hparams/data_args.py b/src/llamafactory/hparams/data_args.py
index 588b8c5c..c84fb2f7 100644
--- a/src/llamafactory/hparams/data_args.py
+++ b/src/llamafactory/hparams/data_args.py
@@ -119,7 +119,7 @@ class DataArguments:
default=None,
metadata={"help": "Override the default system message in the template."},
)
- enable_thinking: bool = field(
+ enable_thinking: Optional[bool] = field(
default=True,
metadata={"help": "Whether or not to enable thinking mode for reasoning models."},
)
diff --git a/src/llamafactory/hparams/model_args.py b/src/llamafactory/hparams/model_args.py
index eec9ceca..d2f7cc52 100644
--- a/src/llamafactory/hparams/model_args.py
+++ b/src/llamafactory/hparams/model_args.py
@@ -235,10 +235,6 @@ class ProcessorArguments:
default=False,
metadata={"help": "Whether to crop the image to patches for internvl."},
)
- use_audio_in_video: bool = field(
- default=False,
- metadata={"help": "Whether or not to use audio in video inputs."},
- )
video_max_pixels: int = field(
default=256 * 256,
metadata={"help": "The maximum number of pixels of video inputs."},
@@ -255,6 +251,10 @@ class ProcessorArguments:
default=128,
metadata={"help": "The maximum number of sampled frames for video inputs."},
)
+ use_audio_in_video: bool = field(
+ default=False,
+ metadata={"help": "Whether or not to use audio in video inputs."},
+ )
audio_sampling_rate: int = field(
default=16000,
metadata={"help": "The sampling rate of audio inputs."},
diff --git a/src/llamafactory/model/model_utils/visual.py b/src/llamafactory/model/model_utils/visual.py
index 901010c7..247de48b 100644
--- a/src/llamafactory/model/model_utils/visual.py
+++ b/src/llamafactory/model/model_utils/visual.py
@@ -24,6 +24,7 @@ import transformers.models
from transformers.activations import ACT2FN
from ...extras import logging
+from ...extras.packages import is_transformers_version_greater_than
if TYPE_CHECKING:
@@ -281,7 +282,7 @@ _register_composite_model(
model_type="qwen2_vl",
projector_key="visual.merger",
vision_model_keys=["visual.patch_embed", "visual.blocks"],
- language_model_keys=["model", "lm_head"],
+ language_model_keys=["language_model"] if is_transformers_version_greater_than("4.52.0") else ["model", "lm_head"],
lora_conflict_keys=["patch_embed"],
)
@@ -290,6 +291,6 @@ _register_composite_model(
model_type="qwen2_5_vl",
projector_key="visual.merger",
vision_model_keys=["visual.patch_embed", "visual.blocks"],
- language_model_keys=["model", "lm_head"],
+ language_model_keys=["language_model"] if is_transformers_version_greater_than("4.52.0") else ["model", "lm_head"],
lora_conflict_keys=["patch_embed"],
)
diff --git a/src/llamafactory/model/patcher.py b/src/llamafactory/model/patcher.py
index cedcf9da..20228812 100644
--- a/src/llamafactory/model/patcher.py
+++ b/src/llamafactory/model/patcher.py
@@ -85,8 +85,8 @@ def patch_processor(
setattr(processor, "video_min_pixels", model_args.video_min_pixels)
setattr(processor, "video_fps", model_args.video_fps)
setattr(processor, "video_maxlen", model_args.video_maxlen)
- setattr(processor, "audio_sampling_rate", model_args.audio_sampling_rate)
setattr(processor, "use_audio_in_video", model_args.use_audio_in_video)
+ setattr(processor, "audio_sampling_rate", model_args.audio_sampling_rate)
def patch_config(
diff --git a/src/llamafactory/train/dpo/trainer.py b/src/llamafactory/train/dpo/trainer.py
index 80f67c6c..2539127c 100644
--- a/src/llamafactory/train/dpo/trainer.py
+++ b/src/llamafactory/train/dpo/trainer.py
@@ -121,11 +121,11 @@ class CustomDPOTrainer(DPOTrainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
- def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
+ def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
- return super()._get_train_sampler()
+ return super()._get_train_sampler(*args, **kwargs)
@override
def get_batch_samples(self, *args, **kwargs):
diff --git a/src/llamafactory/train/kto/trainer.py b/src/llamafactory/train/kto/trainer.py
index 0323041f..f67d0ece 100644
--- a/src/llamafactory/train/kto/trainer.py
+++ b/src/llamafactory/train/kto/trainer.py
@@ -34,7 +34,6 @@ from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, ge
if TYPE_CHECKING:
- import torch.utils.data
from transformers import PreTrainedModel, ProcessorMixin
from ...hparams import FinetuningArguments
@@ -119,12 +118,12 @@ class CustomKTOTrainer(KTOTrainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
- def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
+ def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
r"""Replace the sequential sampler of KTO Trainer created by trl with the random sampler."""
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
- return Trainer._get_train_sampler(self)
+ return Trainer._get_train_sampler(self, *args, **kwargs)
@override
def get_batch_samples(self, *args, **kwargs):
diff --git a/src/llamafactory/train/pt/trainer.py b/src/llamafactory/train/pt/trainer.py
index 8495bbb2..096cbf68 100644
--- a/src/llamafactory/train/pt/trainer.py
+++ b/src/llamafactory/train/pt/trainer.py
@@ -70,11 +70,11 @@ class CustomTrainer(Trainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
- def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
+ def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
- return super()._get_train_sampler()
+ return super()._get_train_sampler(*args, **kwargs)
@override
def compute_loss(self, model, inputs, *args, **kwargs):
diff --git a/src/llamafactory/train/rm/trainer.py b/src/llamafactory/train/rm/trainer.py
index 8c14b0ab..fe2bd557 100644
--- a/src/llamafactory/train/rm/trainer.py
+++ b/src/llamafactory/train/rm/trainer.py
@@ -78,11 +78,11 @@ class PairwiseTrainer(Trainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
- def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
+ def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
- return super()._get_train_sampler()
+ return super()._get_train_sampler(*args, **kwargs)
@override
def compute_loss(
diff --git a/src/llamafactory/train/sft/trainer.py b/src/llamafactory/train/sft/trainer.py
index fece1bd9..d0b8d05b 100644
--- a/src/llamafactory/train/sft/trainer.py
+++ b/src/llamafactory/train/sft/trainer.py
@@ -92,11 +92,11 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
- def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
+ def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
- return super()._get_train_sampler()
+ return super()._get_train_sampler(*args, **kwargs)
@override
def compute_loss(self, model, inputs, *args, **kwargs):
diff --git a/src/llamafactory/webui/common.py b/src/llamafactory/webui/common.py
index 2387174a..887f2517 100644
--- a/src/llamafactory/webui/common.py
+++ b/src/llamafactory/webui/common.py
@@ -205,6 +205,14 @@ def load_eval_results(path: os.PathLike) -> str:
return f"```json\n{result}\n```\n"
+def calculate_pixels(pixels: str) -> int:
+ r"""Calculate the number of pixels from the expression."""
+ if "*" in pixels:
+ return int(pixels.split("*")[0]) * int(pixels.split("*")[1])
+ else:
+ return int(pixels)
+
+
def create_ds_config() -> None:
r"""Create deepspeed config in the current directory."""
os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
diff --git a/src/llamafactory/webui/components/train.py b/src/llamafactory/webui/components/train.py
index 7ca99647..8b7aa6e9 100644
--- a/src/llamafactory/webui/components/train.py
+++ b/src/llamafactory/webui/components/train.py
@@ -106,11 +106,11 @@ def create_train_tab(engine: "Engine") -> dict[str, "Component"]:
use_llama_pro = gr.Checkbox()
with gr.Column():
+ enable_thinking = gr.Checkbox(value=True)
report_to = gr.Dropdown(
- choices=["none", "all", "wandb", "mlflow", "neptune", "tensorboard"],
- value=["none"],
+ choices=["none", "wandb", "mlflow", "neptune", "tensorboard", "all"],
+ value="none",
allow_custom_value=True,
- multiselect=True,
)
input_elems.update(
@@ -126,6 +126,7 @@ def create_train_tab(engine: "Engine") -> dict[str, "Component"]:
mask_history,
resize_vocab,
use_llama_pro,
+ enable_thinking,
report_to,
}
)
@@ -143,6 +144,7 @@ def create_train_tab(engine: "Engine") -> dict[str, "Component"]:
mask_history=mask_history,
resize_vocab=resize_vocab,
use_llama_pro=use_llama_pro,
+ enable_thinking=enable_thinking,
report_to=report_to,
)
)
@@ -231,6 +233,42 @@ def create_train_tab(engine: "Engine") -> dict[str, "Component"]:
)
)
+ with gr.Accordion(open=False) as mm_tab:
+ with gr.Row():
+ freeze_vision_tower = gr.Checkbox(value=True)
+ freeze_multi_modal_projector = gr.Checkbox(value=True)
+ freeze_language_model = gr.Checkbox(value=False)
+
+ with gr.Row():
+ image_max_pixels = gr.Textbox(value="768*768")
+ image_min_pixels = gr.Textbox(value="32*32")
+ video_max_pixels = gr.Textbox(value="256*256")
+ video_min_pixels = gr.Textbox(value="16*16")
+
+ input_elems.update(
+ {
+ freeze_vision_tower,
+ freeze_multi_modal_projector,
+ freeze_language_model,
+ image_max_pixels,
+ image_min_pixels,
+ video_max_pixels,
+ video_min_pixels,
+ }
+ )
+ elem_dict.update(
+ dict(
+ mm_tab=mm_tab,
+ freeze_vision_tower=freeze_vision_tower,
+ freeze_multi_modal_projector=freeze_multi_modal_projector,
+ freeze_language_model=freeze_language_model,
+ image_max_pixels=image_max_pixels,
+ image_min_pixels=image_min_pixels,
+ video_max_pixels=video_max_pixels,
+ video_min_pixels=video_min_pixels,
+ )
+ )
+
with gr.Accordion(open=False) as galore_tab:
with gr.Row():
use_galore = gr.Checkbox()
diff --git a/src/llamafactory/webui/locales.py b/src/llamafactory/webui/locales.py
index ed05bae7..17ffb0a4 100644
--- a/src/llamafactory/webui/locales.py
+++ b/src/llamafactory/webui/locales.py
@@ -871,6 +871,28 @@ LOCALES = {
"info": "拡張ブロックのパラメータのみをトレーニングします。",
},
},
+ "enable_thinking": {
+ "en": {
+ "label": "Enable thinking",
+ "info": "Whether or not to enable thinking mode for reasoning models.",
+ },
+ "ru": {
+ "label": "Включить мысли",
+ "info": "Включить режим мысли для моделей решающего характера.",
+ },
+ "zh": {
+ "label": "启用思考模式",
+ "info": "是否启用推理模型的思考模式。",
+ },
+ "ko": {
+ "label": "생각 모드 활성화",
+ "info": "추론 모델의 생각 모드를 활성화할지 여부.",
+ },
+ "ja": {
+ "label": "思考モードを有効化",
+ "info": "推論モデルの思考モードを有効にするかどうか。",
+ },
+ },
"report_to": {
"en": {
"label": "Enable external logger",
@@ -1374,6 +1396,177 @@ LOCALES = {
"info": "PPO トレーニングにおいて報酬スコアをホワイトニング処理します。",
},
},
+ "mm_tab": {
+ "en": {
+ "label": "Multimodal configurations",
+ },
+ "ru": {
+ "label": "Конфигурации мультимедиа",
+ },
+ "zh": {
+ "label": "多模态参数设置",
+ },
+ "ko": {
+ "label": "멀티모달 구성",
+ },
+ "ja": {
+ "label": "多モーダル設定",
+ },
+ },
+ "freeze_vision_tower": {
+ "en": {
+ "label": "Freeze vision tower",
+ "info": "Freeze the vision tower in the model.",
+ },
+ "ru": {
+ "label": "Заморозить башню визиона",
+ "info": "Заморозить башню визиона в модели.",
+ },
+ "zh": {
+ "label": "冻结视觉编码器",
+ "info": "冻结模型中的视觉编码器。",
+ },
+ "ko": {
+ "label": "비전 타워 고정",
+ "info": "모델의 비전 타워를 고정합니다.",
+ },
+ "ja": {
+ "label": "ビジョンタワーの固定",
+ "info": "モデルのビジョンタワーを固定します。",
+ },
+ },
+ "freeze_multi_modal_projector": {
+ "en": {
+ "label": "Freeze multi-modal projector",
+ "info": "Freeze the multi-modal projector in the model.",
+ },
+ "ru": {
+ "label": "Заморозить мультимодальный проектор",
+ "info": "Заморозить мультимодальный проектор в модели.",
+ },
+ "zh": {
+ "label": "冻结多模态投影器",
+ "info": "冻结模型中的多模态投影器。",
+ },
+ "ko": {
+ "label": "멀티모달 프로젝터 고정",
+ "info": "모델의 멀티모달 프로젝터를 고정합니다.",
+ },
+ "ja": {
+ "label": "多モーダルプロジェクターの固定",
+ "info": "モデルの多モーダルプロジェクターを固定します。",
+ },
+ },
+ "freeze_language_model": {
+ "en": {
+ "label": "Freeze language model",
+ "info": "Freeze the language model in the model.",
+ },
+ "ru": {
+ "label": "Заморозить язык модели",
+ "info": "Заморозить язык модели в модели.",
+ },
+ "zh": {
+ "label": "冻结语言模型",
+ "info": "冻结模型中的语言模型。",
+ },
+ "ko": {
+ "label": "언어 모델 고정",
+ "info": "모델의 언어 모델을 고정합니다.",
+ },
+ "ja": {
+ "label": "言語モデルの固定",
+ "info": "モデルの言語モデルを固定します。",
+ },
+ },
+ "image_max_pixels": {
+ "en": {
+ "label": "Image max pixels",
+ "info": "The maximum number of pixels of image inputs.",
+ },
+ "ru": {
+ "label": "Максимальное количество пикселей изображения",
+ "info": "Максимальное количество пикселей изображения.",
+ },
+ "zh": {
+ "label": "图像最大像素",
+ "info": "输入图像的最大像素数。",
+ },
+ "ko": {
+ "label": "이미지 최대 픽셀",
+ "info": "이미지 입력의 최대 픽셀 수입니다.",
+ },
+ "ja": {
+ "label": "画像最大ピクセル",
+ "info": "画像入力の最大ピクセル数です。",
+ },
+ },
+ "image_min_pixels": {
+ "en": {
+ "label": "Image min pixels",
+ "info": "The minimum number of pixels of image inputs.",
+ },
+ "ru": {
+ "label": "Минимальное количество пикселей изображения",
+ "info": "Минимальное количество пикселей изображения.",
+ },
+ "zh": {
+ "label": "图像最小像素",
+ "info": "输入图像的最小像素数。",
+ },
+ "ko": {
+ "label": "이미지 최소 픽셀",
+ "info": "이미지 입력의 최소 픽셀 수입니다.",
+ },
+ "ja": {
+ "label": "画像最小ピクセル",
+ "info": "画像入力の最小ピクセル数です。",
+ },
+ },
+ "video_max_pixels": {
+ "en": {
+ "label": "Video max pixels",
+ "info": "The maximum number of pixels of video inputs.",
+ },
+ "ru": {
+ "label": "Максимальное количество пикселей видео",
+ "info": "Максимальное количество пикселей видео.",
+ },
+ "zh": {
+ "label": "视频最大像素",
+ "info": "输入视频的最大像素数。",
+ },
+ "ko": {
+ "label": "비디오 최대 픽셀",
+ "info": "비디오 입력의 최대 픽셀 수입니다.",
+ },
+ "ja": {
+ "label": "ビデオ最大ピクセル",
+ "info": "ビデオ入力の最大ピクセル数です。",
+ },
+ },
+ "video_min_pixels": {
+ "en": {
+ "label": "Video min pixels",
+ "info": "The minimum number of pixels of video inputs.",
+ },
+ "ru": {
+ "label": "Минимальное количество пикселей видео",
+ "info": "Минимальное количество пикселей видео.",
+ },
+ "zh": {
+ "label": "视频最小像素",
+ "info": "输入视频的最小像素数。",
+ },
+ "ko": {
+ "label": "비디오 최소 픽셀",
+ "info": "비디오 입력의 최소 픽셀 수입니다.",
+ },
+ "ja": {
+ "label": "ビデオ最小ピクセル",
+ "info": "ビデオ入力の最小ピクセル数です。",
+ },
+ },
"galore_tab": {
"en": {
"label": "GaLore configurations",
@@ -2468,23 +2661,6 @@ LOCALES = {
"label": "HTML タグをエスケープ",
},
},
- "enable_thinking": {
- "en": {
- "label": "Enable thinking",
- },
- "ru": {
- "label": "Включить мышление",
- },
- "zh": {
- "label": "启用思考",
- },
- "ko": {
- "label": "사고를 활성화하다",
- },
- "ja": {
- "label": "思考を可能にする",
- },
- },
"clear_btn": {
"en": {
"value": "Clear history",
diff --git a/src/llamafactory/webui/runner.py b/src/llamafactory/webui/runner.py
index 4fbebde1..3715974a 100644
--- a/src/llamafactory/webui/runner.py
+++ b/src/llamafactory/webui/runner.py
@@ -29,6 +29,7 @@ from .common import (
DEFAULT_CACHE_DIR,
DEFAULT_CONFIG_DIR,
abort_process,
+ calculate_pixels,
gen_cmd,
get_save_dir,
load_args,
@@ -162,7 +163,15 @@ class Runner:
mask_history=get("train.mask_history"),
resize_vocab=get("train.resize_vocab"),
use_llama_pro=get("train.use_llama_pro"),
+ enable_thinking=get("train.enable_thinking"),
report_to=get("train.report_to"),
+ freeze_vision_tower=get("train.freeze_vision_tower"),
+ freeze_multi_modal_projector=get("train.freeze_multi_modal_projector"),
+ freeze_language_model=get("train.freeze_language_model"),
+ image_max_pixels=calculate_pixels(get("train.image_max_pixels")),
+ image_min_pixels=calculate_pixels(get("train.image_min_pixels")),
+ video_max_pixels=calculate_pixels(get("train.video_max_pixels")),
+ video_min_pixels=calculate_pixels(get("train.video_min_pixels")),
use_galore=get("train.use_galore"),
use_apollo=get("train.use_apollo"),
use_badam=get("train.use_badam"),
@@ -256,12 +265,6 @@ class Runner:
args["badam_switch_interval"] = get("train.badam_switch_interval")
args["badam_update_ratio"] = get("train.badam_update_ratio")
- # report_to
- if "none" in args["report_to"]:
- args["report_to"] = "none"
- elif "all" in args["report_to"]:
- args["report_to"] = "all"
-
# swanlab config
if get("train.use_swanlab"):
args["swanlab_project"] = get("train.swanlab_project")
diff --git a/tests/data/test_mm_plugin.py b/tests/data/test_mm_plugin.py
index a19de12a..6910cbad 100644
--- a/tests/data/test_mm_plugin.py
+++ b/tests/data/test_mm_plugin.py
@@ -135,8 +135,7 @@ def _check_plugin(
expected_mm_inputs: dict[str, Any] = {},
expected_no_mm_inputs: dict[str, Any] = {},
) -> None:
- # test omni_messages
- if plugin.__class__.__name__ == "Qwen2OmniPlugin":
+ if plugin.__class__.__name__ == "Qwen2OmniPlugin": # test omni_messages
assert plugin.process_messages(OMNI_MESSAGES, IMAGES, NO_VIDEOS, AUDIOS, processor) == expected_mm_messages
assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, AUDIOS, tokenizer, processor) == (
expected_input_ids,
@@ -146,8 +145,7 @@ def _check_plugin(
plugin.get_mm_inputs(IMAGES, NO_VIDEOS, AUDIOS, IMGLENS, NO_VIDLENS, AUDLENS, BATCH_IDS, processor),
expected_mm_inputs,
)
- # test mm_messages
- if plugin.__class__.__name__ != "BasePlugin":
+ elif plugin.__class__.__name__ != "BasePlugin": # test mm_messages
assert plugin.process_messages(MM_MESSAGES, IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == expected_mm_messages
assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
expected_input_ids,
@@ -201,7 +199,7 @@ def test_gemma3_plugin():
_check_plugin(**check_inputs)
-@pytest.mark.xfail(reason="Unknown error.")
+@pytest.mark.skipif(not is_transformers_version_greater_than("4.52.0"), reason="Requires transformers>=4.52.0")
def test_internvl_plugin():
image_seqlen = 256
tokenizer_module = _load_tokenizer_module(model_name_or_path="OpenGVLab/InternVL3-1B-hf")
@@ -219,7 +217,7 @@ def test_internvl_plugin():
_check_plugin(**check_inputs)
-@pytest.mark.xfail(reason="Unknown error.")
+@pytest.mark.skipif(not is_transformers_version_greater_than("4.51.0"), reason="Requires transformers>=4.51.0")
def test_llama4_plugin():
tokenizer_module = _load_tokenizer_module(model_name_or_path=TINY_LLAMA4)
processor = tokenizer_module["processor"]
@@ -321,10 +319,9 @@ def test_pixtral_plugin():
_check_plugin(**check_inputs)
-@pytest.mark.xfail(reason="Unknown error.")
+@pytest.mark.skipif(not is_transformers_version_greater_than("4.52.0"), reason="Requires transformers>=4.52.0")
def test_qwen2_omni_plugin():
- image_seqlen = 4
- audio_seqlen = 2
+ image_seqlen, audio_seqlen = 4, 2
tokenizer_module = _load_tokenizer_module(model_name_or_path="Qwen/Qwen2.5-Omni-7B")
qwen2_omni_plugin = get_mm_plugin(
name="qwen2_omni", audio_token="<|AUDIO|>", image_token="<|IMAGE|>", video_token="<|VIDEO|>"
diff --git a/tests/data/test_template.py b/tests/data/test_template.py
index eef52efe..1f04e2ee 100644
--- a/tests/data/test_template.py
+++ b/tests/data/test_template.py
@@ -127,20 +127,21 @@ def test_encode_multiturn(use_fast: bool):
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.parametrize("cot_messages", [True, False])
-@pytest.mark.parametrize("enable_thinking", [True, False])
+@pytest.mark.parametrize("enable_thinking", [True, False, None])
def test_reasoning_encode_oneturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
- messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
+ input_messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
template = get_template_and_fix_tokenizer(tokenizer, data_args)
- prompt_ids, answer_ids = template.encode_oneturn(tokenizer, messages)
+ prompt_ids, answer_ids = template.encode_oneturn(tokenizer, input_messages)
+ output_messages = MESSAGES if enable_thinking is False else input_messages
prompt_str = (
- f"<|im_start|>user\n{messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
+ f"<|im_start|>user\n{output_messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
f"{MESSAGES[1]['content']}<|im_end|>\n"
- f"<|im_start|>user\n{messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
+ f"<|im_start|>user\n{output_messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
)
- answer_str = f"{messages[3]['content']}<|im_end|>\n"
- if not cot_messages:
+ answer_str = f"{output_messages[3]['content']}<|im_end|>\n"
+ if not cot_messages or enable_thinking is False:
if enable_thinking:
answer_str = "\n\n\n\n" + answer_str
else:
@@ -151,18 +152,19 @@ def test_reasoning_encode_oneturn(use_fast: bool, cot_messages: bool, enable_thi
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.parametrize("cot_messages", [True, False])
-@pytest.mark.parametrize("enable_thinking", [True, False])
+@pytest.mark.parametrize("enable_thinking", [True, False, None])
def test_reasoning_encode_multiturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
- messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
+ input_messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
template = get_template_and_fix_tokenizer(tokenizer, data_args)
- encoded_pairs = template.encode_multiturn(tokenizer, messages)
- prompt_str_1 = f"<|im_start|>user\n{messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
- answer_str_1 = f"{messages[1]['content']}<|im_end|>\n"
- prompt_str_2 = f"<|im_start|>user\n{messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
- answer_str_2 = f"{messages[3]['content']}<|im_end|>\n"
- if not cot_messages:
+ encoded_pairs = template.encode_multiturn(tokenizer, input_messages)
+ output_messages = MESSAGES if enable_thinking is False else input_messages
+ prompt_str_1 = f"<|im_start|>user\n{output_messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
+ answer_str_1 = f"{output_messages[1]['content']}<|im_end|>\n"
+ prompt_str_2 = f"<|im_start|>user\n{output_messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
+ answer_str_2 = f"{output_messages[3]['content']}<|im_end|>\n"
+ if not cot_messages or enable_thinking is False:
if enable_thinking:
answer_str_1 = "\n\n\n\n" + answer_str_1
answer_str_2 = "\n\n\n\n" + answer_str_2
diff --git a/tests/model/model_utils/test_visual.py b/tests/model/model_utils/test_visual.py
index 44abe349..4bba46d6 100644
--- a/tests/model/model_utils/test_visual.py
+++ b/tests/model/model_utils/test_visual.py
@@ -16,6 +16,7 @@ import pytest
import torch
from transformers import AutoConfig, AutoModelForVision2Seq
+from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import FinetuningArguments, ModelArguments
from llamafactory.model.adapter import init_adapter
@@ -45,10 +46,12 @@ def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bo
assert param.requires_grad != freeze_language_model
-@pytest.mark.parametrize("freeze_vision_tower", (False, True))
-def test_visual_lora(freeze_vision_tower: bool):
+@pytest.mark.parametrize("freeze_vision_tower,freeze_language_model", ((False, False), (False, True), (True, False)))
+def test_visual_lora(freeze_vision_tower: bool, freeze_language_model: bool):
model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
- finetuning_args = FinetuningArguments(finetuning_type="lora", freeze_vision_tower=freeze_vision_tower)
+ finetuning_args = FinetuningArguments(
+ finetuning_type="lora", freeze_vision_tower=freeze_vision_tower, freeze_language_model=freeze_language_model
+ )
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
with torch.device("meta"):
model = AutoModelForVision2Seq.from_config(config)
@@ -61,10 +64,15 @@ def test_visual_lora(freeze_vision_tower: bool):
else:
frozen_params.add(name)
- if freeze_vision_tower:
- assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" not in trainable_params
+ if is_transformers_version_greater_than("4.52.0"):
+ visual_param_name = "base_model.model.model.visual.blocks.0.attn.qkv.lora_A.default.weight"
+ language_param_name = "base_model.model.model.language_model.layers.0.self_attn.q_proj.lora_A.default.weight"
+ merger_param_name = "base_model.model.model.visual.merger.lora_A.default.weight"
else:
- assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" in trainable_params
+ visual_param_name = "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight"
+ language_param_name = "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight"
+ merger_param_name = "base_model.model.visual.merger.lora_A.default.weight"
- assert "merger" not in trainable_params
- assert "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight" in trainable_params
+ assert (visual_param_name in trainable_params) != freeze_vision_tower
+ assert (language_param_name in trainable_params) != freeze_language_model
+ assert (merger_param_name in trainable_params) is False
diff --git a/tests/version.txt b/tests/version.txt
index bbb71ad5..dae5ebba 100644
--- a/tests/version.txt
+++ b/tests/version.txt
@@ -1,2 +1,2 @@
# change if test fails or cache is outdated
-0.9.3.106
+0.9.3.107