[model] add qwen2.5 vl models (#6779)

Former-commit-id: 999c7c8fe0caf6b837a1bdc2c6a24fafec327cd8
2025-08-23 22:32:54 +08:00 · 2025-01-31 03:00:29 +08:00 · 2025-01-31 03:00:29 +08:00 · e8c1979b79
commit e8c1979b79
parent f6779b0e0c
8 changed files with 77 additions and 30 deletions
--- a/README.md
+++ b/README.md
@ -5,7 +5,7 @@
 [![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
 [![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
 [![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
-[![Citation](https://img.shields.io/badge/citation-210-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
+[![Citation](https://img.shields.io/badge/citation-238-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
 [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)

 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@ -73,7 +73,7 @@ Choose your path:

 ## Features

- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
+- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, ChatGLM, Phi, etc.
 - **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
 - **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
 - **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [APOLLO](https://github.com/zhuhanqing/APOLLO), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
@ -105,16 +105,18 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/

 ## Changelog

+[25/01/31] We supported fine-tuning the **[DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)** and **[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** model.
+
 [25/01/15] We supported **[APOLLO](https://arxiv.org/abs/2412.05270)** optimizer. See [examples](examples/README.md) for usage.

 [25/01/14] We supported fine-tuning the **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** and **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** models. Thank [@BUAADreamer](https://github.com/BUAADreamer)'s PR.

+<details><summary>Full Changelog</summary>
+
 [25/01/14] We supported fine-tuning the **[InternLM3](https://huggingface.co/collections/internlm/)** models. Thank [@hhaAndroid](https://github.com/hhaAndroid)'s PR.

 [25/01/10] We supported fine-tuning the **[Phi-4](https://huggingface.co/microsoft/phi-4)** model.

-<details><summary>Full Changelog</summary>
-
 [24/12/21] We supported using **[SwanLab](https://github.com/SwanHubX/SwanLab)** for experiment tracking and visualization. See [this section](#use-swanlab-logger) for details.

 [24/11/27] We supported fine-tuning the **[Skywork-o1](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)** model and the **[OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)** dataset.
@ -243,7 +245,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 | [Phi-4](https://huggingface.co/microsoft)                         | 14B                              | phi4             |
 | [Pixtral](https://huggingface.co/mistralai)                       | 12B                              | pixtral          |
 | [Qwen/QwQ (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen)   | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen             |
-| [Qwen2-VL/QVQ](https://huggingface.co/Qwen)                       | 2B/7B/72B                        | qwen2_vl         |
+| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen)            | 2B/3B/7B/72B                     | qwen2_vl         |
 | [Skywork o1](https://huggingface.co/Skywork)                      | 8B                               | skywork_o1       |
 | [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                |
 | [TeleChat2](https://huggingface.co/Tele-AI)                       | 3B/7B/35B/115B                   | telechat2        |
--- a/README_zh.md
+++ b/README_zh.md
@ -5,7 +5,7 @@
 [![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
 [![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
 [![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
-[![Citation](https://img.shields.io/badge/citation-210-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
+[![Citation](https://img.shields.io/badge/citation-238-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
 [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)

 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@ -75,7 +75,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272

 ## 项目特色

- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
+- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、DeepSeek、Yi、Gemma、ChatGLM、Phi 等等。
 - **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
 - **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
 - **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[APOLLO](https://github.com/zhuhanqing/APOLLO)、[Adam-mini](https://github.com/zyushun/Adam-mini)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
@ -107,16 +107,18 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272

 ## 更新日志

+[25/01/31] 我们支持了 **[DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)** 和 **[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** 模型的微调。
+
 [25/01/15] 我们支持了 **[APOLLO](https://arxiv.org/abs/2412.05270)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。

 [25/01/14] 我们支持了 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 和 **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** 模型的微调。 感谢 [@BUAADreamer](https://github.com/BUAADreamer) 的 PR.

+<details><summary>展开日志</summary>
+
 [25/01/14] 我们支持了 **[InternLM3](https://huggingface.co/collections/internlm/)** 模型的微调。感谢 [@hhaAndroid](https://github.com/hhaAndroid) 的 PR。

 [25/01/10] 我们支持了 **[Phi-4](https://huggingface.co/microsoft/phi-4)** 模型的微调。

-<details><summary>展开日志</summary>
-
 [24/12/21] 我们支持了使用 **[SwanLab](https://github.com/SwanHubX/SwanLab)** 跟踪与可视化实验。详细用法请参考 [此部分](#使用-swanlab-面板)。

 [24/11/27] 我们支持了 **[Skywork-o1](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)** 模型的微调和 **[OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)** 数据集。
@ -245,7 +247,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
 | [Phi-4](https://huggingface.co/microsoft)                         | 14B                              | phi4             |
 | [Pixtral](https://huggingface.co/mistralai)                       | 12B                              | pixtral          |
 | [Qwen/QwQ (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen)   | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen             |
-| [Qwen2-VL/QVQ](https://huggingface.co/Qwen)                       | 2B/7B/72B                        | qwen2_vl         |
+| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen)            | 2B/3B/7B/72B                     | qwen2_vl         |
 | [Skywork o1](https://huggingface.co/Skywork)                      | 8B                               | skywork_o1       |
 | [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                |
 | [TeleChat2](https://huggingface.co/Tele-AI)                       | 3B/7B/35B/115B                   | telechat2        |
--- a/src/llamafactory/chat/hf_engine.py
+++ b/src/llamafactory/chat/hf_engine.py
@ -176,6 +176,9 @@ class HuggingfaceEngine(BaseEngine):
            if torch.is_floating_point(value):  # cast data dtype for paligemma
                value = value.to(model.dtype)

+            if key == "second_per_grid_ts":  # qwen2.5vl special case
+                gen_kwargs[key] = value.tolist()
+            else:
                gen_kwargs[key] = value.to(model.device)

        if getattr(model.config, "model_type", None) in ["minicpmv", "minicpmo"]:
--- a/src/llamafactory/data/collator.py
+++ b/src/llamafactory/data/collator.py
@ -135,12 +135,16 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
        features: Dict[str, "torch.Tensor"] = super().__call__(features)

        if self.model is not None and hasattr(self.model, "get_rope_index"):  # for qwen2vl mrope
-            features["position_ids"], features["rope_deltas"] = self.model.get_rope_index(
-                input_ids=features["input_ids"],
-                image_grid_thw=mm_inputs.get("image_grid_thw", None),
-                video_grid_thw=mm_inputs.get("video_grid_thw", None),
-                attention_mask=features["attention_mask"],
-            )
+            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"],
+            }
+            if "second_per_grid_ts" in mm_inputs:
+                rope_index_kwargs["second_per_grid_ts"] = mm_inputs.get("second_per_grid_ts")
+
+            features["position_ids"], features["rope_deltas"] = self.model.get_rope_index(**rope_index_kwargs)

        if "cross_attention_mask" in mm_inputs:  # for mllama inputs when pad_to_multiple_of is enabled
            cross_attention_mask = mm_inputs.pop("cross_attention_mask")
--- a/src/llamafactory/data/mm_plugin.py
+++ b/src/llamafactory/data/mm_plugin.py
@ -178,16 +178,16 @@ class BasePlugin:
        if len(images) != 0:
            images = self._regularize_images(
                images,
-                image_resolution=getattr(processor, "image_resolution", 512 * 512),
+                image_resolution=getattr(processor, "image_resolution", 768 * 768),
            )
            input_dict["images"] = images

        if len(videos) != 0:
            videos = self._regularize_videos(
                videos,
-                image_resolution=getattr(processor, "video_resolution", 128 * 128),
+                image_resolution=getattr(processor, "video_resolution", 256 * 256),
                video_fps=getattr(processor, "video_fps", 2.0),
-                video_maxlen=getattr(processor, "video_maxlen", 64),
+                video_maxlen=getattr(processor, "video_maxlen", 128),
            )
            input_dict["videos"] = videos

@ -501,7 +501,7 @@ class MiniCPMVPlugin(BasePlugin):
        if len(images) != 0:
            images = self._regularize_images(
                images,
-                image_resolution=getattr(processor, "image_resolution", 512 * 512),
+                image_resolution=getattr(processor, "image_resolution", 768 * 768),
            )
            if "valid_image_nums_ls" in kwargs:
                valid_image_nums_ls = kwargs["valid_image_nums_ls"]
@ -521,9 +521,9 @@ class MiniCPMVPlugin(BasePlugin):
        if len(videos) != 0:
            videos = self._regularize_videos(
                videos,
-                image_resolution=getattr(processor, "video_resolution", 128 * 128),
+                image_resolution=getattr(processor, "video_resolution", 256 * 256),
                video_fps=getattr(processor, "video_fps", 2.0),
-                video_maxlen=getattr(processor, "video_maxlen", 64),
+                video_maxlen=getattr(processor, "video_maxlen", 128),
            )
            video_inputs = image_processor(videos, do_pad=True, max_slice_nums=2, return_tensors="pt")
            mm_inputs.update(video_inputs)
@ -610,7 +610,7 @@ class MllamaPlugin(BasePlugin):
        """
        image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
        imglens: List[int] = kwargs["imglens"]
-        images = self._regularize_images(images, image_resolution=getattr(processor, "image_resolution", 512 * 512))
+        images = self._regularize_images(images, image_resolution=getattr(processor, "image_resolution", 768 * 768))
        batch_images = []
        for image_length in imglens:
            batch_images.append(images[:image_length])
@ -875,7 +875,15 @@ class Qwen2vlPlugin(BasePlugin):
        processor: Optional["ProcessorMixin"],
    ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
        self._validate_input(images, videos)
-        return self._get_mm_inputs(images, videos, processor)
+        mm_inputs = self._get_mm_inputs(images, videos, processor)
+        image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+        if "second_per_grid_ts" in getattr(image_processor, "model_input_names", []) and "video_grid_thw" in mm_inputs:
+            video_fps = getattr(processor, "video_fps", 2.0)
+            mm_inputs["second_per_grid_ts"] = [image_processor.temporal_patch_size / video_fps] * len(
+                mm_inputs["video_grid_thw"]
+            )
+
+        return mm_inputs


 class VideoLlavaPlugin(BasePlugin):
--- a/src/llamafactory/extras/constants.py
+++ b/src/llamafactory/extras/constants.py
@ -1928,6 +1928,14 @@ register_model_group(
            DownloadSource.DEFAULT: "Qwen/Qwen2.5-72B-Instruct",
            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-72B-Instruct",
        },
+        "Qwen2.5-7B-Instruct-1M": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-7B-Instruct-1M",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-7B-Instruct-1M",
+        },
+        "Qwen2.5-14B-Instruct-1M": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-14B-Instruct-1M",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-14B-Instruct-1M",
+        },
        "Qwen2.5-0.5B-Instruct-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8",
@ -2149,6 +2157,18 @@ register_model_group(
            DownloadSource.DEFAULT: "Qwen/QVQ-72B-Preview",
            DownloadSource.MODELSCOPE: "Qwen/QVQ-72B-Preview",
        },
+        "Qwen2.5-VL-3B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-3B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-3B-Instruct",
+        },
+        "Qwen2.5-VL-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-7B-Instruct",
+        },
+        "Qwen2.5-VL-72B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-72B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-72B-Instruct",
+        },
    },
    template="qwen2_vl",
    vision=True,
--- a/src/llamafactory/hparams/model_args.py
+++ b/src/llamafactory/hparams/model_args.py
@ -59,19 +59,19 @@ class ProcessorArguments:
    """

    image_resolution: int = field(
-        default=512 * 512,
-        metadata={"help": "Keeps the number of pixels of image below this resolution."},
+        default=768 * 768,
+        metadata={"help": "The maximum number of pixels of image inputs."},
    )
    video_resolution: int = field(
-        default=128 * 128,
-        metadata={"help": "Keeps the number of pixels of video below this resolution."},
+        default=256 * 256,
+        metadata={"help": "The maximum number of pixels of video inputs."},
    )
    video_fps: float = field(
        default=2.0,
        metadata={"help": "The frames to sample per second for video inputs."},
    )
    video_maxlen: int = field(
-        default=64,
+        default=128,
        metadata={"help": "The maximum number of sampled frames for video inputs."},
    )

--- a/src/llamafactory/model/model_utils/visual.py
+++ b/src/llamafactory/model/model_utils/visual.py
@ -286,3 +286,11 @@ _register_composite_model(
    vision_model_keys=["visual.patch_embed", "visual.blocks"],
    language_model_keys=["model", "lm_head"],
 )
+
+
+_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"],
+)