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Hi @wangtf-Kevin -

If you have a specific question, feedback, or issue related to this model, please feel free to share more details. We’d be happy to help!

Hi @wangtf-Kevin -

If you have a specific question, feedback, or issue related to this model, please feel free to share more details. We’d be happy to help!

That was a specific question or issue.

He's asking why your model, in it's CoT block, goes "(\rightarrow)" to mark logic chains like this:

User is asking question (\rightarrow) google staff ignores it (\rightarrow) Model is sad.

It's not a problem per say, but it's weird enough to be worth asking why it's the case, as an emoji or a "->" or literally anything else would have made more sense. Is that a special token that was trained / used for logic chains?

hi @sonali-kumari11 @SerialKicked
I think it may be due to the use of Chinese. Please note the printed information : $\rightarrow$

I use gemma4 to analyze MinerU ，set default Chinese in Claude code cli use vs code terminal, the print like :
ask:

找出 mineru-models-download -s modelscope -m all 需要下载的所有模型
⧉ Selected 1 lines from docker/china/Dockerfile in Visual Studio Code

anwser:
● 通过分析 mineru/cli/models_download.py 的源码，执行 mineru-models-download -s
modelscope -m all 会下载两类模型：Pipeline 模型 和 VLM 模型。

以下是详细的模型清单：

1. Pipeline 模型 (Pipeline Models)

代码中的 download_pipeline_models()
函数定义了需要下载的模型列表。这些模型主要负责版面分析、公式识别、表格识别和
OCR。

需要下载的具体模型（对应 ModelPath 枚举）包括：

• pp_doclayout_v2: 文档版面分析模型 (Layout Analysis)
• unimernet_small: 公式识别模型 (Formula Recognition)
• pytorch_paddle: OCR 基础模型 (OCR)
• slanet_plus: 表格结构识别模型 (Table Structure Recognition)
• unet_structure: 表格结构识别模型 (Table Structure Recognition)
• paddle_table_cls: 表格分类模型 (Table Classification)
• paddle_orientation_classification: 文本方向分类模型 (Orientation
  Classification)
• pp_formulanet_plus_m: 公式识别增强模型 (Formula Recognition)

2. VLM 模型 (VLM Models)

代码中的 download_vlm_models() 函数负责下载 VLM (Vision-Language Model)
相关的模型。

• VLM 权重文件: 该函数调用 auto_download_and_get_model_root_path("/",
  repo_mode='vlm')，它会下载一个完整的 VLM
  模型权重库（通常是一个大型的模型文件夹），用于高精度的端到端解析。

总结

当你运行 -m all 时，程序会依次执行上述两个下载函数。

下载流程摘要：

1. Pipeline 组 $\rightarrow$ 下载 8 个专项模型 $\rightarrow$ 写入 mineru.json
   的 models-dir.pipeline。
2. VLM 组 $\rightarrow$ 下载 VLM 权重库 $\rightarrow$ 写入 mineru.json 的
   models-dir.vlm。

如果你打算手动下载，你需要确保上述所有模型都被下载到本地，并且在 mineru.json
中正确配置这两个目录的路径。

ask:
❯ use English Re-answer

answer：
● Based on the analysis of the source code in mineru/cli/models_download.py, executing the command mineru-models-download -s modelscope -m all triggers the download of two main categories of models: Pipeline Models and VLM Models.

Here is the detailed breakdown of the models that will be downloaded:

1. Pipeline Models

The download_pipeline_models() function defines a list of specialized models used for layout analysis, formula recognition, table recognition, and OCR.

The specific models downloaded (corresponding to the ModelPath enum) are:

• pp_doclayout_v2: Document Layout Analysis model.
• unimernet_small: Formula Recognition model.
• pytorch_paddle: Base OCR models.
• slanet_plus: Table Structure Recognition model.
• unet_structure: Table Structure Recognition model.
• paddle_table_cls: Table Classification model.
• paddle_orientation_classification: Text Orientation Classification model.
• pp_formulanet_plus_m: Enhanced Formula Recognition model.

2. VLM Models

The download_vlm_models() function handles the download of the Vision-Language Model (VLM) weights.

• VLM Weight Library: This function calls auto_download_and_get_model_root_path("/", repo_mode='vlm'), which downloads a comprehensive VLM model weights directory used for high-accuracy end-to-end parsing.

Summary Table

┌────────────────┬──────────────────────┬──────────────────────────────────────────────┬───────────────────────────┐
│ Model Category │ Scope │ Primary Purpose │ Config Key in mineru.json │
├────────────────┼──────────────────────┼──────────────────────────────────────────────┼───────────────────────────┤
│ Pipeline │ 8 Specialized Models │ Layout, OCR, Formula, and Table Recognition │ models-dir.pipeline │
├────────────────┼──────────────────────┼──────────────────────────────────────────────┼───────────────────────────┤
│ VLM │ VLM Weight Library │ High-precision end-to-end multimodal parsing │ models-dir.vlm │
└────────────────┴──────────────────────┴──────────────────────────────────────────────┴───────────────────────────┘

Execution Workflow:
When the -m all flag is used, the program sequentially executes both download_pipeline_models() and download_vlm_models(). Once completed, it automatically updates your mineru.json configuration file with the local paths to these downloaded models.

Interesting how it's removed from your English translated version. I use the model only in English, and while I've seen $\rightarrow$ surface plenty of times in English, it was always in the thinking block, never in the responses.

Now that I think about it, I noticed the rightarrow thing in the early GGUF versions on early llama.cpp implementation, but I don't remember seeing it recently. But I haven't paid that much attention to it either (don't often read the thinking block).

Edit: Correction, I still see it in modern GGUF and backends. Both in response and thinking block. It's just not super common.

It's just a LaTeX expression embedded in markdown.
I guess it's natural for them to use $\rightarrow$ in thinking section since they use markdown in the section.