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Merged
haolpku merged 1 commit into from
Feb 26, 2026
Merged

fix installation #21

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7 changes: 2 additions & 5 deletions docs/en/notes/mm_guide/image_understanding/image_gcot.md
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Expand Up @@ -39,13 +39,10 @@ cd run_gcot

Save the code in the "Pipeline Example" section below as `image_gcot_pipeline.py`.

### Step 3: Configure Parameters

Ensure you have a VLM model capable of grounding (e.g., Qwen2.5-VL-7B-Instruct).
### Step 3: Download Example Data

```bash
# Install dependencies
pip install open-dataflow vllm
huggingface-cli download --repo-type dataset OpenDCAI/dataflow-demo-image --local-dir example_data

```

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7 changes: 2 additions & 5 deletions docs/en/notes/mm_guide/image_understanding/image_scale_caption_pipeline.md
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Expand Up @@ -40,13 +40,10 @@ cd run_scalecap

Save the code in the "Pipeline Example" section below as `scalecap_pipeline.py`.

### Step 3: Configure Parameters

Ensure the VLM model path (e.g., Qwen2.5-VL) is correct.
### Step 3: Download Example Data

```bash
# Install dependencies
pip install open-dataflow vllm
huggingface-cli download --repo-type dataset OpenDCAI/dataflow-demo-image --local-dir example_data

```

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7 changes: 2 additions & 5 deletions docs/en/notes/mm_guide/image_understanding/image_visual_only_mcq_pipeline.md
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Expand Up @@ -36,13 +36,10 @@ cd run_vis_mcq

Save the code in the "Pipeline Example" section below as `visual_mcq_pipeline.py`.

### Step 3: Configure Parameters

Control filtering thresholds via CLI. For example, requiring 100% visual accuracy and less than 25% blind accuracy:
### Step 3: Download Example Data

```bash
# Install dependencies
pip install open-dataflow vllm
huggingface-cli download --repo-type dataset OpenDCAI/dataflow-demo-image --local-dir example_data

```

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7 changes: 2 additions & 5 deletions docs/en/notes/mm_guide/image_understanding/vision_mct_reasoning_pipeline.md
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Expand Up @@ -39,13 +39,10 @@ cd run_mcts_reasoning

Save the code in the "Pipeline Example" section below as `vision_mcts_pipeline.py`.

### Step 3: Configure Parameters

Ensure the input file (jsonl) contains the `tree` field (for extraction) or just `question/image` (for generation).
### Step 3: Download Example Data

```bash
# Install dependencies
pip install open-dataflow vllm
huggingface-cli download --repo-type dataset OpenDCAI/dataflow-demo-image --local-dir example_data

```

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