fix: add preprocessing script for convert SenseNova-SI dataset to qwen3-vl format

README.md

@@ -818,6 +818,28 @@ uv pip install flash-attn --no-build-isolation
 uv pip install liger-kernel
 ```
 
+**Preprocess dataset**
+
+Convert `SenseNova-SI-800K.jsonl` to Qwen3-VL training format:
+
+```bash
+python training/qwen3_vl/preprocess_sensenova_si_dataset.py \
+  --src data/SenseNova-SI-800K.jsonl \
+  --dst data/SenseNova-SI-800K_qwen3vl_format.jsonl
+```
+
+**Prepare dataset YAML**
+see [training/qwen3_vl/data.yaml](training/qwen3_vl/data.yaml)
+```YAML
+datasets:
+  - path: /path/to/SenseNova-SI-800K/SenseNova-SI-800K_qwen3vl_format.jsonl
+    data_folder: /path/to/SenseNova-SI-800K/
+    data_type: jsonl
+```
+
+**Configure training**
+See [training/qwen3_vl/train_config.yaml](training/qwen3_vl/train_config.yaml)
+
 **Run training**
 
 ```bash

README_CN.md

@@ -810,6 +810,28 @@ uv pip install flash-attn --no-build-isolation
 uv pip install liger-kernel
 ```
 
+**数据预处理**
+
+先将 `SenseNova-SI-800K.jsonl` 转换为 Qwen3-VL 训练数据格式：
+
+```bash
+python training/qwen3_vl/preprocess_sensenova_si_dataset.py \
+  --src data/SenseNova-SI-800K.jsonl \
+  --dst data/SenseNova-SI-800K_qwen3vl_format.jsonl
+```
+
+**准备数据 YAML**
+参考 [training/qwen3_vl/data.yaml](training/qwen3_vl/data.yaml)
+```YAML
+datasets:
+  - path: /path/to/SenseNova-SI-800K/SenseNova-SI-800K_qwen3vl_format.jsonl
+    data_folder: /path/to/SenseNova-SI-800K/
+    data_type: jsonl
+```
+
+**配置训练参数**
+参考 [training/qwen3_vl/train_config.yaml](training/qwen3_vl/train_config.yaml)
+
 **开始训练**
 
 ```bash

training/qwen3_vl/data.yaml

@@ -1,4 +1,4 @@
 datasets:
-  - path: /path/to/SenseNova-SI-800K/SenseNova-SI-800K.jsonl
+  - path: /path/to/SenseNova-SI-800K/SenseNova-SI-800K_qwen3vl_format.jsonl
     data_folder: /path/to/SenseNova-SI-800K/
     data_type: jsonl

training/qwen3_vl/preprocess_sensenova_si_dataset.py

@@ -0,0 +1,148 @@
+"""Preprocess SenseNova-SI dataset JSONL into lmms-engine compatible format.
+
+This script fixes two schema incompatibilities:
+1. `image` mixed types (`str` and `list[str]`) -> normalized to `list[str]`.
+2. `conversations` format -> converted to `messages` with structured `content`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+
+def normalize_image_field(sample: dict[str, Any]) -> bool:
+    """Normalize `image` to list[str] for Arrow/HF Dataset compatibility."""
+    image = sample.get("image")
+
+    if isinstance(image, str):
+        sample["image"] = [image]
+        return True
+
+    if isinstance(image, list):
+        return False
+
+    if image is None:
+        return False
+
+    raise ValueError(f"Unsupported image type: {type(image).__name__}")
+
+
+def map_conversations_to_messages(sample: dict[str, Any]) -> bool:
+    """Convert OpenAI-like `conversations` into lmms-engine `messages`."""
+    conversations = sample.get("conversations")
+    if conversations is None:
+        return False
+
+    if not isinstance(conversations, list):
+        raise ValueError("`conversations` must be a list.")
+
+    mapped_messages: list[dict[str, Any]] = []
+    for conversation in conversations:
+        if not isinstance(conversation, dict):
+            raise ValueError("Each `conversations` item must be an object.")
+
+        sender = conversation.get("from")
+        text = conversation.get("value", "")
+
+        if sender == "human":
+            role = "user"
+        elif sender == "gpt":
+            role = "assistant"
+        else:
+            role = str(sender) if sender is not None else "user"
+
+        mapped_messages.append(
+            {
+                "role": role,
+                "content": [{"type": "text", "text": text}],
+            }
+        )
+
+    sample["messages"] = mapped_messages
+    del sample["conversations"]
+    return True
+
+
+def default_output_path(src_path: Path) -> Path:
+    """Build default output path with `_qwen3vl_format` suffix."""
+    return src_path.with_name(
+        f"{src_path.stem}_qwen3vl_format{src_path.suffix or '.jsonl'}"
+    )
+
+
+def preprocess_jsonl(src_path: Path, dst_path: Path) -> None:
+    """Read JSONL, normalize each sample, and write mapped JSONL."""
+    image_fixed_count = 0
+    conversation_fixed_count = 0
+    total_count = 0
+
+    dst_path.parent.mkdir(parents=True, exist_ok=True)
+
+    with (
+        src_path.open("r", encoding="utf-8") as source,
+        dst_path.open("w", encoding="utf-8") as target,
+    ):
+        for line_number, line in enumerate(source, start=1):
+            stripped = line.strip()
+            if not stripped:
+                continue
+
+            try:
+                sample = json.loads(stripped)
+            except json.JSONDecodeError as error:
+                raise ValueError(
+                    f"Invalid JSON at line {line_number}: {error}"
+                ) from error
+
+            if not isinstance(sample, dict):
+                raise ValueError(f"Line {line_number} is not a JSON object.")
+
+            if normalize_image_field(sample):
+                image_fixed_count += 1
+            if map_conversations_to_messages(sample):
+                conversation_fixed_count += 1
+
+            target.write(json.dumps(sample, ensure_ascii=False) + "\n")
+            total_count += 1
+
+    print(
+        "Done."
+        f" total={total_count},"
+        f" image_fixed={image_fixed_count},"
+        f" conversations_mapped={conversation_fixed_count},"
+        f" output='{dst_path}'"
+    )
+
+
+def build_args() -> argparse.Namespace:
+    """Build and parse CLI arguments."""
+    parser = argparse.ArgumentParser(
+        description="Preprocess SenseNova-SI dataset JSONL for lmms-engine training."
+    )
+    parser.add_argument(
+        "--src",
+        required=True,
+        type=Path,
+        help="Path to original SenseNova-SI dataset JSONL.",
+    )
+    parser.add_argument(
+        "--dst",
+        type=Path,
+        default=None,
+        help="Output JSONL path. Default: <src_stem>_qwen3vl_format.jsonl",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    """Script entrypoint."""
+    args = build_args()
+    dst_path = args.dst if args.dst is not None else default_output_path(args.src)
+    preprocess_jsonl(src_path=args.src, dst_path=dst_path)
+
+
+if __name__ == "__main__":
+    main()