Add OmniReasoner-SFT metadata and training jsonl

.gitattributes

@@ -58,3 +58,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/train.jsonl filter=lfs diff=lfs merge=lfs -text
+source_metadata/derived_composition/finevideo_long_video_dataset/long_video_dataset.json filter=lfs diff=lfs merge=lfs -text
+source_metadata/derived_composition/finevideo_long_video_dataset/long_video_dataset_mcq.json filter=lfs diff=lfs merge=lfs -text
+source_metadata/derived_composition/finevideo_long_video_dataset/long_video_dataset_mcq_filtered.json filter=lfs diff=lfs merge=lfs -text
+source_metadata/derived_composition/scale_finevideo_composition_dataset/long_video_dataset.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,107 @@
+---
+pretty_name: OmniReasoner-SFT
+license: other
+task_categories:
+- visual-question-answering
+- audio-classification
+- video-classification
+language:
+- en
+tags:
+- video
+- audio-visual
+- multimodal
+- reasoning
+- supervised-fine-tuning
+- long-video-understanding
+size_categories:
+- 10K<n<100K
+gated: true
+extra_gated_heading: "Access OmniReasoner-SFT"
+extra_gated_description: "This mixed-source dataset is provided for non-commercial academic research only."
+extra_gated_button_content: "Request access"
+extra_gated_prompt: "This dataset contains raw and derived media from CG-Bench, FineVideo, LLaVA-Video-178K, AVQA-R1/OmniInstruct/AVQA/VGGSound, and original platforms. By requesting access, you agree to use it only for non-commercial academic research, comply with all source dataset licenses and platform terms, not redistribute raw or derived media, and honor takedown requests."
+extra_gated_fields:
+  Name: text
+  Affiliation: text
+  Intended use:
+    type: select
+    options:
+    - Academic research
+    - Education
+    - label: Other non-commercial research
+      value: other_noncommercial
+  I agree to use this dataset for non-commercial academic research only: checkbox
+  I agree not to redistribute raw or derived media: checkbox
+  I agree to comply with source dataset licenses, platform terms, and takedown requests: checkbox
+---
+
+# OmniReasoner-SFT
+
+OmniReasoner-SFT is a mixed-source, research-only supervised fine-tuning dataset
+for audio-visual and long-video reasoning. It contains two-stage cold-start SFT
+trajectories with interval selection, zoom-in evidence, and final answers.
+
+## Contents
+
+- `data/train.jsonl`: HF-ready training JSONL with repo-relative media paths.
+- `media/`: raw and derived media referenced by `train.jsonl`.
+- `manifests/media_manifest.jsonl`: media inventory with repo paths, source
+  family, media type, file size, and reference counts.
+- `manifests/dataset_stats.json`: dataset and media statistics.
+- `configs/`: lmms-engine SFT configs and launch scripts used for training.
+- `scripts/materialize_lmms_jsonl.py`: rewrites repo-relative paths into local
+  absolute `file://` paths after download.
+- `source_metadata/`: small metadata files for derived anomaly/composition
+  construction where available.
+
+## Source Families
+
+This dataset includes both original-source and derived media:
+
+- CG-Bench long videos.
+- FineVideo videos.
+- AVQA-R1 image/audio samples derived from OmniInstruct/AVQA/VGGSound sources.
+- Composition videos generated by concatenating multiple FineVideo videos.
+- Anomaly videos generated by inserting LLaVA-Video-178K / scaled-source anomaly
+  clips into CG-Bench long videos, with anomaly type and ground-truth intervals.
+
+The final SFT trajectories were produced with Gemini hindsight generation and
+then filtered with leakage checks, hard rules, judge scoring, and fusion.
+
+## License And Usage
+
+The annotations, reasoning traces, metadata, and construction recipes are
+released by the dataset authors for non-commercial academic research only.
+
+Raw and derived media retain the copyright and usage restrictions of their
+source datasets, original creators, and original platforms. Access to this
+repository does not grant additional rights beyond those source licenses and
+terms. Users must not redistribute the raw or derived media.
+
+If you are a rights holder and want a file removed, please contact the dataset
+authors through the Hugging Face repository discussion page.
+
+## Citations And Source Notices
+
+Users should cite this dataset and the applicable upstream datasets, including:
+
+- CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding.
+- FineVideo and YouTube-Commons where applicable.
+- LLaVA-Video-178K for anomaly inserted-source clips where applicable.
+- AVQA-R1 / EchoInk-R1, OmniInstruct/OmniBench, AVQA, and VGGSound where applicable.
+
+## Local Training
+
+`data/train.jsonl` stores relative paths such as `media/videos/...`. To recreate
+the absolute `file://` format expected by the original lmms-engine training
+environment after downloading this repository, run:
+
+```bash
+python scripts/materialize_lmms_jsonl.py \
+  --input data/train.jsonl \
+  --output data/train.local.jsonl \
+  --root "$(pwd)"
+```
+
+Then point the lmms-engine config to `data/train.local.jsonl`.

configs/2026042901_omnireasoner_sft_config_single_node_8gpu_bs2_ga8_gbs128_e3.yaml

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+# Cold-start SFT 训练配置
+# 适用于 OmniReasoner 两阶段视频理解任务
+
+trainer_type: fsdp2_trainer
+
+dataset_config:
+  dataset_type: coldstart_sft
+  dataset_format: jsonl
+
+  # 训练数据保持指向现有 lmms 格式产物；这里不改数据资产日期
+  dataset_path: ${OMNIREASONER_ROOT}/data_factory/cold_start/gt004_sft_final_with_t25polish_t26_cgfix_nobad_v2_20260423_lmms.jsonl
+
+  extra_kwargs:
+    use_audio_in_video: true
+    enable_time_anchors: true
+    force_two_stage_video_params: true
+    fast_fps: 1
+    fast_max_frames: 512
+    fast_max_pixels: 50176
+    fast_min_pixels: 50176
+    slow_fps: 2
+    slow_max_frames: 60
+    slow_max_pixels: 100352
+    slow_min_pixels: 100352
+    stage2_short_threshold_sec: 60
+    stage2_audio_max_sec: 60
+
+  processor_config:
+    processor_name: ${OMNIREASONER_ROOT}/model/Qwen2.5-Omni-Thinker-7B
+    processor_type: Qwen2_5OmniProcessor
+    audio_max_length: 60
+    video_max_pixels: 602112
+    video_min_pixels: 28800
+    image_max_pixels: 602112
+    image_min_pixels: 28800
+
+  shuffle: true
+  data_seed: 42
+  eval_dataset_path: null
+
+  object_storage: none
+  bucket_name: null
+
+  packing: false
+  packing_strategy: first_fit
+  packing_length: 51200
+  filter_overlong: true
+  filter_overlong_workers: 8
+  max_length: null
+
+  video_sampling_strategy: fps
+  video_max_pixels: 602112
+  video_max_frames: 768
+  video_min_pixels: 28800
+  fps: 1
+  video_backend: qwen_omni_utils
+
+trainer_args:
+  output_dir: ${OMNIREASONER_ROOT}/Omni-Reasoning/lmms-engine/output/omnireasoner_sft/2026042901_single_node_8gpu_bs2_ga8_gbs128_e3
+  overwrite_output_dir: true
+
+  do_train: true
+  do_eval: false
+  eval_strategy: "no"
+
+  # Global batch = per_device_train_batch_size(2) * world_size(8) * grad_acc(8) = 128
+  per_device_train_batch_size: 2
+  gradient_accumulation_steps: 8
+
+  # 保守默认值：先保持和上一轮同级别学习率，确认稳定后再考虑升到 1e-5 做对照
+  learning_rate: 5.0e-06
+  weight_decay: 0.01
+  adam_beta1: 0.9
+  adam_beta2: 0.999
+  adam_epsilon: 1.0e-08
+  max_grad_norm: 5.0
+
+  num_train_epochs: 3
+  # 按用户口径：25839 * 3 / 128 = 605.6 -> max_steps 取 605
+  max_steps: 605
+
+  lr_scheduler_type: cosine
+  warmup_ratio: 0.03
+  warmup_steps: 0
+
+  logging_strategy: steps
+  logging_steps: 10
+  logging_first_step: true
+  logging_nan_inf_filter: true
+
+  save_strategy: steps
+  save_steps: 100
+  save_total_limit: 3
+  save_safetensors: true
+
+  bf16: true
+  fp16: false
+
+  fsdp2: true
+  fsdp_config:
+    transformer_layer_cls_to_wrap:
+      - Qwen2_5OmniDecoderLayer
+    reshard_after_forward: false
+    min_num_params: 0
+
+  gradient_checkpointing: true
+  use_liger_kernel: true
+  use_rmpad: true
+  optim: adamw_torch_fused
+
+  dataloader_num_workers: 4
+  dataloader_prefetch_factor: 1
+  dataloader_drop_last: true
+  dataloader_pin_memory: true
+
+  report_to:
+    - wandb
+  run_name: 2026042901_single_node_8gpu_bs2_ga8_gbs128_e3
+
+  seed: 42
+  local_rank: 0
+  ddp_timeout: 7200
+
+  freeze_modules: ["visual"]
+
+  sp_ulysses_degree: 1
+
+  reduce_dtype: bfloat16
+  output_dtype: bfloat16
+
+model_config:
+  extra_kwargs: {}
+  load_from_pretrained_path: ${OMNIREASONER_ROOT}/model/Qwen2.5-Omni-Thinker-7B
+  load_from_config: null
+  attn_implementation: flash_attention_2
+  model_type: qwen2_5_omni
+  torch_dtype: bfloat16
+  overwrite_config: null
+
+  monkey_patch_kwargs:
+    patch_type: ["liger", "timeanchor"]
+    rms_norm: true
+    swiglu: true
+    fused_linear_cross_entropy: true
+    use_rmpad: true
+    enable_time_anchors: true
+
+extra_kwargs: null

configs/2026042901_run_omnireasoner_sft_single_node_g01234567_bs2_ga8_gbs128_e3.sh

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+#!/bin/bash
+# OmniReasoner SFT 单机 8 卡训练启动脚本
+#
+# 训练配置:
+# - per_device_train_batch_size = 2
+# - gradient_accumulation_steps = 8
+# - global batch size = 2 x 8 x 8 = 128
+# - num_train_epochs = 3
+# - max_steps = 605
+
+source ~/.bashrc
+conda activate chenyu_lmms_engine
+
+set -euo pipefail
+
+export PYTHONPATH=${OMNIREASONER_ROOT}/Omni-Reasoning/lmms-engine/src:${PYTHONPATH:-}
+
+MODEL_PATH="${OMNIREASONER_ROOT}/model/Qwen2.5-Omni-Thinker-7B"
+CONFIG_PATH="$(dirname "$0")/2026042901_omnireasoner_sft_config_single_node_8gpu_bs2_ga8_gbs128_e3.yaml"
+
+NUM_NODES=1
+NODE_RANK=0
+MASTER_ADDR="127.0.0.1"
+MASTER_PORT="${MASTER_PORT:-29501}"
+NUM_GPUS_PER_NODE=8
+
+export WANDB_PROJECT="omnireasoner_sft"
+export WANDB_NAME="2026042901_single_node_8gpu_bs2_ga8_gbs128_e3"
+export WANDB_MODE="${WANDB_MODE:-online}"
+# export WANDB_API_KEY="<YOUR_WANDB_API_KEY>"
+export WANDB_API_KEY="2f3e15b6b931d12a70cf8ae666aa5163906a2ccc"
+
+export DEBUG_MODE="True"
+export NCCL_BLOCKING_WAIT=0
+export TOKENIZERS_PARALLELISM=false
+export NCCL_DEBUG=INFO
+export NCCL_TIMEOUT=7200
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export SYNC_SAMPLER=1
+export MMINFO_PREFETCH=1
+
+export TMPDIR=${OMNIREASONER_ROOT}/.tmp
+export XDG_CACHE_HOME=${OMNIREASONER_ROOT}/.cache
+export NUMBA_CACHE_DIR=${OMNIREASONER_ROOT}/.cache/numba_local_2026042901
+mkdir -p "$TMPDIR" "$XDG_CACHE_HOME" "$NUMBA_CACHE_DIR"
+
+export TRANSFORMERS_OFFLINE=1
+
+echo "============================================"
+echo "SFT OmniReasoner 单机 8 卡配置："
+echo "  CUDA_VISIBLE_DEVICES: ${CUDA_VISIBLE_DEVICES}"
+echo "  当前节点编号: NODE_RANK=${NODE_RANK}"
+echo "  总节点数: NNODES=${NUM_NODES}"
+echo "  主节点地址: ${MASTER_ADDR}:${MASTER_PORT}"
+echo "  每节点 GPU 数: ${NUM_GPUS_PER_NODE}"
+echo "  MODEL_PATH: ${MODEL_PATH}"
+echo "  CONFIG_PATH: ${CONFIG_PATH}"
+echo "  WANDB_NAME: ${WANDB_NAME}"
+echo "============================================"
+
+torchrun \
+    --nproc_per_node="${NUM_GPUS_PER_NODE}" \
+    --nnodes="${NUM_NODES}" \
+    --node_rank="${NODE_RANK}" \
+    --master_addr="${MASTER_ADDR}" \
+    --master_port="${MASTER_PORT}" \
+    -m lmms_engine.launch.cli \
+    config_yaml="${CONFIG_PATH}"

configs/README.md

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+# Cold-start SFT 训练迁移指南
+
+本目录包含将 cold-start SFT 训练迁移到 lmms-engine 框架的配置和脚本。
+
+## 概述
+
+Cold-start SFT 是一个两阶段视频理解任务：
+- **Stage 1 (全局视频)**：低分辨率、低帧率的全局视频输入，模型输出 `<interval>` 或 `<answer>`
+- **Stage 2 (局部裁剪视频)**：高分辨率、高帧率的局部裁剪视频输入，模型输出最终 `<answer>`
+
+## 文件结构
+
+```
+omnireasoner_sft/
+├── README.md                      # 本文件
+├── omnireasoner_sft_config.yaml      # 训练配置文件
+├── run_omnireasoner_sft.sh           # 单节点训练脚本
+└── run_omnireasoner_sft_multinode.sh # 多节点训练脚本
+```
+
+## 数据格式
+
+### 原始格式 (cold_start_sft_4k.jsonl)
+
+```json
+{
+  "messages": [
+    {"role": "system", "content": [{"type": "text", "text": "..."}]},
+    {"role": "user", "content": [
+      {"type": "video", "video": "/path/to/video.mp4", "fps": 1.0, ...},
+      {"type": "text", "text": "..."}
+    ]},
+    {"role": "assistant", "content": [{"type": "text", "text": "..."}]},
+    {"role": "user", "content": [
+      {"type": "text", "text": "Zoom-in video clip [30.0, 107.0] seconds:"},
+      {"type": "video", "video": "/path/to/video.mp4", "video_start": 30.0, "video_end": 107.0, "fps": 4.0, ...}
+    ]},
+    {"role": "assistant", "content": [{"type": "text", "text": "..."}]}
+  ],
+  "stage1_mode": "interval"
+}
+```
+
+### lmms-engine 格式
+
+```json
+{
+  "messages": [
+    {"role": "system", "content": [{"type": "text", "text": "..."}]},
+    {"role": "user", "content": [
+      {"type": "video_url", "video_url": {"url": "file:///path/to/video.mp4"}, "fps": 1.0, ...},
+      {"type": "text", "text": "..."}
+    ]},
+    {"role": "assistant", "content": [{"type": "text", "text": "..."}]},
+    {"role": "user", "content": [
+      {"type": "text", "text": "Zoom-in video clip [30.0, 107.0] seconds:"},
+      {"type": "video_url", "video_url": {"url": "file:///path/to/video.mp4"}, 
+       "video_start": 30.0, "video_end": 107.0, "fps": 4.0, ...}
+    ]},
+    {"role": "assistant", "content": [{"type": "text", "text": "..."}]}
+  ]
+}
+```
+
+主要变化：
+- `{"type": "video", "video": "..."}` → `{"type": "video_url", "video_url": {"url": "file://..."}}`
+- 保留 `video_start`, `video_end` 等参数用于视频裁剪
+
+## 使用步骤
+
+### 1. 转换数据格式
+
+```bash
+cd /path/to/sih
+
+# 转换数据
+python data_factory/subset/cold_start/convert_to_lmms_engine.py \
+    --input_jsonl data_factory/subset/cold_start/data/cold_start_sft_4k.jsonl \
+    --output_jsonl data_factory/subset/cold_start/data/cold_start_sft_4k_lmms.jsonl
+```
+
+### 2. 修改配置
+
+编辑 `run_omnireasoner_sft.sh` 中的必需配置：
+
+```bash
+# 数据路径（GT-003 最终 coldstart 合并数据）
+DATASET_PATH="${OMNIREASONER_ROOT}/data_factory/cold_start/gt004_sft_final_with_t25.jsonl"
+
+# 模型路径
+MODEL_PATH="Qwen/Qwen2.5-Omni-7B"
+
+# 输出目录
+OUTPUT_DIR="./output/omnireasoner_sft"
+```
+
+### 3. 启动训练
+
+**单节点训练：**
+```bash
+bash run_omnireasoner_sft.sh
+```
+
+**多节点训练：**
+```bash
+# 节点 0
+NODE_RANK=0 MASTER_ADDR=node0 bash run_omnireasoner_sft_multinode.sh
+
+# 节点 1
+NODE_RANK=1 MASTER_ADDR=node0 bash run_omnireasoner_sft_multinode.sh
+```
+
+## 自定义 Dataset 类
+
+本迁移使用了自定义的 `ColdStartSFTDataset` 类（位于 `lmms_engine/datasets/iterable/coldstart_sft_dataset.py`），主要特性：
+
+- 支持 `video_start` / `video_end` 参数进行视频片段裁剪
+- 使用 `qwen_omni_utils` 加载视频，支持音频提取
+- 兼容两阶段对话格式（5轮或3轮）
+- 强制按 stage 套用 fast/slow 参数（可关），并按视频顺序传递 `fps` 与 `video_durations`
+
+## 配置说明
+
+### 视频处理参数
+
+| 参数 | Stage 1 (全局) | Stage 2 (局部) |
+|------|---------------|----------------|
+| fps | 1 | 数据中指定 (通常 4) |
+| max_frames | 768 | 数据中指定 (通常 300) |
+| max_pixels | 602112 | 数据中指定 |
+
+### 重要配置项
+
+```yaml
+dataset_config:
+  dataset_type: coldstart_sft           # 使用自定义数据集类型
+  extra_kwargs:
+    use_audio_in_video: true            # 从视频中提取音频
+    enable_time_anchors: true           # 启用 TimeAnchor（可选）
+    force_two_stage_video_params: true  # 强制按 stage 套用 fast/slow
+    fast_fps: 1
+    fast_max_frames: 512
+    fast_max_pixels: 50176
+    slow_fps: 4
+    slow_max_frames: 60
+    slow_max_pixels: 100352
+  video_backend: qwen_omni_utils        # 使用 qwen_omni_utils 加载视频
+
+model_config:
+  monkey_patch_kwargs:
+    patch_type: ["liger", "timeanchor"] # 启用 Liger kernel 和 TimeAnchor patch
+    enable_time_anchors: true           # 设为 false 可禁用 TimeAnchor
+```
+
+### TimeAnchor 功能
+
+TimeAnchor 在每个视频的 2 秒 chunk 开头插入时间戳（如 `<0 seconds>`, `<2 seconds>`），用于帮助模型理��视频时间线。
+
+**启用 TimeAnchor**：
+```yaml
+model_config:
+  monkey_patch_kwargs:
+    patch_type: ["liger", "timeanchor"]
+    enable_time_anchors: true
+```
+
+**禁用 TimeAnchor**：
+```yaml
+model_config:
+  monkey_patch_kwargs:
+    patch_type: ["liger"]  # 不包含 "timeanchor"
+```
+
+或者保留 patch 但禁用功能：
+```yaml
+model_config:
+  monkey_patch_kwargs:
+    patch_type: ["liger", "timeanchor"]
+    enable_time_anchors: false
+```
+
+## 与原始脚本的差异
+
+1. **不迁移的功能**：
+   - `path_replacements`：视频路径替换逻辑
+   - `DurationBucketSampler`：按时长分桶的采样器
+   - `drop_stage2_for_stage2_only`：丢弃 stage2_only 样本的 dummy 数据
+
+2. **数据格式变化**：
+   - 视频路径需要 `file://` 前缀
+   - content type 从 `video` 改为 `video_url`
+
+3. **框架差异**：
+   - 使用 lmms-engine 的 FSDP2 trainer 而非 TRL 的 SFTTrainer
+   - 使用 YAML 配置而非命令行参数
+
+## 注意事项
+
+1. **视频路径**：确保所有节点都能访问视频文件路径
+2. **内存**：长视频可能需要较大显存，建议使用 `gradient_checkpointing: true`
+3. **音频**：如果不需要音频，可设置 `use_audio_in_video: false` 以加快加载速度
+4. **Stage 2 视频**：视频裁剪由 `qwen_omni_utils` 的 `calculate_video_frame_range` 函数处理
+5. **时序参数对齐**：训练前建议抽样打印 5 轮样本，确认 `len(videos) == len(fps) == len(video_durations)` 且顺序一致
+
+## 2 Epoch 的 max_steps 估算（当前默认并行配置）
+
+默认口径（`run_omnireasoner_sft_multinode.sh` + `omnireasoner_sft_config.yaml`）：
+- 样本数：`15530`（`gt004_sft_final_with_t25.jsonl.stats.json`）
+- `nnodes=8`, `nproc_per_node=1`, `per_device_train_batch_size=1`, `gradient_accumulation_steps=1`
+- `dataloader_drop_last=true`
+
+估算：
+- `global_batch = 8 * 1 * 1 = 8`
+- `steps_per_epoch = floor(15530 / 8) = 1941`
+- `2 epoch -> max_steps = 3882`
+
+## 问题排查
+
+1. **视频加载失败**：检查 `qwen_omni_utils` 是否正确安装
+2. **内存不足**：减小 `video_max_frames` 或 `video_max_pixels`
+3. **NCCL 超时**：增加 `ddp_timeout` 值
+
+## 相关文件
+
+- 数据转换脚本：`data_factory/subset/cold_start/convert_to_lmms_engine.py`
+- 自定义 Dataset：`lmms_engine/datasets/iterable/coldstart_sft_dataset.py`
+- 原始训练脚本：`src/r1-v/src/open_r1/omnireasoner_sft/train_coldstart_sft.py`

configs/omnireasoner_sft_config.yaml

@@ -0,0 +1,181 @@
+# Cold-start SFT 训练配置
+# 适用于两阶段视频理解任务（全局视频 + 局部裁剪视频）
+
+trainer_type: fsdp2_trainer
+
+dataset_config:
+  # 使用自定义的 coldstart_sft 数据集类型
+  dataset_type: coldstart_sft
+  dataset_format: jsonl
+  
+  # 数据路径（转换后的数据）
+  dataset_path: ${OMNIREASONER_ROOT}/data_factory/cold_start/gt004_sft_final_with_t25polish_t26_cgfix_nobad_v2_20260423_lmms.jsonl
+  
+  # 额外配置
+  extra_kwargs:
+    use_audio_in_video: true   # 从视频中提取音频
+    enable_time_anchors: true  # 启用 TimeAnchor（在每个 2s chunk 插入时间戳）
+    force_two_stage_video_params: true # 强制按 stage 套用 fast/slow 参数，避免数据缺字段时漂移
+    fast_fps: 1
+    fast_max_frames: 512
+    fast_max_pixels: 50176
+    fast_min_pixels: 50176
+    slow_fps: 2
+    slow_max_frames: 60
+    slow_max_pixels: 100352
+    slow_min_pixels: 100352
+    stage2_short_threshold_sec: 60
+    stage2_audio_max_sec: 60
+  
+  # Processor 配置
+  processor_config:
+    processor_name: ${OMNIREASONER_ROOT}/model/Qwen2.5-Omni-Thinker-7B
+    processor_type: Qwen2_5OmniProcessor
+    audio_max_length: 60      # audio chunk size for processor placeholders (not total stage2 cap)
+    video_max_pixels: 602112   # ~768*28*28, 适合 Stage1 全局视频
+    video_min_pixels: 28800    # ~36*28*28
+    image_max_pixels: 602112
+    image_min_pixels: 28800
+  
+  # 数据集设置
+  shuffle: true
+  data_seed: 42
+  eval_dataset_path: null
+  
+  # 存储后端
+  object_storage: none
+  bucket_name: null
+  
+  # Packing 配置（长视频数据不建议 packing）
+  packing: false
+  packing_strategy: first_fit
+  packing_length: 51200
+  filter_overlong: true   # 过滤掉超长样本，防止单个样本超出packing/ sequence 长度限制
+  filter_overlong_workers: 8 # 并行估算每个样本 token 数时使用的 worker 数量
+  max_length: null
+  
+  # 视频处理配置
+  video_sampling_strategy: fps
+  video_max_pixels: 602112
+  video_max_frames: 768        # Stage1 全局视频最大帧数
+  video_min_pixels: 28800
+  fps: 1                       # Stage1 采样帧率
+  video_backend: qwen_omni_utils
+
+# 训练参数
+trainer_args:
+  output_dir: ${OMNIREASONER_ROOT}/Omni-Reasoning/lmms-engine/output/omnireasoner_sft/2026042401
+  overwrite_output_dir: true
+  
+  # 训练策略
+  do_train: true
+  do_eval: false
+  eval_strategy: 'no'
+  
+  # Batch size 和梯度累积
+  per_device_train_batch_size: 1
+  gradient_accumulation_steps: 4
+  
+  # 学习率
+  learning_rate: 5.0e-06
+  weight_decay: 0.01
+  adam_beta1: 0.9
+  adam_beta2: 0.999
+  adam_epsilon: 1.0e-08
+  max_grad_norm: 5.0
+  
+  # 训练 epochs
+  num_train_epochs: 3
+  # 单机 4 卡口径：
+  # global_batch_size = per_device_train_batch_size(1) * world_size(4) * grad_acc(4) = 16
+  # 数据总量按 25839 条估算，drop_last=true:
+  # steps_per_epoch = floor(25839 / 16) = 1614
+  # 3 epoch -> 4842
+  max_steps: 4842
+  
+  # 学习率调度
+  lr_scheduler_type: cosine
+  warmup_ratio: 0.03
+  warmup_steps: 0
+  
+  # 日志
+  logging_strategy: steps
+  logging_steps: 10
+  logging_first_step: true
+  logging_nan_inf_filter: true
+  
+  # 保存策略
+  save_strategy: steps
+  save_steps: 100
+  save_total_limit: 3
+  save_safetensors: true
+  
+  # 精度
+  bf16: true
+  fp16: false
+  
+  # 分布式训练
+  fsdp2: true
+  fsdp_config:
+    transformer_layer_cls_to_wrap:
+      - Qwen2_5OmniDecoderLayer
+    reshard_after_forward: false
+    min_num_params: 0
+  
+  # 优化
+  gradient_checkpointing: true
+  use_liger_kernel: true
+  use_rmpad: true
+  optim: adamw_torch_fused
+  
+  # DataLoader
+  dataloader_num_workers: 4
+  dataloader_prefetch_factor: 1
+  dataloader_drop_last: true
+  dataloader_pin_memory: true
+  
+  # WandB
+  report_to:
+    - wandb
+  run_name: omnireasoner_sft_multinode
+  
+  # 其他
+  seed: 42
+  local_rank: 0
+  ddp_timeout: 7200
+  
+  # 冻结视觉模块（可选）
+  # freeze_modules: null  # 设置为 ["visual"] 可冻结视觉编码器
+  freeze_modules: ["visual"]
+  
+  # 序列并行
+  sp_ulysses_degree: 1
+  
+  # 输出精度
+  reduce_dtype: bfloat16
+  output_dtype: bfloat16
+
+# 模型配置
+model_config:
+  extra_kwargs: {}
+  load_from_pretrained_path: ${OMNIREASONER_ROOT}/model/Qwen2.5-Omni-Thinker-7B
+  load_from_config: null
+  attn_implementation: flash_attention_2
+  model_type: qwen2_5_omni
+  torch_dtype: bfloat16
+  overwrite_config: null
+  
+  # Monkey Patch 配置
+  # - liger: Liger kernel 优化（RMSNorm, SwiGLU, CrossEntropy fused）
+  # - timeanchor: TimeAnchor 功能（在每个 2s chunk 插入时间戳）
+  monkey_patch_kwargs:
+    patch_type: ["liger", "timeanchor"]
+    # Liger kernel 参数
+    rms_norm: true
+    swiglu: true
+    fused_linear_cross_entropy: true
+    use_rmpad: true
+    # TimeAnchor 参数
+    enable_time_anchors: true  # 设为 false 可禁用 TimeAnchor
+
+extra_kwargs: null

configs/omnireasoner_sft_config_origin.yaml

@@ -0,0 +1,150 @@
+# Cold-start SFT 训练配置
+# 适用于两阶段视频理解任务（全局视频 + 局部裁剪视频）
+
+trainer_type: fsdp2_trainer
+
+dataset_config:
+  # 使用自定义的 coldstart_sft 数据集类型
+  dataset_type: coldstart_sft
+  dataset_format: jsonl
+  
+  # 数据路径（转换后的数据）
+  dataset_path: /path/to/cold_start_sft_4k_lmms.jsonl
+  
+  # 额外配置
+  extra_kwargs:
+    use_audio_in_video: true  # 从视频中提取音频
+  
+  # Processor 配置
+  processor_config:
+    processor_name: Qwen/Qwen2.5-Omni-7B
+    processor_type: Qwen2_5OmniProcessor
+    audio_max_length: 60
+    video_max_pixels: 602112   # ~768*28*28, 适合 Stage1 全局视频
+    video_min_pixels: 28800    # ~36*28*28
+    image_max_pixels: 602112
+    image_min_pixels: 28800
+  
+  # 数据集设置
+  shuffle: true
+  data_seed: 42
+  eval_dataset_path: null
+  
+  # 存储后端
+  object_storage: none
+  bucket_name: null
+  
+  # Packing 配置（长视频数据不建议 packing）
+  packing: false
+  packing_strategy: first_fit
+  packing_length: 51200
+  filter_overlong: true
+  filter_overlong_workers: 8
+  max_length: null
+  
+  # 视频处理配置
+  video_sampling_strategy: fps
+  video_max_pixels: 602112
+  video_max_frames: 768        # Stage1 全局视频最大帧数
+  video_min_pixels: 28800
+  fps: 1                       # Stage1 采样帧率
+  video_backend: qwen_omni_utils
+
+# 训练参数
+trainer_args:
+  output_dir: ./output/omnireasoner_sft
+  overwrite_output_dir: false
+  
+  # 训练策略
+  do_train: true
+  do_eval: false
+  eval_strategy: 'no'
+  
+  # Batch size 和梯度累积
+  per_device_train_batch_size: 1
+  gradient_accumulation_steps: 4
+  
+  # 学习率
+  learning_rate: 1.0e-05
+  weight_decay: 0.0
+  adam_beta1: 0.9
+  adam_beta2: 0.999
+  adam_epsilon: 1.0e-08
+  max_grad_norm: 1.0
+  
+  # 训练 epochs
+  num_train_epochs: 3
+  max_steps: -1
+  
+  # 学习率调度
+  lr_scheduler_type: cosine
+  warmup_ratio: 0.03
+  warmup_steps: 0
+  
+  # 日志
+  logging_strategy: steps
+  logging_steps: 10
+  logging_first_step: true
+  logging_nan_inf_filter: true
+  
+  # 保存策略
+  save_strategy: steps
+  save_steps: 500
+  save_total_limit: 3
+  save_safetensors: true
+  
+  # 精度
+  bf16: true
+  fp16: false
+  
+  # 分布式训练
+  fsdp2: true
+  fsdp_config:
+    transformer_layer_cls_to_wrap:
+      - Qwen2_5OmniDecoderLayer
+    reshard_after_forward: false
+    min_num_params: 0
+  
+  # 优化
+  gradient_checkpointing: true
+  use_liger_kernel: true
+  use_rmpad: true
+  optim: adamw_torch_fused
+  
+  # DataLoader
+  dataloader_num_workers: 4
+  dataloader_drop_last: true
+  dataloader_pin_memory: true
+  
+  # WandB
+  report_to:
+    - wandb
+  run_name: omnireasoner_sft
+  
+  # 其他
+  seed: 42
+  local_rank: 0
+  ddp_timeout: 7200
+  
+  # 冻结视觉模块（可选）
+  freeze_modules: null  # 设置为 ["visual"] 可冻结视觉编码器
+  
+  # 序列并行
+  sp_ulysses_degree: 1
+  
+  # 输出精度
+  reduce_dtype: bfloat16
+  output_dtype: bfloat16
+
+# 模型配置
+model_config:
+  extra_kwargs: {}
+  load_from_pretrained_path: Qwen/Qwen2.5-Omni-7B
+  load_from_config: null
+  attn_implementation: flash_attention_2
+  model_type: qwen2_5_omni
+  torch_dtype: bfloat16
+  overwrite_config: null
+  monkey_patch_kwargs: null
+
+extra_kwargs: null

configs/run_omnireasoner_sft.sh

@@ -0,0 +1,96 @@
+#!/bin/bash
+# OmniReasoner SFT 训练启动脚本
+# 
+# 用途: 使用 lmms-engine 框架训练两阶段视频理解模型
+#
+# 使用方法:
+#   1. 修改下方配置参数
+#   2. 运行: bash run_omnireasoner_sft.sh
+
+# ============================================================================
+# 必需配置 - 请修改这些参数
+# ============================================================================
+
+# 数据路径（GT-003 最终 coldstart 合并数据）
+DATASET_PATH="${OMNIREASONER_ROOT}/data_factory/cold_start/gt004_sft_final_with_t25.jsonl"
+
+# 模型路径
+MODEL_PATH="Qwen/Qwen2.5-Omni-7B"
+
+# 输出目录
+OUTPUT_DIR="./output/omnireasoner_sft"
+
+# ============================================================================
+# 可选配置 - 根据需要调整
+# ============================================================================
+
+# GPU 数量
+NUM_GPUS=8
+
+# 训练参数
+BATCH_SIZE=1
+GRAD_ACC_STEPS=4
+LEARNING_RATE="1.0e-05"
+NUM_EPOCHS=2
+WARMUP_RATIO=0.03
+
+# 视频处理参数
+VIDEO_MAX_FRAMES=768
+VIDEO_FPS=1
+USE_AUDIO_IN_VIDEO=true
+
+# 保存和日志
+SAVE_STEPS=500
+LOGGING_STEPS=10
+SAVE_TOTAL_LIMIT=3
+
+# WandB 配置（可选）
+WANDB_PROJECT="omnireasoner_sft"
+WANDB_RUN_NAME="omnireasoner_sft_run1"
+
+# ============================================================================
+# 环境变量
+# ============================================================================
+
+export NCCL_BLOCKING_WAIT=0
+export TOKENIZERS_PARALLELISM=false
+export NCCL_DEBUG=INFO
+
+# HuggingFace（如需要）
+# export HF_TOKEN="<YOUR_HF_TOKEN>"
+# export HF_HOME="$HOME/.cache/huggingface"
+# export HF_HUB_ENABLE_HF_TRANSFER="1"
+
+# WandB
+export WANDB_PROJECT=${WANDB_PROJECT}
+# export WANDB_API_KEY="<YOUR_WANDB_API_KEY>"
+
+# ============================================================================
+# 训练命令
+# ============================================================================
+
+CONFIG_PATH="$(dirname "$0")/omnireasoner_sft_config.yaml"
+
+torchrun \
+    --nproc_per_node="${NUM_GPUS}" \
+    --nnodes="1" \
+    --node_rank="0" \
+    --master_addr="127.0.0.1" \
+    --master_port="29500" \
+    -m lmms_engine.launch.cli \
+    config_yaml=${CONFIG_PATH} \
+    dataset_config.dataset_path="${DATASET_PATH}" \
+    dataset_config.extra_kwargs.use_audio_in_video=${USE_AUDIO_IN_VIDEO} \
+    dataset_config.video_max_frames=${VIDEO_MAX_FRAMES} \
+    dataset_config.fps=${VIDEO_FPS} \
+    model_config.load_from_pretrained_path="${MODEL_PATH}" \
+    trainer_args.output_dir="${OUTPUT_DIR}" \
+    trainer_args.per_device_train_batch_size=${BATCH_SIZE} \
+    trainer_args.gradient_accumulation_steps=${GRAD_ACC_STEPS} \
+    trainer_args.learning_rate=${LEARNING_RATE} \
+    trainer_args.num_train_epochs=${NUM_EPOCHS} \
+    trainer_args.warmup_ratio=${WARMUP_RATIO} \
+    trainer_args.save_steps=${SAVE_STEPS} \
+    trainer_args.logging_steps=${LOGGING_STEPS} \
+    trainer_args.save_total_limit=${SAVE_TOTAL_LIMIT} \
+    trainer_args.run_name="${WANDB_RUN_NAME}"

configs/run_omnireasoner_sft_multinode.sh

@@ -0,0 +1,149 @@
+#!/bin/bash
+# OmniReasoner SFT 多节点训练启动脚本
+#
+# 用途: 在多节点上使用 lmms-engine 框架训练两阶段视频理解模型
+#
+# 使用方法:
+#   在 K8s 环境中通过环境变量自动配置：$WORLD_SIZE, $RANK, $MASTER_ADDR, $MASTER_PORT
+#   或手动设置：RANK=0 WORLD_SIZE=8 bash run_omnireasoner_sft_multinode.sh
+
+# source ${HOME_WORK}/liushuman-sg/miniconda3/etc/profile.d/conda.sh
+# conda init
+source ~/.bashrc
+# 激活一个有ffmpeg 后端的 conda环境
+conda activate chenyu_lmms_engine
+
+
+
+set -euo pipefail
+
+export PYTHONPATH=${OMNIREASONER_ROOT}/Omni-Reasoning/lmms-engine/src:${PYTHONPATH:-}
+
+# ============================================================================
+# 必需配置 - 请修改这些参数
+# ============================================================================
+
+# 数据路径（所有节点需要能访问，GT-003 最终 coldstart 合并数据）
+DATASET_PATH="${OMNIREASONER_ROOT}/data_factory/cold_start/gt004_sft_final_with_t25.jsonl"
+
+# 模型路径
+MODEL_PATH="${OMNIREASONER_ROOT}/model/Qwen2.5-Omni-Thinker-7B"
+
+# 输出目录（所有节点需要能访问）
+# OUTPUT_DIR="${OMNIREASONER_ROOT}/Omni-Reasoning/lmms-engine/output/omnireasoner_sft/$(date +%Y%m%d_%H%M%S)"
+
+# ============================================================================
+# 多节点配置 - 从环境变量读取（K8s 自动注入）
+# ============================================================================
+
+# 节点数量（从 K8s 环境变量读取）
+NUM_NODES=${WORLD_SIZE:-8}
+
+# 当前节点编号（从 K8s 环境变量读取）
+NODE_RANK=${RANK:-0}
+
+# 主节点地址（从 K8s 环境变量读取）
+MASTER_ADDR=${MASTER_ADDR:-"node0"}
+
+# 主节点端口（从 K8s 环境变量读取）
+MASTER_PORT=${MASTER_PORT:-29500}
+
+# 每个节点的 GPU 数量
+NUM_GPUS_PER_NODE=1
+
+# ============================================================================
+# 训练配置（统一放在 omnireasoner_sft_config.yaml 中）
+# ============================================================================
+
+# WandB
+export WANDB_PROJECT="omnireasoner_sft"
+export WANDB_NAME="omnireasoner_sft_multinode"
+# WandB 配置 - 实时监控
+export WANDB_MODE=online  # 改为 online 启用实时监控
+export WANDB_API_KEY="2f3e15b6b931d12a70cf8ae666aa5163906a2ccc"  # 如果未登录，需要设置 API key
+
+# ============================================================================
+# 环境变量（参照 run_sft_coldstart.sh 的 anti-hang 配置）
+# ============================================================================
+
+# Debug 模式
+export DEBUG_MODE="True"
+
+# NCCL 配置（防止超时）
+export NCCL_BLOCKING_WAIT=0
+export TOKENIZERS_PARALLELISM=false
+export NCCL_DEBUG=INFO
+export NCCL_TIMEOUT=7200
+
+# Torch 异步错误处理（获得更好的错误信息）
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+
+# 限制 CPU 线程并行度（减少音视频解码竞争）
+# export OMP_NUM_THREADS=2
+# export MKL_NUM_THREADS=2
+# export OPENBLAS_NUM_THREADS=2
+# export TORCHCODEC_NUM_THREADS=2
+
+# 使用同步采样器（确保所有 rank 同步处理样本）
+export SYNC_SAMPLER=1
+
+# 启用 mm-info 预取
+export MMINFO_PREFETCH=1
+
+# 网络设置（根据集群配置调整）
+# export NCCL_IB_DISABLE=1
+# export NCCL_SOCKET_IFNAME=eth0
+
+# 别让 numba 往 .venv/site-packages 写缓存，并且每个 rank 用独立缓存目录，避免互相抢
+export TMPDIR=${OMNIREASONER_ROOT}/.tmp
+export XDG_CACHE_HOME=${OMNIREASONER_ROOT}/.cache
+export NUMBA_CACHE_DIR=${OMNIREASONER_ROOT}/.cache/numba_${RANK}
+# export NUMBA_DISABLE_JIT=1
+mkdir -p "$TMPDIR" "$XDG_CACHE_HOME" "$NUMBA_CACHE_DIR"
+
+export TRANSFORMERS_OFFLINE=1
+
+# ============================================================================
+# 训练命令
+# ============================================================================
+
+CONFIG_PATH="$(dirname "$0")/omnireasoner_sft_config.yaml"
+
+# # 同步 output_dir 到 YAML（避免 FUSE 覆盖写问题）
+# python - <<PY
+# from pathlib import Path
+# import re
+# config_path = Path("${CONFIG_PATH}")
+# output_dir = "${OUTPUT_DIR}"
+# text = config_path.read_text()
+# new_text, n = re.subn(r"^\s*output_dir:\s*.*$", f"  output_dir: {output_dir}", text, flags=re.M)
+# if n == 0:
+#     raise SystemExit("output_dir not found in YAML")
+# config_path.write_text(new_text)
+# PY
+
+# 创建输出目录
+# mkdir -p "${OUTPUT_DIR}"
+
+echo "============================================"
+echo "SFT OmniReasoner 多节点配置："
+echo "  当前节点编号: NODE_RANK=${NODE_RANK}"
+echo "  总节点数: NNODES=${NUM_NODES}"
+echo "  主节点地址: ${MASTER_ADDR}:${MASTER_PORT}"
+echo "  每节点 GPU 数: ${NUM_GPUS_PER_NODE}"
+echo "  MODEL_PATH: ${MODEL_PATH}"
+echo "  DATASET_PATH: ${DATASET_PATH}"
+# echo "  OUTPUT_DIR: ${OUTPUT_DIR}"
+# echo "  LOG_PATH: ${LOG_PATH}"
+echo "============================================"
+
+torchrun \
+    --nproc_per_node="${NUM_GPUS_PER_NODE}" \
+    --nnodes="${NUM_NODES}" \
+    --node_rank="${NODE_RANK}" \
+    --master_addr="${MASTER_ADDR}" \
+    --master_port="${MASTER_PORT}" \
+    -m lmms_engine.launch.cli \
+    config_yaml=${CONFIG_PATH} \
+    dataset_config.dataset_path="${DATASET_PATH}"
+    # 2>&1 | tee -a "${OUTPUT_DIR}/log_node_${NODE_RANK}.log" && echo "Done."

configs/run_omnireasoner_sft_single_node_g4567_20260423_lmms_e3.sh

@@ -0,0 +1,78 @@
+#!/bin/bash
+# OmniReasoner SFT 单机 4 卡训练启动脚本
+#
+# 使用本机 GPU 4,5,6,7 训练
+
+# source ${HOME_WORK}/liushuman-sg/miniconda3/etc/profile.d/conda.sh
+# conda init
+source ~/.bashrc
+conda activate chenyu_lmms_engine
+
+set -euo pipefail
+
+export PYTHONPATH=${OMNIREASONER_ROOT}/Omni-Reasoning/lmms-engine/src:${PYTHONPATH:-}
+
+# ============================================================================
+# 必需配置
+# ============================================================================
+
+MODEL_PATH="${OMNIREASONER_ROOT}/model/Qwen2.5-Omni-Thinker-7B"
+
+# ============================================================================
+# 单机 4 卡配置
+# ============================================================================
+
+# export CUDA_VISIBLE_DEVICES=4,5,6,7
+NUM_NODES=1
+NODE_RANK=0
+MASTER_ADDR="127.0.0.1"
+MASTER_PORT=${MASTER_PORT:-29500}
+NUM_GPUS_PER_NODE=4
+
+# ============================================================================
+# 训练配置
+# ============================================================================
+
+export WANDB_PROJECT="omnireasoner_sft"
+export WANDB_NAME="omnireasoner_sft_single_node_g4567_20260423_lmms_e3"
+export WANDB_MODE=online
+export WANDB_API_KEY="2f3e15b6b931d12a70cf8ae666aa5163906a2ccc"
+
+export DEBUG_MODE="True"
+export NCCL_BLOCKING_WAIT=0
+export TOKENIZERS_PARALLELISM=false
+export NCCL_DEBUG=INFO
+export NCCL_TIMEOUT=7200
+export TORCH_NCCL_ASYNC_ERROR_HANDLING=1
+export SYNC_SAMPLER=1
+export MMINFO_PREFETCH=1
+
+export TMPDIR=${OMNIREASONER_ROOT}/.tmp
+export XDG_CACHE_HOME=${OMNIREASONER_ROOT}/.cache
+export NUMBA_CACHE_DIR=${OMNIREASONER_ROOT}/.cache/numba_local
+mkdir -p "$TMPDIR" "$XDG_CACHE_HOME" "$NUMBA_CACHE_DIR"
+
+export TRANSFORMERS_OFFLINE=1
+
+CONFIG_PATH="$(dirname "$0")/omnireasoner_sft_config.yaml"
+
+echo "============================================"
+echo "SFT OmniReasoner 单机配置："
+echo "  CUDA_VISIBLE_DEVICES: ${CUDA_VISIBLE_DEVICES}"
+echo "  当前节点编号: NODE_RANK=${NODE_RANK}"
+echo "  总节点数: NNODES=${NUM_NODES}"
+echo "  主节点地址: ${MASTER_ADDR}:${MASTER_PORT}"
+echo "  每节点 GPU 数: ${NUM_GPUS_PER_NODE}"
+echo "  MODEL_PATH: ${MODEL_PATH}"
+echo "  DATASET_PATH: from ${CONFIG_PATH}"
+echo "  TRAIN_STEPS: from ${CONFIG_PATH}"
+echo "============================================"
+
+torchrun \
+    --nproc_per_node="${NUM_GPUS_PER_NODE}" \
+    --nnodes="${NUM_NODES}" \
+    --node_rank="${NODE_RANK}" \
+    --master_addr="${MASTER_ADDR}" \
+    --master_port="${MASTER_PORT}" \
+    -m lmms_engine.launch.cli \
+    config_yaml=${CONFIG_PATH}

data/train.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0741e511e53513a3c991fa9d0048bcbdcb456c17ca26f120b8fca6cb540bc5e7
+size 131920003

manifests/dataset_stats.json

@@ -0,0 +1,59 @@
+{
+  "num_examples": 25839,
+  "dataset_type_counts": {
+    "anomaly": 5319,
+    "avqa_r1": 2988,
+    "cgbench": 1729,
+    "composition": 13222,
+    "finevideo_online_tooluse": 2581
+  },
+  "problem_type_counts": {
+    "anomaly_detection": 5319,
+    "multiple_choice": 9989,
+    "open_ended": 10531
+  },
+  "num_media_files": 17915,
+  "media_bytes": 2631023855534,
+  "num_required_train_media_files": 16755,
+  "num_optional_source_media_files": 1160,
+  "num_unavailable_source_references": 33559,
+  "media_by_type": {
+    "audio": {
+      "files": 2988,
+      "bytes": 2860639814
+    },
+    "image": {
+      "files": 2988,
+      "bytes": 133465056
+    },
+    "video": {
+      "files": 11939,
+      "bytes": 2628029750664
+    }
+  },
+  "media_by_source_family": {
+    "anomaly": {
+      "files": 5326,
+      "bytes": 1694454993375
+    },
+    "avqa_r1": {
+      "files": 5976,
+      "bytes": 2994104870
+    },
+    "cgbench": {
+      "files": 1219,
+      "bytes": 351843453216
+    },
+    "composition": {
+      "files": 2903,
+      "bytes": 540517522885
+    },
+    "finevideo_online_tooluse": {
+      "files": 2491,
+      "bytes": 41213781188
+    }
+  },
+  "input_jsonl_basename": "gt004_sft_final_with_t25polish_t26_cgfix_nobad_v2_20260423_lmms.jsonl",
+  "path_policy": "Original local absolute paths are not included in the uploaded JSONL; media paths are repo-relative.",
+  "hash_policy": "sha256 is left null to avoid a second full read of multi-terabyte media during staging; Hugging Face upload performs its own file hashing."
+}

scripts/materialize_lmms_jsonl.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+
+MEDIA_KEYS = {
+    "video_url": "url",
+    "image_url": "url",
+    "audio_url": "url",
+}
+
+
+def to_file_url(root: Path, value: str) -> str:
+    if value.startswith("file://"):
+        value = value[7:]
+    if value.startswith("media/"):
+        return "file://" + str((root / value).resolve())
+    return value
+
+
+def rewrite(obj: Any, root: Path) -> Any:
+    if isinstance(obj, dict):
+        out = {}
+        for key, value in obj.items():
+            if key in MEDIA_KEYS and isinstance(value, dict):
+                nested = dict(value)
+                url_key = MEDIA_KEYS[key]
+                if isinstance(nested.get(url_key), str):
+                    nested[url_key] = to_file_url(root, nested[url_key])
+                out[key] = nested
+            elif key in {"video_path", "image_path", "audio_path"} and isinstance(value, str):
+                out[key] = str((root / value).resolve()) if value.startswith("media/") else value
+            else:
+                out[key] = rewrite(value, root)
+        return out
+    if isinstance(obj, list):
+        return [rewrite(item, root) for item in obj]
+    return obj
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input", required=True)
+    parser.add_argument("--output", required=True)
+    parser.add_argument("--root", default=".")
+    args = parser.parse_args()
+
+    root = Path(args.root).resolve()
+    with open(args.input, "r", encoding="utf-8") as src, open(args.output, "w", encoding="utf-8") as dst:
+        for line in src:
+            if not line.strip():
+                continue
+            dst.write(json.dumps(rewrite(json.loads(line), root), ensure_ascii=False) + "\n")
+
+
+if __name__ == "__main__":
+    main()

source_metadata/derived_anomaly/dataset_scaled/config.json

@@ -0,0 +1,18 @@
+{
+  "anomaly_types": [
+    "audio_only",
+    "video_only",
+    "both"
+  ],
+  "anomaly_ratios": [
+    0.2,
+    0.25,
+    0.5
+  ],
+  "min_anomaly_duration": 60.0,
+  "max_anomaly_duration": 1000.0,
+  "max_overall_duration": 1800.0,
+  "random_seed": 42,
+  "variants_per_type": 3,
+  "max_retries_per_variant": 3
+}

source_metadata/derived_anomaly/dataset_scaled/statistics.json

@@ -0,0 +1,14 @@
+{
+  "total_samples": 9614,
+  "anomaly_type_distribution": {
+    "audio_only": 3653,
+    "both": 3657,
+    "video_only": 2304
+  },
+  "anomaly_duration_stats": {
+    "min": 61.0,
+    "max": 180.47,
+    "mean": 143.76,
+    "median": 146.24
+  }
+}

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