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name: training-and-data description: "Use PixelRAG's separate training project, released LoRA adapters, and synthetic data pipeline guidance safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PixelRAG Training and Data

Use this sub-skill when the task asks about PixelRAG LoRA fine-tuning, the released screenshot embedding adapter, training/eval datasets, synthetic query generation, hard-negative mining, JSONL formats, or the separate train/ uv project.

Start Here

1. Decide whether the user needs the released model or full retraining.
   • For most usage, load the published LoRA adapter instead of retraining.
   • Full retraining is a multi-GPU/data/credential workflow.
2. Remember train/ is a separate uv project. Do not install it from the root package environment.
3. Confirm resource requirements before running anything heavy: A100/H100-class GPU, large disk, HF datasets, vLLM reader for eval, OpenAI/W&B/Google keys depending on stage.
4. Validate small data samples with pixelrag_training_data_check.py before launching generation/training scripts.

Read or Run

• Read training-recipe.md for environment, released adapter, training command shape, and evaluation expectations.
• Read data-pipeline.md for synthetic query generation, filters, hard negatives, export/split, and JSONL fields.
• Read troubleshooting.md for keys, data paths, CUDA, vLLM, W&B, and silent-zero eval failures.
• Run pixelrag_training_data_check.py on a JSONL sample before spending GPU/API budget.

Common Routes

Request	Action
"Use the trained PixelRAG embedding"	Load Chrisyichuan/wiki-screenshot-embedding-lora adapter on top of Qwen/Qwen3-VL-Embedding-2B; no retraining needed.
"Reproduce the training run"	Use the separate train/ uv env, required datasets, vLLM reader, OpenAI grader, and W&B/offline logging plan.
"Generate synthetic data"	Follow query generation -> self-contained filter -> hard-negative mining -> VQA false-negative filter -> naturalness scoring -> export/split -> HF packaging.
"Debug QA score is zero"	Check OpenAI key/base URL and reader endpoint; training may be fine while eval grading fails.
"Evaluate a checkpoint"	Use training eval scripts and route paper-level benchmark comparison to ../evaluation-reproduction/SKILL.md.

Boundaries

• Do not call Google/OpenAI, upload to HF, or start W&B/vLLM/training jobs without explicit user approval.
• Do not mix root PixelRAG package extras with the pinned train/ project environment.
• Do not claim CPU is a substitute for the training run; CPU may validate JSONL shape but not training performance.
• Keep local data roots, tokens, W&B run URLs, and API keys out of reusable notes unless the user explicitly asks for local operational commands.