AI Model Training Pipeline Designer — Plan, configure, and export your complete fine-tuning pipeline
Define where your training data comes from and how much you need. Quality starts here.
337K raw conversations. Loss reached 1.087. Result: model was POISONED by junk data. Identity collapsed under noise. Lesson: garbage in, garbage out — even at scale.
Rebuilt from scratch. 25K clean conversations from 337K raw. 600+ skills mapped. Zero junk tolerance. The clean dataset became the foundation for everything after.
Trained on clean data but tool format broke. Discovered the model's native template must be respected — you can't force alien formats onto a pretrained model.
100/100/100 on all eval gates using native Qwen template + system prompt. But this was Qwen's weights, not ours. The format worked — now could LoRA preserve it?
Training on RunPod A100. Uses Qwen native <tool_call> XML format. If format survives LoRA training, vertical integration is achieved — YOUR model, YOUR weights, YOUR format. Loss target: 0.037.
Configure quality filters. Watch your dataset shrink to gold. The RLL path: 337K raw became 25K clean (7.4% survival rate).
Configure your filters above to see your quality score.
Choose and configure the conversation format your model will learn. V3 lesson: always match the base model's native format.
Configure Low-Rank Adaptation parameters. Each setting has a direct impact on training quality, speed, and VRAM usage.
| Metric | Value |
|---|---|
| Estimated VRAM Required | — |
| VRAM Available | — |
| VRAM Fit | — |
| Training Steps | — |
| Estimated Duration | — |
| Cost per Hour | — |
| Total Estimated Cost | — |
Define pass/fail gates for your trained model. V4 achieved 100/100/100 across all gates. Set your thresholds and test categories.
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