Auto Recipe — Recipe Index & Recommendation

Auto Recipe — Recipe Index & Recommendation

This skill indexes every shipped recipe and helps users pick the right starting config, adjust parallelism, and avoid common pitfalls.

How to Use This Skill

1. Ask the user for: model name/size, GPU count & type, training goal (pretrain / SFT / PEFT), and sequence length (if non-default).
2. Look up the best-match recipe in the index below.
3. Recommend the recipe function name + entry-point command.
4. Provide adjustment advice (parallelism resizing, batch tuning, pitfalls).

First Answer Checklist

When recommending recipes, always include these distinctions before the long index details:

1. Library recipes under src/megatron/bridge/recipes/ are for functional training and use scripts/training/run_recipe.py.
2. Benchmark recipes under src/megatron/bridge/perf_recipes/ are for upper-bound throughput benchmarks. They own their canonical benchmark data and settings and should not be presented as production training recipes.
3. For a first-time Bridge smoke test, recommend llama3_8b_pretrain_config with mock data via --dataset mock.
4. For normal SFT recommendations, select a finetuning preset such as --dataset squad or --dataset tulu3; for pretrain and mock validation recommendations, use --dataset mock. Do not pair the pretraining-only mock preset with an SFT or PEFT mode.
5. After the recipe and dataset, give the required resizing rules: TP must divide num_key_value_heads, keep TP within one node unless using NVL72-class interconnect, enable SP when TP > 1, configure CP for long context, DP is implicit, and reduce micro_batch_size first on OOM.
6. State whether each proposed override changes the convergence contract or only the execution/performance mapping. Do not trade convergence semantics for throughput without calling it a new experiment.

Configuration Layers and Change Control

Separate training semantics from their hardware mapping before recommending or