ZhuofengLi/megatron-configs
Training model configurations This directory records model architecture configurations used by training examples or imported from reference experiments. These files preserve model structure and provenance; they do not automatically configure or launch a Megatron Bridge training job. Configurations The H100 entries below are restricted to runs pinned in the RSI-Index/ale-rsi-pre-training workspace. Nominal Compute Model Parameters Actual Tokens Training GPUs… See the full description on the dataset page: https://huggingface.co/datasets/ZhuofengLi/megatron-configs.
Training model configurations
This directory records model architecture configurations used by training examples or imported from reference experiments. These files preserve model structure and provenance; they do not automatically configure or launch a Megatron Bridge training job.
Configurations
The H100 entries below are restricted to runs pinned in the `RSI-Index/ale-rsi-pre-training` workspace.
Pure training time is computed from the median stable iteration time after step 100 multiplied by the configured training steps; it excludes LM Evaluation Harness. End-to-end time includes Harness evaluations, checkpointing, initialization, and final sync, but excludes scheduler queue time. Run 76xmgi2r did not finish, so its full-run values are projections from the observed iterations. Run yc0dfezu lost W&B connectivity at step 26,050, while the same training job completed all 47,064 steps in 24.49 hours according to the scheduler and final success marker.
All pinned H100 layouts use TP=PP=CP=1, no activation recomputation, and fused gradient accumulation. The 550M pinned run has activation_offload_fraction=1 in its W&B config despite nooffload appearing in its display name; all other pinned runs use zero activation offload. The Marin source configuration remains recorded separately in each YAML.
The Marin source YAML preserves the inherited model.use_qk_norm: false value emitted by LlamaConfig. These runs instantiate Qwen3Config, whose attention_config() overrides that inherited field and always installs learned per-head Q/K RMSNorm. Therefore each file records resolved.normalization: rmsnorm and resolved.effective_use_qk_norm: true to distinguish the effective model architecture from the serialized source field.
When adapting a reference configuration to Megatron Bridge, keep the source model fields unchanged unless the experiment explicitly studies architecture changes. Record training schedule, optimizer, data, and distributed parallelism separately because those settings are framework- and hardware-dependent.
