Files

T

Will LinandClaude Fable 5 a20b69a4a8 [train] MiniMax-H3 config suite: SFT sparsity sweep, data-free DMD2, 32-GPU Slurm launch, README

- fine_tuning/minimax_h3: FA4 dense control, VSA sparsity 0/0.8/0.9/0.95/0.97
  overfits, effective-batch-2 via gradient accumulation over two videos
  (validated: both losses decline, ~10 s/step on 4x GB200), and the 2-GPU
  configs kept as negative results (sp=2 backward working set OOMs 184 GiB).
- distribution_matching/minimax_h3: data-free DMD2 (rollout_mode: simulate)
  overfit and its 32-GPU scale-up (8 trays, sp=4, HSDP 8x4, global batch 64
  = 8 DP groups x accum 8).
- examples/train/slurm/dmd2_32xgb200.sbatch: login-node launch deriving
  world size/rendezvous from the allocation, per-node /dev/shm weight cache,
  per-node log dirs, CLUSTER-marked partition/account/container hooks.
- README.md in both config dirs: launch, topology/batch semantics, measured
  step times, memory findings, shard cache, preprocessing.
- pyproject: explicit setuptools include list.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

2026-08-11 04:41:49 +00:00

consistency_model/wan

[feat] Add Clean-history Teacher Forcing and Causal Consistency Distillation (#1505 )

2026-07-07 08:13:11 +00:00

distribution_matching

[train] MiniMax-H3 config suite: SFT sparsity sweep, data-free DMD2, 32-GPU Slurm launch, README

2026-08-11 04:41:49 +00:00

fine_tuning

[train] MiniMax-H3 config suite: SFT sparsity sweep, data-free DMD2, 32-GPU Slurm launch, README

2026-08-11 04:41:49 +00:00

knowledge_distillation/wan

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

rl/wan

[feat] Add Wan RL DiffusionNFT training (#1450 )

2026-06-11 21:18:59 -07:00

example.yaml

[feat] Enable LTX-2 NVFP4 linear and attention QAT fine-tuning (#1626 )

2026-07-26 02:19:34 -07:00

overfit_cosmos25_t2w.yaml

[feat] Cosmos 2.5 training support in fastvideo.train (#1224 )

2026-05-01 01:15:02 +00:00

overfit_hunyuan_t2v.yaml

[bugfix]: fix VAE temporal tiling blend corruption in tiled_encode (#1181 )

2026-03-29 23:12:42 +00:00

overfit_ltx2_3_t2v.yaml

[feat] Add LTX-2 and LTX-2.3 fine-tuning to the modular trainer (#1624 )

2026-07-20 11:58:39 -07:00

overfit_ltx2_t2v_nvfp4_qat.yaml

[feat]: FA4-FP4 ATTN_QAT_INFER on sm_100/sm_103 + NVFP4 weight purge (#1647 )

2026-07-27 15:46:52 -07:00

overfit_ltx2_t2v.yaml

[feat] Add LTX-2 and LTX-2.3 fine-tuning to the modular trainer (#1624 )

2026-07-20 11:58:39 -07:00

overfit_minimax_h3_t2va.yaml

[feat] add Minimax H3 sft pipeline (#1688 )

2026-08-07 16:12:04 -07:00

README.md

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

README.md

Training Configs

Single-step training configurations organized by method and model.

configs/
├── fine_tuning/          # Standard finetuning and DFSFT
├── distribution_matching/  # DMD2 and Self-Forcing
├── knowledge_distillation/ # KD from teacher to student
└── example.yaml          # Annotated reference config with all fields

Each method directory contains per-model subdirectories (e.g. wan/, hunyuan/).

Launch any config with:

bash examples/train/run.sh examples/train/configs/<method>/<model>/<config>.yaml

For multi-step training pipelines, see examples/train/scenario/.