Instructions to use teawhite/ActionRoPE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Wan2.2
How to use teawhite/ActionRoPE with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 893 Bytes
880dff9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | # 4 卡(自测用,配合 CUDA_VISIBLE_DEVICES=0,1,2,3) DeepSpeed ZeRO-2,bf16,无 offload,梯度裁剪 1.0,梯度累积 1。
# train_micro_batch_size_per_gpu 由 accelerate 填 "auto" ⇒ 取 train.py 里 DataLoader 的 batch_size(默认 1)。
# 不用 deepspeed_config_file:accelerate 1.14 里它与 mixed_precision 等键互斥,yaml 原生字段更省事。
compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
gradient_accumulation_steps: 1
gradient_clipping: 1.0
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: false
zero_stage: 2
distributed_type: DEEPSPEED
downcast_bf16: 'no'
enable_cpu_affinity: false
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
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