train_micro_batch_size_per_gpu int64 1 8 | gradient_accumulation_steps int64 1 1 | gradient_clipping float64 1 1 | steps_per_print int64 1 25 | zero_optimization dict | bf16 dict | optimizer dict | activation_checkpointing dict | wall_clock_breakdown bool 1
class |
|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 1 | {
"stage": 2,
"offload_optimizer": {
"device": "none"
},
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 50000000,
"allgather_bucket_size": 50000000
} | {
"enabled": true
} | {
"type": "AdamW",
"params": {
"lr": 0.00005,
"betas": [
0.9,
0.999
],
"eps": 0.000001,
"weight_decay": 0
}
} | {
"partition_activations": false,
"cpu_checkpointing": false,
"contiguous_memory_optimization": false,
"number_checkpoints": null
} | false |
2 | 1 | 1 | 25 | {
"stage": 2,
"offload_optimizer": {
"device": "none"
},
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 50000000,
"allgather_bucket_size": 50000000
} | {
"enabled": true
} | {
"type": "AdamW",
"params": {
"lr": 0.00005,
"betas": [
0.9,
0.999
],
"eps": 1e-8,
"weight_decay": 0
}
} | {
"partition_activations": false,
"cpu_checkpointing": false,
"contiguous_memory_optimization": false,
"number_checkpoints": null
} | false |
6 | 1 | 1 | 25 | {
"stage": 2,
"offload_optimizer": {
"device": "none"
},
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 50000000,
"allgather_bucket_size": 50000000
} | {
"enabled": true
} | {
"type": "AdamW",
"params": {
"lr": 0.00005,
"betas": [
0.9,
0.999
],
"eps": 1e-8,
"weight_decay": 0
}
} | {
"partition_activations": false,
"cpu_checkpointing": false,
"contiguous_memory_optimization": false,
"number_checkpoints": null
} | false |
8 | 1 | 1 | 25 | {
"stage": 2,
"offload_optimizer": {
"device": "none"
},
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 50000000,
"allgather_bucket_size": 50000000
} | {
"enabled": true
} | {
"type": "AdamW",
"params": {
"lr": 0.00005,
"betas": [
0.9,
0.999
],
"eps": 1e-8,
"weight_decay": 0
}
} | {
"partition_activations": false,
"cpu_checkpointing": false,
"contiguous_memory_optimization": false,
"number_checkpoints": null
} | false |
GAM LIBERO Training Assets
This dataset repository contains the assets needed to fine-tune GAM on LIBERO.
Layout
checkpoints/track4world_da3.pth
data/libero_noop/<suite>/*.hdf5
data/libero_noop/_stats/*.json
configs/training/libero_unified/
track4world_da3.pth is the DA3-Giant base checkpoint. The LIBERO HDF5 files contain embedded RGB, proprioception, actions, and depth used by the public GAM training configs.
Code: https://github.com/JeonSeongHu/gam_test/tree/public/libero-da3giant Checkpoints: https://huggingface.co/SeonghuJeon/3da-libero-gam
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