| --- |
| tags: |
| - robotics |
| - latent-action-model |
| - alam |
| library_name: pytorch |
| --- |
| |
| # ALAM pretrained tokenizer checkpoints |
|
|
| Repository ID: `Mark-ZJTang/alam_pretrain`. |
|
|
| This repository contains the two ALAM v3 tokenizer checkpoints used by the |
| released downstream evaluations. Both use 7 latent-action slots, a 256-entry |
| codebook, and 128-dimensional latent vectors. |
|
|
| | Directory | Downstream use | Training epoch/step | |
| | --- | --- | --- | |
| | `metaworld_epoch19_step58216` | MetaWorld MT50 | epoch 19, step 58,216 | |
| | `libero_epoch16_step49024` | LIBERO Table 9 shared-checkpoint configuration | epoch 16, step 49,024 | |
|
|
| Each directory contains `config.yaml` and `pytorch_model.bin`. Verify the files |
| against `evaluation/WEIGHTS_MANIFEST.sha256` in the GitHub code release. License |
| metadata must be completed by the copyright owner before public publication. |
|
|
| From the matching GitHub code checkout, restore both checkpoints and run strict |
| CPU structure checks with: |
|
|
| ```bash |
| .venvs/publish/bin/python workflows/publishing/download_huggingface.py \ |
| --artifact alam_pretrain |
| bash workflows/alam_pretraining/evaluate_checkpoint.sh cpu |
| bash workflows/alam_pretraining/evaluate_checkpoint.sh cpu \ |
| --checkpoint evaluation/checkpoints/alam/libero_epoch16_step49024 |
| ``` |
|
|
| Use `evaluate_checkpoint.sh cuda` for real encoder execution; the preserved |
| encoder has a historical internal CUDA device assumption. |
|
|
| The project-owner resource specification for full ALAM pretraining is 128 |
| NVIDIA H20 GPUs (one process per GPU), with per-GPU batch 32 and gradient |
| accumulation 2. Historical directory labels containing `64gpu` are provenance |
| names, not the public resource contract. |
|
|
