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This repository contains the TinyRecursiveModels checkpoints for arc v1 public eval and arc v2 public eval that were trained for the performance verification. They were trained using the code and recipe of the [official TRM repository](https://github.com/SamsungSAILMontreal/TinyRecursiveModels). We had to adapt the environment setup as detailed below. We provide these checkpoints for transparency and to facilitate further research. We did not contribute to the TRM reserach nor maintain the TRM code. For any questions, please reach out to the TRM maintainers.
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TRM writes checkpoints as `torch state_dicts`. The subdirectories `arc_v1_public` and `arc_v2_public` contain the final checkpoints `step_<final-step>`, which can be loaded with the `load_checkpoint` or by providing the checkpoint path as `load_checkpoint=path/to/checkpoint`. For reference, see the `PretrainConfig` in `pretrain.py`.
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license: mit
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This repository contains the TinyRecursiveModels checkpoints for arc v1 public eval and arc v2 public eval that were trained for the performance verification. They were trained using the code and recipe of the [official TRM repository](https://github.com/SamsungSAILMontreal/TinyRecursiveModels). We had to adapt the environment setup as detailed below. We provide these checkpoints for transparency and to facilitate further research. We did not contribute to the TRM reserach nor maintain the TRM code. For any questions, please reach out to the TRM maintainers.
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TRM writes checkpoints as `torch state_dicts`. The subdirectories `arc_v1_public` and `arc_v2_public` contain the final checkpoints `step_<final-step>`, which can be loaded with the `load_checkpoint` or by providing the checkpoint path as `load_checkpoint=path/to/checkpoint`. For reference, see the `PretrainConfig` in `pretrain.py`.
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