Martin Jaggi, Atli Kosson, Alexander Hägele, Elie Bakouch, Loubna Allal, Leandro Werra
We lifted 13 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| epfml/schedules-and-scaling | canonical | 3 of 8 |
| frotaur/icmlbackperp | canonical | 3 of 4 |
| fabian-sp/lr-scheduling | extension | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| calculate_slope | Ran | fabian-sp/lr-scheduling/reanalysis/analysis_horizon_transfer.py code served (permissive licence) · get_code("c2122ff2161f0942") |
| move_to_cpu | Ran | epfml/schedules-and-scaling/src/logger/logger.py code served (permissive licence) · get_code("1bd86a911c3f78a3") |
| multiget | Ran | frotaur/icmlbackperp/modules/tokenizer.py pointer only (licence: NONE) · get_code("db9d402a23bb81a3") |
| precompute_freqs_cis | Ran | epfml/schedules-and-scaling/src/models/llama.py code served (permissive licence) · get_code("a1d6f89d43fc42e1") |
| put_and_return | Ran | frotaur/icmlbackperp/modules/tokenizer.py pointer only (licence: NONE) · get_code("e21c63d05fe20e51") |
| remove_none_vals | Ran | frotaur/icmlbackperp/modules/tokenizer.py pointer only (licence: NONE) · get_code("b5644fc0a380fa78") |
| self_preserving_overwrite | Ran | epfml/schedules-and-scaling/src/logger/logger.py code served (permissive licence) · get_code("198aea734522a443") |
| apply_rotary_emb | Not yet run | epfml/schedules-and-scaling/src/models/llama.py code served (permissive licence) · get_code("9891174b43bd9c9a") |
| cos_inf_schedule | Not yet run | epfml/schedules-and-scaling/src/optim/utils.py code served (permissive licence) · get_code("91d9a7a7d597054b") |
| get_batch | Not yet run | epfml/schedules-and-scaling/src/optim/utils.py code served (permissive licence) · get_code("f9dc20d4ad58a5f9") |
| get_model | Not yet run | epfml/schedules-and-scaling/src/models/utils.py code served (permissive licence) · get_code("843859340885ab12") |
| load_model | Not yet run | frotaur/icmlbackperp/modules/models/load_model.py pointer only (licence: NONE) · get_code("98497683abdc8f17") |
| wsd_schedule | Not yet run | epfml/schedules-and-scaling/src/optim/utils.py code served (permissive licence) · get_code("e8ac769090ecbe45") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Scale has become a main ingredient in obtaining strong machine learning models. As a result, understanding a model's scaling properties is key to effectively designing both the right training setup as well as future generations of architectures. In this work, we argue that scale and training research has been needlessly complex due to reliance on the cosine schedule, which prevents training across different lengths for the same model size. We investigate the training behavior of a direct alternative -constant learning rate and cooldowns -and find that it scales predictably and reliably similar to cosine. Additionally, we show that stochastic weight averaging yields improved performance along the training trajectory, without additional training costs, across different scales. Importantly, with these findings we demonstrate that scaling experiments can be performed with significantly reduced compute and GPU hours by utilizing fewer but reusable training runs. Our code is available at https://github.com/epfml/schedules-and-scaling/.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.18392")
get_code_for_paper("2405.18392")
have("2405.18392")
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