Instructions to use CodeIsAbstract/HybridModelScratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeIsAbstract/HybridModelScratch with Transformers:
# Load model directly from transformers import HybridFourierLM model = HybridFourierLM.from_pretrained("CodeIsAbstract/HybridModelScratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.15, | |
| "eval_steps": 100, | |
| "global_step": 300, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.0125, | |
| "grad_norm": 7352.2265625, | |
| "learning_rate": 1.6000000000000003e-05, | |
| "loss": 175.5381, | |
| "step": 25 | |
| }, | |
| { | |
| "epoch": 0.025, | |
| "grad_norm": 2155.41015625, | |
| "learning_rate": 3.266666666666667e-05, | |
| "loss": 174.6277, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.0375, | |
| "grad_norm": 29.340621948242188, | |
| "learning_rate": 4.933333333333334e-05, | |
| "loss": 157.9852, | |
| "step": 75 | |
| }, | |
| { | |
| "epoch": 0.05, | |
| "grad_norm": 11.753185272216797, | |
| "learning_rate": 6.6e-05, | |
| "loss": 133.1257, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.05, | |
| "eval_accuracy": 0.08756986554511534, | |
| "eval_loss": 7.706512928009033, | |
| "eval_runtime": 7.3236, | |
| "eval_samples_per_second": 66.224, | |
| "eval_steps_per_second": 2.185, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.0625, | |
| "grad_norm": 11.1610746383667, | |
| "learning_rate": 8.266666666666667e-05, | |
| "loss": 119.0136, | |
| "step": 125 | |
| }, | |
| { | |
| "epoch": 0.075, | |
| "grad_norm": 7.337241172790527, | |
| "learning_rate": 9.933333333333334e-05, | |
| "loss": 113.2304, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 0.0875, | |
| "grad_norm": 9.895074844360352, | |
| "learning_rate": 9.995847987378953e-05, | |
| "loss": 109.5068, | |
| "step": 175 | |
| }, | |
| { | |
| "epoch": 0.1, | |
| "grad_norm": 8.935585975646973, | |
| "learning_rate": 9.982700328363471e-05, | |
| "loss": 107.0186, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 0.1, | |
| "eval_accuracy": 0.13644318107230674, | |
| "eval_loss": 6.609853267669678, | |
| "eval_runtime": 7.4218, | |
| "eval_samples_per_second": 65.348, | |
| "eval_steps_per_second": 2.156, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 0.1125, | |
| "grad_norm": 17.207761764526367, | |
| "learning_rate": 9.96057350657239e-05, | |
| "loss": 105.3798, | |
| "step": 225 | |
| }, | |
| { | |
| "epoch": 0.125, | |
| "grad_norm": 37.589744567871094, | |
| "learning_rate": 9.929507396034141e-05, | |
| "loss": 103.9933, | |
| "step": 250 | |
| }, | |
| { | |
| "epoch": 0.1375, | |
| "grad_norm": 48.09913635253906, | |
| "learning_rate": 9.889557979979694e-05, | |
| "loss": 103.0857, | |
| "step": 275 | |
| }, | |
| { | |
| "epoch": 0.15, | |
| "grad_norm": 18.950061798095703, | |
| "learning_rate": 9.840797249956985e-05, | |
| "loss": 102.3462, | |
| "step": 300 | |
| }, | |
| { | |
| "epoch": 0.15, | |
| "eval_accuracy": 0.14476797317970908, | |
| "eval_loss": 6.367699146270752, | |
| "eval_runtime": 7.5111, | |
| "eval_samples_per_second": 64.571, | |
| "eval_steps_per_second": 2.13, | |
| "step": 300 | |
| } | |
| ], | |
| "logging_steps": 25, | |
| "max_steps": 2000, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 9223372036854775807, | |
| "save_steps": 300, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 2.006672071458816e+17, | |
| "train_batch_size": 64, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |