Instructions to use mllm-dev/gpt2_f_experiment_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_2") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.8025827407836914, | |
| "best_model_checkpoint": "sean_test_out/checkpoint-903", | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 903, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.55, | |
| "grad_norm": 418741.09375, | |
| "learning_rate": 2.6777408637873754e-05, | |
| "loss": 0.975, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.6483, | |
| "eval_loss": 0.8025827407836914, | |
| "eval_runtime": 33.2114, | |
| "eval_samples_per_second": 301.102, | |
| "eval_steps_per_second": 4.185, | |
| "step": 903 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 903, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 500, | |
| "total_flos": 2.650663586377728e+16, | |
| "train_batch_size": 72, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |