Instructions to use arpanl/finetuned_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpanl/finetuned_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="arpanl/finetuned_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("arpanl/finetuned_model") model = AutoModelForImageClassification.from_pretrained("arpanl/finetuned_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from arpanl/finetuned_model: direct link, hf CLI and curl.
- Browser
- Download file 669 Bytes
-
https://huggingface.co/arpanl/finetuned_model/resolve/main/trainer_state.json
- Command line
-
hf download hf://arpanl/finetuned_model/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/arpanl/finetuned_model/resolve/main/trainer_state.json
669 Bytes
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 50.0, | |
| "eval_steps": 1000, | |
| "global_step": 100, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 50.0, | |
| "step": 100, | |
| "total_flos": 1.084994813177856e+17, | |
| "train_loss": 0.5841025924682617, | |
| "train_runtime": 1156.5484, | |
| "train_samples_per_second": 1.21, | |
| "train_steps_per_second": 0.086 | |
| } | |
| ], | |
| "logging_steps": 1000, | |
| "max_steps": 100, | |
| "num_train_epochs": 50, | |
| "save_steps": 1000, | |
| "total_flos": 1.084994813177856e+17, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |