Instructions to use ve-nk-at/stack_exc_multilabel_base_class_head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ve-nk-at/stack_exc_multilabel_base_class_head with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("google/gemma-2-2b") model = PeftModel.from_pretrained(base_model, "ve-nk-at/stack_exc_multilabel_base_class_head") - Transformers
How to use ve-nk-at/stack_exc_multilabel_base_class_head with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ve-nk-at/stack_exc_multilabel_base_class_head", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from ve-nk-at/stack_exc_multilabel_base_class_head: direct link, hf CLI and curl.
- Browser
- Download file 2.55 kB
-
https://huggingface.co/ve-nk-at/stack_exc_multilabel_base_class_head/resolve/main/trainer_state.json
- Command line
-
hf download hf://ve-nk-at/stack_exc_multilabel_base_class_head/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/ve-nk-at/stack_exc_multilabel_base_class_head/resolve/main/trainer_state.json
2.55 kB
| { | |
| "best_global_step": 80, | |
| "best_metric": 0.6729883436736228, | |
| "best_model_checkpoint": "/content/gemma_lora_imb/checkpoint-80", | |
| "epoch": 1.5686274509803921, | |
| "eval_steps": 20, | |
| "global_step": 80, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.39215686274509803, | |
| "grad_norm": 12.482938766479492, | |
| "learning_rate": 8.137254901960784e-06, | |
| "loss": 1.2092, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.39215686274509803, | |
| "eval_f1_macro": 0.49173730322672554, | |
| "eval_loss": 0.822236180305481, | |
| "eval_runtime": 3.4673, | |
| "eval_samples_per_second": 58.546, | |
| "eval_steps_per_second": 2.019, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.7843137254901961, | |
| "grad_norm": 34.74234390258789, | |
| "learning_rate": 6.176470588235295e-06, | |
| "loss": 0.7317, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 0.7843137254901961, | |
| "eval_f1_macro": 0.6225186035727692, | |
| "eval_loss": 0.7549954056739807, | |
| "eval_runtime": 3.4442, | |
| "eval_samples_per_second": 58.939, | |
| "eval_steps_per_second": 2.032, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 1.1764705882352942, | |
| "grad_norm": 11.875113487243652, | |
| "learning_rate": 4.215686274509805e-06, | |
| "loss": 0.5794, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 1.1764705882352942, | |
| "eval_f1_macro": 0.6456221198156682, | |
| "eval_loss": 0.6300039887428284, | |
| "eval_runtime": 3.4467, | |
| "eval_samples_per_second": 58.897, | |
| "eval_steps_per_second": 2.031, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 1.5686274509803921, | |
| "grad_norm": 9.780080795288086, | |
| "learning_rate": 2.254901960784314e-06, | |
| "loss": 0.4269, | |
| "step": 80 | |
| }, | |
| { | |
| "epoch": 1.5686274509803921, | |
| "eval_f1_macro": 0.6729883436736228, | |
| "eval_loss": 0.6284839510917664, | |
| "eval_runtime": 3.4549, | |
| "eval_samples_per_second": 58.757, | |
| "eval_steps_per_second": 2.026, | |
| "step": 80 | |
| } | |
| ], | |
| "logging_steps": 20, | |
| "max_steps": 102, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 2, | |
| "save_steps": 20, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 1.4138200998448128e+16, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |