Instructions to use zabir735/clip-zabir-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zabir735/clip-zabir-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="zabir735/clip-zabir-2") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("zabir735/clip-zabir-2") model = AutoModelForZeroShotImageClassification.from_pretrained("zabir735/clip-zabir-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 558, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 2.69, | |
| "learning_rate": 5.197132616487455e-06, | |
| "loss": 2.0523, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "step": 558, | |
| "total_flos": 256008176490528.0, | |
| "train_loss": 2.0468607529944416, | |
| "train_runtime": 2493.1865, | |
| "train_samples_per_second": 1.79, | |
| "train_steps_per_second": 0.224 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 558, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "total_flos": 256008176490528.0, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |