Instructions to use HuggingFaceM4/tiny-random-siglip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceM4/tiny-random-siglip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="HuggingFaceM4/tiny-random-siglip", trust_remote_code=True) 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("HuggingFaceM4/tiny-random-siglip", trust_remote_code=True) model = AutoModelForZeroShotImageClassification.from_pretrained("HuggingFaceM4/tiny-random-siglip", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| Tiny random Siglip model. For testing purposes only. | |
| Script used to create this tiny random model: | |
| ```python | |
| from transformers import AutoConfig, AutoModel | |
| config = AutoConfig.from_pretrained("HuggingFaceM4/siglip-so400m-14-384", trust_remote_code=True) | |
| config._name_or_path = 'HuggingFaceM4/tiny-random-siglip' | |
| config.text_config.hidden_size = int(config.text_config.hidden_size/8) | |
| config.text_config.intermediate_size = int(config.text_config.intermediate_size/8) | |
| config.text_config.num_attention_heads = int(config.text_config.num_attention_heads/8) | |
| config.text_config.num_hidden_layers = 3 | |
| config.text_config.projection_dim = int(config.text_config.projection_dim/8) | |
| config.vision_config.hidden_size = int(config.vision_config.hidden_size/8) | |
| config.vision_config.image_size = 30 | |
| config.vision_config.intermediate_size = int(config.vision_config.intermediate_size/8) | |
| config.vision_config.num_attention_heads = int(config.vision_config.num_attention_heads/8) | |
| config.vision_config.num_hidden_layers = 3 | |
| config.vision_config.patch_size = 2 | |
| config.vision_config.projection_dim = int(config.vision_config.projection_dim/8) | |
| config.auto_map = { | |
| "AutoConfig": "HuggingFaceM4/tiny-random-siglip--configuration_siglip.SiglipConfig", | |
| "AutoModel": "HuggingFaceM4/tiny-random-siglip--modeling_siglip.SiglipModel" | |
| } | |
| config.save_pretrained("./tiny-random-siglip") | |
| model = AutoModel.from_pretrained("HuggingFaceM4/siglip-so400m-14-384", trust_remote_code=True) | |
| SiglipModel = model.__class__ | |
| new_model = SiglipModel(config) | |
| new_model.save_pretrained("./tiny-random-siglip") | |
| ``` |