Instructions to use clfegg/image_embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use clfegg/image_embed with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-feature-extraction', 'clfegg/image_embed');
Download config.json from clfegg/image_embed: direct link, hf CLI and curl.
- Browser
- Download file 627 Bytes
-
https://huggingface.co/clfegg/image_embed/resolve/main/config.json
- Command line
-
hf download hf://clfegg/image_embed/config.json
-
curl -L -o config.json https://huggingface.co/clfegg/image_embed/resolve/main/config.json
627 Bytes
| { | |
| "_attn_implementation_autoset": true, | |
| "_name_or_path": "../clip-ViT-B-32\\0_CLIPModel", | |
| "architectures": [ | |
| "CLIPModel" | |
| ], | |
| "export_model_type": "clip", | |
| "initializer_factor": 1.0, | |
| "logit_scale_init_value": 2.6592, | |
| "model_type": "clip", | |
| "projection_dim": 512, | |
| "text_config": { | |
| "bos_token_id": 0, | |
| "dropout": 0.0, | |
| "eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "model_type": "clip_text_model" | |
| }, | |
| "transformers_version": "4.46.1", | |
| "vision_config": { | |
| "dropout": 0.0, | |
| "gradient_checkpointing": false, | |
| "model_type": "clip_vision_model" | |
| } | |
| } | |