Feature Extraction
Transformers
ONNX
multimodal
embeddings
edge
quantization
clip
text-embedding
image-embedding
video-embedding
audio-embedding
mobile
arm64
int8
Instructions to use blckrvrfx/edge-multimodal-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blckrvrfx/edge-multimodal-embeddings with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="blckrvrfx/edge-multimodal-embeddings")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("blckrvrfx/edge-multimodal-embeddings", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download edge_multimodal_embeddings/cli.py from blckrvrfx/edge-multimodal-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 1.56 kB
-
https://huggingface.co/blckrvrfx/edge-multimodal-embeddings/resolve/main/edge_multimodal_embeddings/cli.py
- Command line
-
hf download hf://blckrvrfx/edge-multimodal-embeddings/edge_multimodal_embeddings/cli.py
-
curl -L -o cli.py https://huggingface.co/blckrvrfx/edge-multimodal-embeddings/resolve/main/edge_multimodal_embeddings/cli.py
1.56 kB
| import argparse, json, time, numpy as np | |
| def main(): | |
| p = argparse.ArgumentParser(prog="edge-embed") | |
| p.add_argument("--profile", default="edge_gpu") | |
| p.add_argument("--device", default="auto") | |
| sub = p.add_subparsers(dest="cmd") | |
| ep = sub.add_parser("embed"); ep.add_argument("input"); ep.add_argument("--modality", default="auto") | |
| cp = sub.add_parser("compare"); cp.add_argument("a"); cp.add_argument("b") | |
| bp = sub.add_parser("benchmark"); bp.add_argument("-n", type=int, default=50) | |
| sub.add_parser("info") | |
| args = p.parse_args() | |
| if not args.cmd: p.print_help(); return | |
| import logging; logging.basicConfig(level=logging.INFO) | |
| from edge_multimodal_embeddings.core.config import EmbedderConfig, HardwareProfile | |
| from edge_multimodal_embeddings.core.embedder import EdgeMultimodalEmbedder | |
| cfg = EmbedderConfig.from_profile(HardwareProfile(args.profile)) | |
| if args.device != "auto": cfg.device = args.device | |
| emb = EdgeMultimodalEmbedder(cfg) | |
| if args.cmd == "embed": | |
| t0 = time.time(); e = emb.embed(args.input, modality=args.modality) | |
| print(f"Shape: {e.shape} | Norm: {np.linalg.norm(e):.4f} | {(time.time()-t0)*1000:.0f}ms") | |
| elif args.cmd == "compare": | |
| print(f"Similarity: {emb.similarity(emb.embed(args.a), emb.embed(args.b)):.4f}") | |
| elif args.cmd == "benchmark": print(json.dumps(emb.benchmark(args.n), indent=2, default=str)) | |
| elif args.cmd == "info": print(json.dumps(emb.get_status(), indent=2, default=str)) | |
| emb.unload() | |
| if __name__ == "__main__": main() |