Zero-Shot Image Classification
Transformers
Safetensors
sentence-transformers
mllama
image-text-to-text
mmeb
text-generation-inference
Instructions to use intfloat/mmE5-mllama-11b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intfloat/mmE5-mllama-11b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="intfloat/mmE5-mllama-11b-instruct") 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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("intfloat/mmE5-mllama-11b-instruct") model = AutoModelForMultimodalLM.from_pretrained("intfloat/mmE5-mllama-11b-instruct", device_map="auto") - sentence-transformers
How to use intfloat/mmE5-mllama-11b-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/mmE5-mllama-11b-instruct") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 477 Bytes
c713514 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"do_convert_rgb": true,
"do_normalize": true,
"do_pad": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "MllamaImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"max_image_tiles": 4,
"processor_class": "MllamaProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 448,
"width": 448
}
}
|