Zero-Shot Image Classification
Transformers
ONNX
Chinese
English
m2_encoder
feature-extraction
multimodal
image-text-retrieval
bilingual
chinese
english
vision-language
custom-code
custom_code
Eval Results (legacy)
Instructions to use malusama/M2-Encoder-0.4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malusama/M2-Encoder-0.4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="malusama/M2-Encoder-0.4B", 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 AutoModel model = AutoModel.from_pretrained("malusama/M2-Encoder-0.4B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 387 Bytes
f471fb4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"name_or_path": "THUDM/glm-10b-chinese",
"eos_token": "<|endoftext|>",
"pad_token": "<|endoftext|>",
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"unk_token": "[UNK]",
"add_prefix_space": false,
"tokenizer_class": "GLMChineseTokenizer",
"use_fast": false,
"auto_map": {
"AutoTokenizer": [
"tokenization_glm.GLMChineseTokenizer",
null
]
}
}
|