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
| { | |
| "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 | |
| ] | |
| } | |
| } | |