Feature Extraction
Transformers
Safetensors
English
penguinvl_vision_encoder
multi-modal
large-language-model
vision-language-model
vision-encoder
custom_code
Instructions to use tencent/Penguin-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Penguin-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tencent/Penguin-Encoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tencent/Penguin-Encoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| """PenguinVL vision encoder model configuration.""" | |
| from transformers import Qwen3Config | |
| class PenguinVLVisionEncoderConfig(Qwen3Config): | |
| model_type = "penguinvl_vision_encoder" | |
| def __init__( | |
| self, | |
| hidden_size=1536, | |
| intermediate_size=8960, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| num_channels=3, | |
| patch_size=14, | |
| layer_norm_eps=1e-6, | |
| attention_dropout=0.0, | |
| num_key_value_heads=2, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.num_channels = num_channels | |
| self.patch_size = patch_size | |
| self.attention_dropout = attention_dropout | |
| self.num_key_value_heads = num_key_value_heads | |
| self.layer_norm_eps = layer_norm_eps | |