Visual Document Retrieval
Safetensors
ColPali
sentence-transformers
colpali-engine
qwen3_5
vision-language
colbert
late-interaction
multi-vector
vidore
document-retrieval
multimodal
state-of-the-art
Instructions to use tencent/EVIE-Preview-4.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ColPali
How to use tencent/EVIE-Preview-4.5B with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- sentence-transformers
How to use tencent/EVIE-Preview-4.5B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tencent/EVIE-Preview-4.5B") 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
| """Bidirectional attention for ColQwen3.5 retrieval. | |
| Released `colpali-engine` (through 0.3.17) builds ColQwen3.5 with the causal | |
| Qwen3.5 masks it inherits from the generative backbone. EVIE was trained and | |
| evaluated with the full-attention layers encoder-ized, so the checkpoint must be | |
| switched before it reproduces the reported scores. | |
| Qwen3.5 interleaves GatedDeltaNet (`linear_attention`) and `full_attention` | |
| layers. Only the full-attention layers are flipped here; the recurrent layers | |
| are order-dependent by construction and are left untouched. | |
| """ | |
| from typing import Any | |
| _ATTENTION_CLASSES = ("Qwen3_5Attention", "Qwen3Attention") | |
| def enable_bidirectional_attention(model: Any) -> None: | |
| """Encoder-ize the full-attention layers of a ColQwen3.5 model, in place.""" | |
| config = getattr(model, "config", None) | |
| for cfg in (config, getattr(config, "text_config", None)): | |
| # `create_causal_mask` falls back to `create_bidirectional_mask` on this flag. | |
| if cfg is not None: | |
| cfg.is_causal = False | |
| for module in model.modules(): | |
| if module.__class__.__name__ in _ATTENTION_CLASSES and hasattr(module, "is_causal"): | |
| module.is_causal = False | |