Visual Document Retrieval
Safetensors
sentence-transformers
colpali-engine
qwen3_5
vision-language
colbert
late-interaction
multi-vector
vidore
document-retrieval
multimodal
Instructions to use tencent/EVIE-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tencent/EVIE-8B with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("tencent/EVIE-8B") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
Download env.sh.example from tencent/EVIE-8B: direct link, hf CLI and curl.
- Browser
- Download file 486 Bytes
-
https://huggingface.co/tencent/EVIE-8B/resolve/main/env.sh.example
- Command line
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hf download hf://tencent/EVIE-8B/env.sh.example
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curl -L -o env.sh.example https://huggingface.co/tencent/EVIE-8B/resolve/main/env.sh.example
486 Bytes
| # Copy to env.sh (gitignored). Point at eval data / a virtualenv. | |
| export EVIE_ROOT="${EVIE_ROOT:-$(pwd)}" | |
| export PYTHON="${PYTHON:-python3}" # or $VIRTUAL_ENV/bin/python | |
| export RUNS_DIR="${RUNS_DIR:-$EVIE_ROOT/runs}" | |
| export LOG_DIR="${LOG_DIR:-$EVIE_ROOT/logs}" | |
| export EVAL_ROOT="${EVAL_ROOT:-$EVIE_ROOT/data}" | |
| export HF_HOME="${HF_HOME:-$EVIE_ROOT/.cache/huggingface}" | |
| export HF_DATASETS_CACHE="${HF_DATASETS_CACHE:-$HF_HOME/datasets}" | |
| export MODEL_DIR="${MODEL_DIR:-$EVIE_ROOT}" | |