38 lines
1.3 KiB
Bash
Executable File
38 lines
1.3 KiB
Bash
Executable File
#!/bin/sh
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set -e
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. ./.env
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mkdir -p $LOCAL_DATA_DIR
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mkdir -p $LOCAL_EMBEDDINGS_DIR
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mkdir -p $LOCAL_MODEL_DIR
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mkdir -p $LOCAL_OUTPUT_DIR
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mkdir -p $LOCAL_EVALOUTPUT_DIR
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. .venv/bin/activate
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set -x
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BACKBONE_MODEL_NAME="Qwen2-VL-7B-Instruct"
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RETRIEVAL_MODEL_TYPE="colpali"
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RETRIEVAL_MODEL_NAME="colpaligemma-3b-pt-448-base"
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RETRIEVAL_ADAPTER_MODEL_NAME="colpali-v1.2"
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EMBEDDING_NAME="colpali-v1.2_m3-docvqa_dev" # from Step 1 Embedding
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SPLIT="dev"
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DATASET_NAME="m3-docvqa"
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FAISS_INDEX_TYPE='ivfflat'
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N_RETRIEVAL_PAGES=1
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INDEX_NAME="${EMBEDDING_NAME}_pageindex_$FAISS_INDEX_TYPE" # from Step 2 Indexing
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OUTPUT_SAVE_NAME="${RETRIEVAL_ADAPTER_MODEL_NAME}_${BACKBONE_MODEL_NAME}_${DATASET_NAME}" # where to save RAG results
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BITS=16 # BITS=4 for 4-bit qunaitzation in low memory GPUs
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uv run examples/run_rag_m3docvqa.py \
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--use_retrieval \
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--retrieval_model_type=$RETRIEVAL_MODEL_TYPE \
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--load_embedding=True \
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--split=$SPLIT \
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--bits=$BITS \
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--n_retrieval_pages=$N_RETRIEVAL_PAGES \
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--data_name=$DATASET_NAME \
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--model_name_or_path=$BACKBONE_MODEL_NAME \
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--embedding_name=$EMBEDDING_NAME \
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--retrieval_model_name_or_path=$RETRIEVAL_MODEL_NAME \
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--retrieval_adapter_model_name_or_path=$RETRIEVAL_ADAPTER_MODEL_NAME \
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--output_dir=$LOCAL_EVALOUTPUT_DIR/$OUTPUT_SAVE_NAME
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