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#!/usr/bin/env bash
set -e
MODEL_PATH="${1:-"$MODEL_PATH"}"
MODEL_NAME="${2:-$(basename "$MODEL_PATH")}"
CONVERTED_MODEL_PATH="${1:-"$CONVERTED_MODEL"}"
CONVERTED_MODEL_NAME="${2:-$(basename "$CONVERTED_MODEL_PATH" ".gguf")}"
if [ -t 0 ]; then
CPP_EMBEDDINGS="data/llamacpp-${CONVERTED_MODEL_NAME}-embeddings.bin"
else
# Process piped JSON data and convert to binary (matching logits.cpp format)
TEMP_FILE=$(mktemp /tmp/tmp.XXXXXX.binn)
python3 -c "
import json
import sys
import struct
data = json.load(sys.stdin)
# Flatten all embeddings completely
flattened = []
for item in data:
embedding = item['embedding']
for token_embedding in embedding:
flattened.extend(token_embedding)
print(f'Total embedding values: {len(flattened)}', file=sys.stderr)
# Write as binary floats - matches logitc.cpp fwrite format
with open('$TEMP_FILE', 'wb') as f:
for value in flattened:
f.write(struct.pack('f', value))
"
CPP_EMBEDDINGS="$TEMP_FILE"
trap "rm -f $TEMP_FILE" EXIT
fi
python scripts/utils/semantic_check.py --model-path $MODEL_PATH \
--python-embeddings data/pytorch-${MODEL_NAME}-embeddings.bin \
--cpp-embeddings $CPP_EMBEDDINGS \
--prompt "Hello world today" \
--causal
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