mirror of
https://github.com/ggerganov/llama.cpp
synced 2026-03-30 03:45:34 +02:00
db81d5ec4b
This commit updates the embedding model verification script to use the CONVERTED_EMBEDDING_MODEL environment variable instead of using the EMBEDDING_MODEL_PATH (the original embedding model path) as the basis for the converted model file name. The motivation for this that currently if the converted embedding model file name differs from the original embedding model directory/name the verification script will look for the wrong .bin files that were generating when running the models.
85 lines
2.2 KiB
Bash
Executable File
85 lines
2.2 KiB
Bash
Executable File
#!/usr/bin/env bash
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set -e
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# Parse command line arguments
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MODEL_PATH=""
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MODEL_NAME=""
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PROMPTS_FILE=""
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# First argument is always model path
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if [ $# -gt 0 ] && [[ "$1" != --* ]]; then
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MODEL_PATH="$1"
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shift
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fi
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# Parse remaining arguments
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while [[ $# -gt 0 ]]; do
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case $1 in
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--prompts-file|-pf)
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PROMPTS_FILE="$2"
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shift 2
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;;
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*)
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# If MODEL_NAME not set and this isn't a flag, use as model name
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if [ -z "$MODEL_NAME" ] && [[ "$1" != --* ]]; then
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MODEL_NAME="$1"
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fi
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shift
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;;
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esac
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done
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# Set defaults
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MODEL_PATH="${MODEL_PATH:-"$EMBEDDING_MODEL_PATH"}"
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MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}"
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CONVERTED_MODEL_PATH="${CONVERTED_EMBEDDING_PATH:-"$CONVERTED_EMBEDDING_MODEL"}"
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CONVERTED_MODEL_NAME="${CONVERTED_MODEL_NAME:-$(basename "$CONVERTED_MODEL_PATH" .gguf)}"
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if [ -t 0 ]; then
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CPP_EMBEDDINGS="data/llamacpp-${CONVERTED_MODEL_NAME}-embeddings.bin"
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else
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# Process piped JSON data and convert to binary (matching logits.cpp format)
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TEMP_FILE=$(mktemp /tmp/tmp.XXXXXX.binn)
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python3 -c "
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import json
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import sys
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import struct
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data = json.load(sys.stdin)
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# Flatten all embeddings completely
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flattened = []
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for item in data:
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embedding = item['embedding']
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for token_embedding in embedding:
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flattened.extend(token_embedding)
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print(f'Total embedding values: {len(flattened)}', file=sys.stderr)
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# Write as binary floats - matches logitc.cpp fwrite format
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with open('$TEMP_FILE', 'wb') as f:
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for value in flattened:
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f.write(struct.pack('f', value))
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"
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CPP_EMBEDDINGS="$TEMP_FILE"
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trap "rm -f $TEMP_FILE" EXIT
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fi
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# Build the semantic_check.py command
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SEMANTIC_CMD="python scripts/utils/semantic_check.py --model-path $MODEL_PATH \
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--python-embeddings data/pytorch-${MODEL_NAME}-embeddings.bin \
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--cpp-embeddings $CPP_EMBEDDINGS"
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# Add prompts file if specified, otherwise use default prompt
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if [ -n "$PROMPTS_FILE" ]; then
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SEMANTIC_CMD="$SEMANTIC_CMD --prompts-file \"$PROMPTS_FILE\""
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else
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SEMANTIC_CMD="$SEMANTIC_CMD --prompt \"Hello world today\""
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fi
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# Execute the command
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eval $SEMANTIC_CMD
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