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https://github.com/ggml-org/llama.cpp.git
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40a751ea9a
* model-conversion : remove hardcoded /bin/bash shebangs [no ci] This commit updates the bash scripts to use env instead of using hardcoded /bin/bash in the shebang line. The motivation for this is that some systems may have bash installed in a different location, and using /usr/bin/env bash ensures that the script will use the first bash interpreter found in the user's PATH, making the scripts more portable across different environments. * model-conversion : rename script to .py [no ci] This commit renames run-casual-gen-embeddings-org.sh to run-casual-gen-embeddings-org.py to reflect its Python nature.
44 lines
1.1 KiB
Bash
Executable File
44 lines
1.1 KiB
Bash
Executable File
#!/usr/bin/env bash
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set -e
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MODEL_PATH="${1:-"$MODEL_PATH"}"
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MODEL_NAME="${2:-$(basename "$MODEL_PATH")}"
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if [ -t 0 ]; then
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CPP_EMBEDDINGS="data/llamacpp-${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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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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--prompt "Hello world today" \
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--causal
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