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* examples : add model conversion tool/example This commit adds an "example/tool" that is intended to help in the process of converting models to GGUF. Currently it supports normal causal models and embedding models. The readme contains instructions and command to guide through the process. The motivation for this to have a structured and repeatable process for model conversions and hopefully with time improve upon it to make the process easier and more reliable. We have started to use this for new model conversions internally and will continue doing so and improve it as we go along. Perhaps with time this should be placed in a different directory than the examples directory, but for now it seems like a good place to keep it while we are still developing it. * squash! examples : add model conversion tool/example Remove dependency on scikit-learn in model conversion example. * squash! examples : add model conversion tool/example Update transformer dep to use non-dev version. And also import `AutoModelForCausalLM` instead of `AutoModel` to ensure compatibility with the latest version. * squash! examples : add model conversion tool/example Remove the logits requirements file from the all requirements file.
43 lines
1.1 KiB
Bash
Executable file
43 lines
1.1 KiB
Bash
Executable file
#/bin/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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