fbc01ad865
the summarization becomes much better if we allow the model to first generate a candidate summarization and then improving on it. doing the improvement step just once seems to significantly improve the summary. we also now use an llm (mistral 7b) for the summarisations, as we can then use the same model for multiple tasks and serve it using gpus, thus significantly decreasing the latency.
36 lines
878 B
Python
Executable file
Vendored
36 lines
878 B
Python
Executable file
Vendored
#!.venv/bin/python3
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import argparse
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import subprocess
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import os
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os.environ["LIBTORCH"] = "libtorch"
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os.environ["LD_LIBRARY_PATH"] = "libtorch/lib"
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os.environ["DYLD_LIBRARY_PATH"] = "libtorch/lib"
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parser = argparse.ArgumentParser()
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parser.add_argument("--release", action="store_true")
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args = parser.parse_args()
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if args.release:
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os.environ["STRACT_CARGO_ARGS"] = "--release"
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processes = []
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processes.append(subprocess.Popen(["just", "dev-api"]))
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processes.append(subprocess.Popen(["just", "dev-search-server"]))
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processes.append(subprocess.Popen(["just", "dev-webgraph"]))
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processes.append(subprocess.Popen(["just", "dev-frontend"]))
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processes.append(subprocess.Popen(["just", "dev-llm"]))
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# kill processes on ctrl-c
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import time
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while True:
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try:
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time.sleep(1)
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except KeyboardInterrupt:
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for p in processes:
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p.kill()
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break
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