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https://github.com/open-jarvis/OpenJarvis.git
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Andrew Park
a6afeae721
Train a single ~9B orchestrator (Qwen3-8B) for local<->cloud routing via SFT on synthetic traces derived from NeuLab ADP (agent-data-collection). - sft_data/: full ADP trajectory loader, tier model + pricing, 4 paradigm renderers, reward-ranked selection, conversations-JSONL serializer, CLI - wire sft_trainer._generate_traces() to the pipeline (no GPU / no API keys) - Qwen3-8B SFT config + scripts/orchestrator CLI wrapper - offline fixture tests (12) Heuristic competence labels for the cold-start; real paradigm execution + GRPO are the documented v2.
20 lines
562 B
Python
20 lines
562 B
Python
#!/usr/bin/env python
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"""Thin CLI wrapper: build the orchestrator SFT dataset from NeuLab ADP.
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Equivalent to::
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python -m openjarvis.learning.intelligence.orchestrator.sft_data.build [...]
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Usage:
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python scripts/orchestrator/build_sft_data.py \
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--out data/orchestrator_sft_traces.jsonl \
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--max-tasks 2000 --adp-configs codeactinstruct,code_feedback,openhands
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"""
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from __future__ import annotations
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from openjarvis.learning.intelligence.orchestrator.sft_data.build import _main
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if __name__ == "__main__":
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raise SystemExit(_main())
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