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244e346b83
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
25 lines
1.2 KiB
Plaintext
25 lines
1.2 KiB
Plaintext
# SkillOpt Environment Variables
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# Copy this file to .env and fill in your values.
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# ── Azure OpenAI (required for openai_chat backend) ──────────────────
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AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
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AZURE_OPENAI_API_VERSION=2024-12-01-preview
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# Authentication: choose one method
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# Option 1: API Key
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AZURE_OPENAI_API_KEY=
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# Option 2: Azure CLI (set auth_mode=azure_cli in config)
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# Option 3: Managed Identity (set auth_mode=managed_identity + client_id in config)
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# ── OpenAI (alternative to Azure) ────────────────────────────────────
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# OPENAI_API_KEY=sk-...
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# ── Anthropic / Claude (for claude_chat backend) ─────────────────────
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# ANTHROPIC_API_KEY=sk-ant-...
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# ── Qwen Local Model (for qwen_chat backend) ────────────────────────
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# QWEN_CHAT_BASE_URL=http://localhost:8000/v1
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# QWEN_CHAT_MODEL=Qwen/Qwen3.5-4B
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# ── Ray (optional, for distributed rollout) ──────────────────────────
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# RAY_ADDRESS=auto
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