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- 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
26 lines
849 B
Plaintext
26 lines
849 B
Plaintext
# ── Core ──────────────────────────────────────────
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openai>=1.30.0
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pyyaml>=6.0
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numpy>=1.24.0
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openpyxl>=3.1.0
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azure-identity>=1.15.0
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azure-core>=1.30.0
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httpx>=0.27.0
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# ── Optional: ALFWorld benchmark ──────────────────
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# alfworld>=0.4.0
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# gymnasium>=0.29.0
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# ── Optional: Claude model backend ────────────────
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# claude-agent-sdk>=0.1.0
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# ── Optional: Qwen local model (via vLLM) ────────
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# vllm>=0.4.0
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# ── Optional: WebUI dashboard ────────────────────
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# gradio>=4.0.0
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# ── Optional: Documentation site ─────────────────
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# mkdocs-material>=9.5.0
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# mkdocstrings[python]>=0.24.0
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