ruoxi_sun
4f274c2c32
- fingerprint benchmark: add fp_fusion(26-cell fusion) to collect_results/run.py - run_llmmap.py: default model path to evalstone built-in model_library - add model libraries (llmdetector 11 refs / fp_fusion 8 fusion refs / llmmap templates 60 models incl 8 new: GLM-5.2/5.3, DeepSeek-Flash/Pro/ Flash-0731, Kimi-K3, MiniMax-M2.7, TianGong-Taie) - add fp_fusion engine (battery/engine/scorer) + docs - gitignore: exclude binary model weights and temp backups
32 lines
1.1 KiB
Bash
Executable File
32 lines
1.1 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Build the fingerprint-enabled evalstone image:
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# evalscope-complete-py312-fp:<tag>
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#
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# 用法：
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# bash scripts/build_fingerprint_image.sh [tag] # 默认 tag: v1
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# TAG=v2 bash scripts/build_fingerprint_image.sh # 指定 tag
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#
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# 构建完成后，新容器内无需任何环境配置：
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# docker run -it --rm \
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# -v /data1/eval/evalstone:/workspace/evalscope \
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# -v /data1/models:/opt/models \
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# evalscope-complete-py312-fp:v1
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# cd /workspace/evalscope
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# python bash/run.py --suite fingerprint --model <name> --api-url http://<host>:30000/v1
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set -euo pipefail
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PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
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TAG="${1:-${TAG:-v1}}"
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CTX="$PROJECT_ROOT/docker/fingerprint_context"
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# 1) 刷新 build 上下文（工具仓库 + 裁剪后的模型缓存）
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bash "$PROJECT_ROOT/scripts/stage_fingerprint_context.sh"
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# 2) 构建
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docker build -f "$PROJECT_ROOT/docker/Dockerfile.fingerprint" \
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-t "evalscope-complete-py312-fp:$TAG" "$CTX"
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echo
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echo "[build] done: evalscope-complete-py312-fp:$TAG"
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docker images | grep evalscope-complete-py312-fp || true
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