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Download src/miniwebagent/scripts/cluster/eval.sh from sparklabuofu/MiniWeb-demo: direct link, hf CLI and curl.
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https://huggingface.co/spaces/sparklabuofu/MiniWeb-demo/resolve/main/src/miniwebagent/scripts/cluster/eval.sh
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hf download hf://spaces/sparklabuofu/MiniWeb-demo/src/miniwebagent/scripts/cluster/eval.sh
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curl -L -o eval.sh https://huggingface.co/spaces/sparklabuofu/MiniWeb-demo/resolve/main/src/miniwebagent/scripts/cluster/eval.sh
1.07 kB
| # One MiniWeb run on one GPU: a vLLM server with the given LoRAs, then miniwebagent.evaluate. | |
| # eval.sh <gpu> <vllm port> "<lora name=path ...>" <out name> <miniweb ports, e.g. 8300,8301> [miniwebagent.evaluate args ...] | |
| # Env: MAX_LORAS (8), MAX_LORA_RANK (64); MODEL / SERVED / NO_LORA / EXTRA_ARGS / CHAT_KWARGS for other models (RQ1). | |
| # There are no API keys on the cluster, so the answer checks' LLM tier is off here (VERIFIER_JUDGE_MODEL=none): | |
| # re-grade the run where keys are, with `python -m miniwebagent.evaluate --regrade <run> --out <run>`. | |
| source "$(dirname "$0")/env.sh" | |
| GPU=$1; VPORT=$2; LORAS=$3; NAME=$4; PORTS=$5; shift 5 | |
| node_db | |
| cd $B/miniweb | |
| serve $B/logs/vllm_$NAME.log CUDA_VISIBLE_DEVICES=$GPU PORT=$VPORT LORA_MODULES="$LORAS" MAX_LORAS=${MAX_LORAS:-8} \ | |
| MAX_LORA_RANK=${MAX_LORA_RANK:-64} | |
| export VERIFIER_JUDGE_MODEL=none | |
| echo "$(date +%F\ %T) eval $NAME on GPU $GPU, vLLM :$VPORT, MiniWeb $PORTS: $*" | |
| $B/menv/bin/python -m miniwebagent.evaluate --vllm http://127.0.0.1:$VPORT/v1 --ports $PORTS --out $B/eval/$NAME "$@" | |