ZacharyZhang-NY
0ee99e271d
Real-models implementation: multimodal ingestion, Context Cartridge memory, sparse + Qwen embedding retrieval, Cache Fabric, Qwen runtime adapters, verifier contracts, Visual HashHop proofs, BF16 LoRA fine-tuning, and a local dashboard. Verified by a 15-check PRD proof suite on the live model.
27 lines
749 B
PowerShell
27 lines
749 B
PowerShell
$ErrorActionPreference = "Stop"
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Set-Location (Split-Path -Parent $PSScriptRoot)
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if (-not (Get-Command wsl.exe -ErrorAction SilentlyContinue)) {
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throw "WSL is required for the vLLM Linux runtime."
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}
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$wslRoot = (wsl.exe -d Ubuntu wslpath -a (Get-Location).Path).Trim()
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$ropeScaling = '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":262144}'
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$command = @"
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set -euo pipefail
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cd "$wslRoot"
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source .venv-linux-vllm/bin/activate
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python -m vllm.entrypoints.openai.api_server \
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--model Qwen/Qwen3.5-9B \
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--host 127.0.0.1 \
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--port 8001 \
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--max-model-len 1010000 \
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--enable-prefix-caching \
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--gpu-memory-utilization 0.88 \
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--trust-remote-code \
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--rope-scaling '$ropeScaling'
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"@
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wsl.exe -d Ubuntu -- bash -lc $command
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