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nachawatinachawati
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feat(ide): add webllm model download script, dockerfile stage, ci caching, and readme docs
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‎.github/workflows/ci.yml‎

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personal_token: ${{ secrets.MODELSCRIPT_SITE_DEPLOY_PAT }}
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external_repository: modelscript/modelscript.github.io
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publish_dir: ./packages/site/build/client
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- name: Cache WebLLM Model
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id: cache-model
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uses: actions/cache@v4
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with:
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path: packages/ide/models
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key: ${{ runner.os }}-qwen3-0.6b-q4f16_1-mlc-v1
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- name: Download WebLLM Model
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if: steps.cache-model.outputs.cache-hit != 'true'
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run: bash packages/ide/scripts/download-model.sh
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- name: Build IDE Static
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if: github.ref == 'refs/heads/main' && github.event_name != 'pull_request'
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run: npm run build-static --workspace=@modelscript/ide
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- name: Copy model to IDE static output
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if: github.ref == 'refs/heads/main' && github.event_name != 'pull_request'
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run: cp -r packages/ide/models packages/ide/dist/static/api/models
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- name: Deploy IDE
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uses: peaceiris/actions-gh-pages@v4
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if: github.ref == 'refs/heads/main' && github.event_name != 'pull_request'

‎Dockerfile‎

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# ==============================================================================
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# IDE
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# ==============================================================================
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# Download WebLLM model weights (cached layer)
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FROM node:22-alpine AS download-model
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RUN apk add --no-cache curl bash
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WORKDIR /models
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COPY packages/ide/scripts/download-model.sh /tmp/download-model.sh
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RUN bash /tmp/download-model.sh /models
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FROM deps AS build-ide-false
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COPY scripts scripts
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COPY packages/core packages/core
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COPY --from=build-ide /app/packages/vscode/language-configuration.json packages/vscode/
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COPY --from=build-ide /app/packages/morsel/public packages/morsel/public
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COPY --from=build-ide /app/node_modules/@vscode node_modules/@vscode
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COPY --from=download-model /models packages/ide/models
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EXPOSE 3200
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ENV NODE_ENV=production
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ENV PORT=3200

‎README.md‎

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| API | http://localhost:3000 | REST API for simulation, publishing, queries |
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| IDE (VS Code Web) | http://localhost:3200 | Browser-based VS Code with Modelica support |
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#### AI Chat (Optional)
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The IDE includes a browser-local AI assistant powered by WebLLM (Qwen3-0.6B). To enable it, download the model weights (~350 MB, one-time):
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```bash
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npm run download-model --workspace=@modelscript/ide
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```
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Once downloaded, restart `npm run dev` and open the **ModelScript AI** panel in the IDE sidebar.
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### CLI Usage
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After building, the CLI is available as `msc`:

‎packages/ide/package.json‎

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"build-server": "npx esbuild src/server.ts --bundle --platform=node --format=esm --outfile=dist/server.js --external:express --external:http-proxy-middleware",
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"build-static": "npm run download-vscode && npm run build-github-fs && npx tsx src/build-static.ts",
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"dev": "npm run download-vscode && npx tsx src/server.ts",
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"download-model": "bash scripts/download-model.sh",
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"download-vscode": "npx tsx src/download-vscode.ts",
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"lint": "eslint src --ext ts"
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},

‎packages/ide/scripts/download-model.sh‎

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#!/usr/bin/env bash
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# Download Qwen3-0.6B-q4f16_1-MLC model weights and WASM for WebLLM.
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# Files are cached — re-running is a no-op if all files already exist.
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#
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# Usage: ./scripts/download-model.sh [target_dir]
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# Default target_dir: packages/ide/models
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
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REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
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TARGET="${1:-$REPO_ROOT/models}"
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MODEL_DIR="$TARGET/Qwen3-0.6B-q4f16_1-MLC"
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WASM_FILE="$TARGET/Qwen3-0.6B-q4f16_1-ctx4k_cs1k-webgpu.wasm"
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HF_BASE="https://huggingface.co/mlc-ai/Qwen3-0.6B-q4f16_1-MLC/resolve/main"
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WASM_URL="https://huggingface.co/mlc-ai/binary-mlc-llm-libs/resolve/main/Qwen3-0.6B-q4f16_1-ctx4k_cs1k-webgpu.wasm"
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# Files to download from the HuggingFace model repo
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MODEL_FILES=(
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mlc-chat-config.json
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ndarray-cache.json
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tokenizer.json
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tokenizer_config.json
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vocab.json
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merges.txt
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params_shard_0.bin
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params_shard_1.bin
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params_shard_2.bin
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params_shard_3.bin
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params_shard_4.bin
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params_shard_5.bin
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params_shard_6.bin
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params_shard_7.bin
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params_shard_8.bin
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)
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echo "==> Downloading Qwen3-0.6B-q4f16_1-MLC model to $TARGET"
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mkdir -p "$MODEL_DIR"
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# Download model files
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for file in "${MODEL_FILES[@]}"; do
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if [ -f "$MODEL_DIR/$file" ]; then
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echo " [cached] $file"
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else
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echo " [downloading] $file"
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curl -fSL --retry 3 "$HF_BASE/$file" -o "$MODEL_DIR/$file"
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fi
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done
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# Download WASM
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if [ -f "$WASM_FILE" ]; then
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echo " [cached] $(basename "$WASM_FILE")"
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else
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echo " [downloading] $(basename "$WASM_FILE")"
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curl -fSL --retry 3 "$WASM_URL" -o "$WASM_FILE"
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fi
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echo "==> Model download complete"

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