From c2a62912af870a38426030635067eada449d6650 Mon Sep 17 00:00:00 2001 From: RuurdKuiper Date: Tue, 6 Oct 2026 16:53:41 +0200 Subject: [PATCH] Add Qwen3.8-27B WebGPU recurrent-state generation example --- js/README.md | 2 + js/qwen38-webgpu/.gitignore | 6 + js/qwen38-webgpu/LICENSE | 202 ++ js/qwen38-webgpu/README.md | 47 + js/qwen38-webgpu/download-model.mjs | 36 + js/qwen38-webgpu/export/.gitignore | 5 + js/qwen38-webgpu/export/README.md | 27 + js/qwen38-webgpu/export/export.py | 60 + .../export/requirements-export.lock | 41 + .../export/scripts/catalog_entry.py | 23 + .../export/scripts/check_adaptation.py | 47 + .../export/scripts/inspect_model.py | 55 + .../export/scripts/prepare_browser.py | 79 + .../export/scripts/shard_model.py | 77 + .../export/scripts/streaming_export.py | 167 ++ js/qwen38-webgpu/export/scripts/validate.py | 67 + js/qwen38-webgpu/export/setup.sh | 5 + js/qwen38-webgpu/index.html | 12 + js/qwen38-webgpu/model.json | 142 ++ js/qwen38-webgpu/package-lock.json | 2115 +++++++++++++++++ js/qwen38-webgpu/package.json | 23 + js/qwen38-webgpu/src/inference.worker.js | 284 +++ js/qwen38-webgpu/src/main.js | 54 + js/qwen38-webgpu/test-browser.mjs | 28 + js/qwen38-webgpu/validation.json | 69 + js/qwen38-webgpu/vite.config.js | 26 + 26 files changed, 3699 insertions(+) create mode 100644 js/qwen38-webgpu/.gitignore create mode 100644 js/qwen38-webgpu/LICENSE create mode 100644 js/qwen38-webgpu/README.md create mode 100644 js/qwen38-webgpu/download-model.mjs create mode 100644 js/qwen38-webgpu/export/.gitignore create mode 100644 js/qwen38-webgpu/export/README.md create mode 100644 js/qwen38-webgpu/export/export.py create mode 100644 js/qwen38-webgpu/export/requirements-export.lock create mode 100644 js/qwen38-webgpu/export/scripts/catalog_entry.py create mode 100644 js/qwen38-webgpu/export/scripts/check_adaptation.py create mode 100644 js/qwen38-webgpu/export/scripts/inspect_model.py create mode 100644 js/qwen38-webgpu/export/scripts/prepare_browser.py create mode 100644 js/qwen38-webgpu/export/scripts/shard_model.py create mode 100644 js/qwen38-webgpu/export/scripts/streaming_export.py create mode 100644 js/qwen38-webgpu/export/scripts/validate.py create mode 100644 js/qwen38-webgpu/export/setup.sh create mode 100644 js/qwen38-webgpu/index.html create mode 100644 js/qwen38-webgpu/model.json create mode 100644 js/qwen38-webgpu/package-lock.json create mode 100644 js/qwen38-webgpu/package.json create mode 100644 js/qwen38-webgpu/src/inference.worker.js create mode 100644 js/qwen38-webgpu/src/main.js create mode 100644 js/qwen38-webgpu/test-browser.mjs create mode 100644 js/qwen38-webgpu/validation.json create mode 100644 js/qwen38-webgpu/vite.config.js diff --git a/js/README.md b/js/README.md index dcd93fbd3..5093cb2e0 100644 --- a/js/README.md +++ b/js/README.md @@ -52,3 +52,5 @@ Click links for README of each examples. * [Phi-3-mini-4k-instruct](chat) - demonstrates how to run [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) in your browser using [onnxruntime-web](https://github.com/microsoft/onnxruntime/js) with webgpu. + +* [Qwen3.8-27B INT4](qwen38-webgpu) - demonstrates community-converted Qwen3.8-27B text generation with ONNX Runtime WebGPU, JSPI external-data loading and recurrent-state caching. diff --git a/js/qwen38-webgpu/.gitignore b/js/qwen38-webgpu/.gitignore new file mode 100644 index 000000000..3e18c3c38 --- /dev/null +++ b/js/qwen38-webgpu/.gitignore @@ -0,0 +1,6 @@ +node_modules/ +dist/ +.models/ +results/ +.env +.DS_Store diff --git a/js/qwen38-webgpu/LICENSE b/js/qwen38-webgpu/LICENSE new file mode 100644 index 000000000..4dd2987ea --- /dev/null +++ b/js/qwen38-webgpu/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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It runs independently of Clinstruct. All model operators execute locally on GPU, with CPU inference fallback disabled; JavaScript handles tokenization, sequence metadata and greedy sampling. + +**Weights: approximately 15.26 GB. Tested on a 48 GiB Apple Silicon Mac, Chrome 154, Metal 3 WebGPU.** Earlier integrated tests measured about 5–6 tokens/s; the standalone 32-token smoke test measured about 10 tokens/s. See [validation.json](validation.json) for exact timings. Other hardware and long contexts are unvalidated. This is a runnable engineering example, not a quality benchmark or an official Microsoft/Alibaba release. + +## Run + +Install Node.js ≥20.19. Use current Chrome or Edge with hardware WebGPU, `shader-f16` and WebAssembly JSPI, in an ordinary browser profile. + +```sh +git clone https://github.com/RuurdKuiper/qwen38-27b-onnx-webgpu.git +cd qwen38-27b-onnx-webgpu +npm ci +npm run download:model +npm run dev -- --port 5174 +``` + +Open `http://127.0.0.1:5174`, click **Load model**, then generate. The download script gets the [public Hugging Face pack](https://huggingface.co/Ruurd/Qwen3.8-27B-ONNX-WebGPU-INT4) at the immutable commit in `model.json` and verifies file sizes/SHA-256 checksums. Re-running it preserves verified completed shards. The local server only serves static assets; it runs no inference. Prompts remain in the browser. + +If the model is already installed elsewhere: + +```sh +MODEL_DIR=/absolute/path/to/model-pack npm run dev -- --port 5174 +``` + +`npm run build` bundles the application and pinned runtime, excluding weights. To deploy that build, serve the model pack separately at `/models/onnx/Qwen3.8-27B-GenAI-WebGPU-INT4/` on the same secure origin. Never bundle the weights into the application ZIP. + +## Model and generation contract + +This export requires the complete graph/state contract, not only `input_ids → logits`. The worker inspects `manifest.json` and `genai_config.json` and retains **128 state tensors on GPU**: 32 KV, 48 convolution and 48 recurrent. Each prompt starts fresh state; decoding retains state between tokens. Generation is batch-one contiguous unpadded text, 32 new tokens maximum in this UI, temperature zero, and thinking disabled. Qwen's three-axis position IDs are supplied explicitly. + +The native graph is preserved. `model.browser.onnx` replaces six known attention-mask metadata operations with host `seqlens_k` and `total_sequence_length` inputs to avoid CPU placement; weights and model computation are unchanged relative to the native export. INT4 block size is 32; both MatMul and Gather (untied embeddings) are quantized. Vision, MTP, paged attention, speculative decoding and CUDA-specific GatedDeltaNet are excluded. + +The example reports model bytes, download/load time, first-token latency, decoding speed, available JS heap statistics, GPU adapter/features and runtime warnings. Heap statistics exclude GPU allocations. The loader uses `onnxruntime-web@1.31.0-dev.20260914-8d85527a0`; Transformers.js 4.3.0 supplies tokenization only. The tested 48 GiB machine is not a measured minimum requirement. Large Blob loading failed in an incognito test. + +## Reproduce and test + +`export/` contains the pinned Python exporter environment and the official-builder wrapper. The memory-bounded serializer is an optional adapter around the official DEFAULT quantizer; it handles large intermediate weights and emits ≤1 GiB shards without changing quantization arithmetic. See `export/README.md` for commands and architecture limitations. + +With Vite running, `npm run test:browser` launches installed Chrome in a fresh ordinary profile, blocks external requests, checks arithmetic, ≥20 consecutive generated tokens, strict GPU execution and all 128 retained state tensors, and writes `results/browser.json`. For another origin set `DEMO_URL`. + +## Attribution + +Qwen3.8-27B is copyright 2026 Alibaba Cloud under Apache 2.0. The model repository includes its `LICENSE` and `NOTICE`. This browser example and conversion tooling are copyright 2026 Ruurd Kuiper under Apache 2.0; dependencies retain their own licenses. The worker originates from the tested Clinstruct integration and is reproduced here so other applications can use the same generation path. + +Upstream projects: [ONNX Runtime](https://github.com/microsoft/onnxruntime), [ONNX Runtime GenAI](https://github.com/microsoft/onnxruntime-genai), [Transformers.js](https://github.com/huggingface/transformers.js), [original checkpoint](https://huggingface.co/Qwen/Qwen3.8-27B). diff --git a/js/qwen38-webgpu/download-model.mjs b/js/qwen38-webgpu/download-model.mjs new file mode 100644 index 000000000..e74a25d73 --- /dev/null +++ b/js/qwen38-webgpu/download-model.mjs @@ -0,0 +1,36 @@ +// Download immutable public assets; the browser performs all inference locally. +import { createHash } from 'node:crypto'; +import { createReadStream, createWriteStream } from 'node:fs'; +import { mkdir, readFile, rename, rm, stat } from 'node:fs/promises'; +import { resolve } from 'node:path'; +import { Readable, Transform } from 'node:stream'; +import { pipeline } from 'node:stream/promises'; +const config = JSON.parse(await readFile(new URL('./model.json', import.meta.url), 'utf8')); +const root = resolve(process.env.MODEL_DIR ?? `.models/${config.id}`); +await mkdir(root, { recursive: true }); +async function checksum(file) { + const hash = createHash('sha256'); + for await (const chunk of createReadStream(file)) hash.update(chunk); + return hash.digest('hex'); +} +for (const file of config.files) { + if (!/^[A-Za-z0-9_.-]+$/.test(file.name) || ['.', '..'].includes(file.name)) throw new Error('Unexpected model filename.'); + const destination = resolve(root, file.name); + try { + if ((await stat(destination)).size === file.bytes && await checksum(destination) === file.sha256) { + console.log('Verified existing', file.name); continue; + } + } catch (error) { if (error.code !== 'ENOENT') throw error; } + const partial = destination + '.part'; + console.log('Downloading', file.name, file.bytes, 'bytes'); + const response = await fetch(`https://huggingface.co/${config.repo}/resolve/${config.revision}/${file.name}?download=true`); + if (!response.ok) throw new Error(`HTTP ${response.status}: ${file.name}`); + const hash = createHash('sha256'); let bytes = 0; + const verify = new Transform({ transform(chunk, encoding, callback) { bytes += chunk.length; hash.update(chunk); callback(null, chunk); } }); + try { + await pipeline(Readable.fromWeb(response.body), verify, createWriteStream(partial)); + if (bytes !== file.bytes || hash.digest('hex') !== file.sha256) throw new Error(`Integrity check failed: ${file.name}`); + await rename(partial, destination); + } catch (error) { await rm(partial, { force: true }); throw error; } +} +console.log('Verified model pack ready:', root); diff --git a/js/qwen38-webgpu/export/.gitignore b/js/qwen38-webgpu/export/.gitignore new file mode 100644 index 000000000..a25b00592 --- /dev/null +++ b/js/qwen38-webgpu/export/.gitignore @@ -0,0 +1,5 @@ +.venv/ +.work/ +node_modules/ +__pycache__/ +*.log diff --git a/js/qwen38-webgpu/export/README.md b/js/qwen38-webgpu/export/README.md new file mode 100644 index 000000000..5394d0326 --- /dev/null +++ b/js/qwen38-webgpu/export/README.md @@ -0,0 +1,27 @@ +# Reproduce the conversion + +Run from this repository's root. Requires `uv` and space for the ~55.56 GB original checkpoint, ~15.26 GB output and temporary files. The recorded export used a 48 GiB Mac. These dependencies pin the official builder/runtime tested on 6 October 2026. + +```sh +bash export/setup.sh +export/.venv/bin/python export/export.py \ + --model Qwen/Qwen3.8-27B \ + --revision 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0 \ + --bounded \ + --output .models/Qwen3.8-27B-GenAI-WebGPU-INT4 +``` + +The command downloads the pinned checkpoint, exports INT4 with WebGPU, runs offline native GenAI sanity checks, adapts the known attention-mask metadata subgraph and emits the browser manifest. The native CPU check does not serve inference to the browser. `--skip-native` skips it. + +`--bounded` retains the official builder and DEFAULT weight-only quantizer, but serializes/quantizes one weight at a time to avoid retaining the whole FP16 intermediate model during serialization. The original baseline was killed while saving. The adapter copies independent protobuf descriptors, handles >2 GiB intermediate weights through temporary external files, quantizes both MatMul/Gather, and emits ≤1 GiB tensor-aligned shards. It matched all 491 initializer shapes/dtypes/hashes and graph nodes in a 2B cross-check against the ordinary exporter. This internal adapter is tied to the pinned builder version. + +Defaults are INT4 block size 32, decoder only, `exclude_mtp=true`, `state_window=0`, ordinary non-paged linear attention and MatMul/Gather quantization. No CUDA kernels, vision, MTP, speculative decoding or paged attention. Output must be empty; use a new directory for a retry. Reports are beside the output in `model-name.reports/`. + +The original/native graph is retained. The browser adapter removes exactly six known mask metadata nodes and rejects unfamiliar subgraphs. Different architectures need their own input/output/state and position-ID inspection and WebGPU operator validation; the current worker implements the Qwen graph contract. Optional offline graph/state equivalence check: + +```sh +export/.venv/bin/python export/scripts/check_adaptation.py \ + .models/Qwen3.8-27B-GenAI-WebGPU-INT4 +``` + +Serve the example and run `npm run test:browser` to test the result. The model's configured context length is not a measured browser capacity. Reproducible numerical serialization does not establish BF16 quality parity or compatibility with arbitrary GPUs. diff --git a/js/qwen38-webgpu/export/export.py b/js/qwen38-webgpu/export/export.py new file mode 100644 index 000000000..ea2c0c8db --- /dev/null +++ b/js/qwen38-webgpu/export/export.py @@ -0,0 +1,60 @@ +"""Reproduce the tested GenAI INT4/WebGPU export into a self-contained pack.""" +import argparse +import json +import os +from pathlib import Path +import subprocess +import sys + +# Set before importing huggingface_hub: ordinary HTTP avoids the observed Xet stall. +os.environ.setdefault('HF_HUB_DISABLE_XET', '1') +os.environ.setdefault('HF_HUB_DOWNLOAD_TIMEOUT', '120') +os.environ['PYTHONUNBUFFERED'] = '1' +from huggingface_hub import model_info, snapshot_download + +TOOL = Path(__file__).resolve().parent +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument('--model', required=True, help='Official Hugging Face checkpoint ID') +parser.add_argument('--revision', help='Resolve and record HEAD if omitted') +parser.add_argument('--output', type=Path, required=True, help='A new/empty model-pack directory') +parser.add_argument('--cache', type=Path, default=TOOL / '.work/cache') +parser.add_argument('--bounded', action='store_true', help='Use the validated per-weight serializer for large models') +parser.add_argument('--skip-native', action='store_true', help='Skip offline CPU sanity validation') +parser.add_argument('--extra-option', action='append', default=[], help='Additional builder key=value, e.g. block_size=32') +args = parser.parse_args() +root = args.output.resolve() +if root.exists() and any(root.iterdir()): + parser.error('Output must be empty. Use a new directory or remove the failed output before retrying.') +root.mkdir(parents=True, exist_ok=True) +notes = root.parent / (root.name + '.reports') +notes.mkdir(parents=True, exist_ok=True) +info = model_info(args.model, revision=args.revision) +snapshot = snapshot_download(args.model, revision=info.sha, cache_dir=str(args.cache.resolve()), + allow_patterns=['*.json', '*.jinja', '*.safetensors', '*.txt'], max_workers=8) +metadata = {'model': args.model, 'revision': info.sha, 'precision': 'int4', 'provider': 'webgpu', + 'boundedSerializer': args.bounded, 'extraOptions': args.extra_option} +(root / 'export-info.json').write_text(json.dumps(metadata, indent=2) + '\n') + +def run(label, command): + print(f'\n{label}', flush=True) + with (notes / (label + '.log')).open('w') as log: + process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, cwd=notes) + for line in process.stdout: + print(line, end='', flush=True) + log.write(line) + code = process.wait() + if code: raise SystemExit(f'{label} failed ({code}); see {notes}') + +builder = [sys.executable, str(TOOL / 'scripts/streaming_export.py')] if args.bounded else [sys.executable, '-m', 'onnxruntime_genai.models.builder'] +# MatMul/Gather also quantizes untied input embeddings, essential for the 27B pack. +run('export', builder + ['-m', args.model, '-i', snapshot, '-o', str(root), '-p', 'int4', '-e', 'webgpu', + '-c', str(args.cache.resolve()), '--extra_options', 'exclude_mtp=true', 'state_window=0', + 'op_types_to_quantize=MatMul/Gather', *args.extra_option]) +if not args.skip_native: + run('native-validation', [sys.executable, str(TOOL / 'scripts/validate.py'), str(root)]) +# Keep small exports' original external-data layout; split oversized baseline files. +if any(p.stat().st_size >= 2**31 for p in root.glob('*.onnx.data')): + run('sharding', [sys.executable, str(TOOL / 'scripts/shard_model.py'), str(root)]) +run('browser-adaptation', [sys.executable, str(TOOL / 'scripts/prepare_browser.py'), str(root)]) +run('inspection', [sys.executable, str(TOOL / 'scripts/inspect_model.py'), str(root)]) +print(f'\nReady for browser validation: {root}\nReports: {notes}') diff --git a/js/qwen38-webgpu/export/requirements-export.lock b/js/qwen38-webgpu/export/requirements-export.lock new file mode 100644 index 000000000..43bfc739a --- /dev/null +++ b/js/qwen38-webgpu/export/requirements-export.lock @@ -0,0 +1,41 @@ +annotated-doc==0.0.5 +anyio==4.15.1 +certifi==2026.7.22 +click==8.5.0 +filelock==4.0.12 +flatbuffers==25.12.19 +fsspec==2026.9.0 +h11==0.16.0 +hf-xet==1.6.0 +httpcore==1.0.9 +httpx==0.28.1 +huggingface-hub==1.33.0 +idna==3.20 +jinja2==3.1.6 +markdown-it-py==4.2.0 +markupsafe==3.0.4 +mdurl==0.1.2 +ml-dtypes==0.6.0 +mpmath==1.3.0 +networkx==3.7 +numpy==2.5.3 +onnx==1.23.2 +onnx-ir==1.0.0 +onnxruntime==1.30.0 +onnxruntime-genai==0.17.1 +packaging==26.3 +protobuf==7.36.2 +pygments==2.21.0 +pyyaml==6.0.3 +regex==2026.9.29 +rich==15.0.0 +safetensors==0.8.0 +setuptools==84.0.0 +shellingham==1.5.4 +sympy==1.14.0 +tokenizers==0.23.2 +torch==2.14.1 +tqdm==4.70.1 +transformers==5.18.0 +typer==0.27.2 +typing-extensions==4.16.0 diff --git a/js/qwen38-webgpu/export/scripts/catalog_entry.py b/js/qwen38-webgpu/export/scripts/catalog_entry.py new file mode 100644 index 000000000..ed508a8d0 --- /dev/null +++ b/js/qwen38-webgpu/export/scripts/catalog_entry.py @@ -0,0 +1,23 @@ +"""Print a catalog entry for a newly exported pack; review it before adding it.""" +import argparse +import json +from pathlib import Path + +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument('model_dir', type=Path) +parser.add_argument('--id', required=True) +parser.add_argument('--label', required=True) +parser.add_argument('--family', required=True) +parser.add_argument('--parameters', type=float, required=True) +parser.add_argument('--license', required=True) +parser.add_argument('--license-url', required=True) +args = parser.parse_args() +manifest = json.loads((args.model_dir / 'manifest.json').read_text()) +info = json.loads((args.model_dir / 'export-info.json').read_text()) +assert manifest.get('browserGraph'), 'Prepare/inspect the browser graph first.' +entry = {'id': args.id, 'label': args.label, 'family': args.family, 'parametersB': args.parameters, + 'repo': info['model'], 'revision': info['revision'], 'dtype': 'q4f16', 'runtime': 'genai', + 'loader': 'jspi', 'source': 'local-export', 'downloadMb': round(manifest['totalBytes'] / 1e6), + 'licenseName': args.license, 'licenseUrl': args.license_url, + 'files': [f['name'] for f in manifest['files']] + ['manifest.json']} +print(json.dumps(entry, indent=2)) diff --git a/js/qwen38-webgpu/export/scripts/check_adaptation.py b/js/qwen38-webgpu/export/scripts/check_adaptation.py new file mode 100644 index 000000000..17bbdceb9 --- /dev/null +++ b/js/qwen38-webgpu/export/scripts/check_adaptation.py @@ -0,0 +1,47 @@ +"""Compare original/adapted graph logits and every cache after prefill/decode.""" +import json +import sys +from pathlib import Path + +import numpy as np +import onnxruntime as ort + +root = Path(sys.argv[1]).resolve() +notes = root.parent / (root.name + ".reports") +notes.mkdir(parents=True, exist_ok=True) +config = json.loads((root / "genai_config.json").read_text()) +manifest = json.loads((root / "manifest.json").read_text()) +graph = next(g for g in manifest["graphs"] if g["file"] == config["model"]["decoder"]["filename"]) +original = ort.InferenceSession(str(root / graph["file"]), providers=["CPUExecutionProvider"]) +adapted = ort.InferenceSession(str(root / "model.browser.onnx"), providers=["CPUExecutionProvider"]) +reference = json.loads((notes / "native-validation-cpu.json").read_text())["tests"][0] +symbols = {"batch_size": 1, "past_sequence_length": 0, "kv_cache_dim": config["model"]["decoder"]["head_size"]} +state = {} +for value in graph["inputs"]: + if value["name"].startswith("past"): + shape = [symbols.get(d, d) for d in value["dims"]] + state[value["name"]] = np.zeros(shape, dtype=np.float16) +ids = reference["input_ids"] +past = 0 +checks = [] +for step in range(2): + total = past + len(ids) + feeds = {**state, "input_ids": np.asarray([ids], dtype=np.int64), + "position_ids": np.tile(np.arange(past, total, dtype=np.int64), (3, 1, 1)), + "attention_mask": np.ones((1, total), dtype=np.int64)} + original_outputs = dict(zip([v.name for v in original.get_outputs()], original.run(None, feeds))) + browser_feeds = {k: v for k, v in feeds.items() if k != "attention_mask"} + browser_feeds.update(seqlens_k=np.asarray([total - 1], dtype=np.int32), + total_sequence_length=np.asarray(total, dtype=np.int32)) + browser_outputs = dict(zip([v.name for v in adapted.get_outputs()], adapted.run(None, browser_feeds))) + equal = {name: np.array_equal(value, browser_outputs[name]) for name, value in original_outputs.items()} + assert all(equal.values()), f"Graph adaptation changed outputs: {equal}" + checks.append({"step": step, "inputTokens": len(ids), "outputsCompared": len(equal), "allBitwiseEqual": True}) + for name in state: + output = name.replace("past_key_values.", "present.").replace("past.", "present.") + state[name] = original_outputs[output] + ids = [int(np.argmax(original_outputs["logits"][0, -1]))] + past = total +result = {"status": "passed", "provider": "cpu", "purpose": "offline graph-adaptation equivalence check", "checks": checks} +(notes / "adaptation-equivalence.json").write_text(json.dumps(result, indent=2) + "\n") +print(json.dumps(result, indent=2)) diff --git a/js/qwen38-webgpu/export/scripts/inspect_model.py b/js/qwen38-webgpu/export/scripts/inspect_model.py new file mode 100644 index 000000000..0ef28a138 --- /dev/null +++ b/js/qwen38-webgpu/export/scripts/inspect_model.py @@ -0,0 +1,55 @@ +"""Read graph metadata without loading external weights; emit a browser manifest.""" +import json +import sys +from collections import Counter +from pathlib import Path + +import onnx + +root = Path(sys.argv[1]).resolve() +notes = root.parent / (root.name + ".reports") +notes.mkdir(parents=True, exist_ok=True) + +def tensor_info(value): + tensor = value.type.tensor_type + return { + "name": value.name, + "type": onnx.TensorProto.DataType.Name(tensor.elem_type).lower(), + "dims": [d.dim_param if d.dim_param else d.dim_value for d in tensor.shape.dim], + } + +graphs = [] +for path in sorted(root.glob("*.onnx")): + model = onnx.load(path, load_external_data=False) + data_files = set() + for tensor in model.graph.initializer: + for item in tensor.external_data: + if item.key == "location": + data_files.add(item.value) + graphs.append({ + "file": path.name, + "inputs": [tensor_info(v) for v in model.graph.input], + "outputs": [tensor_info(v) for v in model.graph.output], + "externalData": sorted(data_files), + "operators": dict(sorted(Counter(f"{n.domain or 'ai.onnx'}::{n.op_type}" for n in model.graph.node).items())), + "opsets": {v.domain or "ai.onnx": v.version for v in model.opset_import}, + "int4": [{"name": n.name, "bits": onnx.helper.get_attribute_value(next(a for a in n.attribute if a.name == "bits")), + "blockSize": onnx.helper.get_attribute_value(next(a for a in n.attribute if a.name == "block_size"))} + for n in model.graph.node if n.op_type == "MatMulNBits"], + "largestInitializers": sorted( + [{"name": t.name, "dims": list(t.dims), "type": onnx.TensorProto.DataType.Name(t.data_type).lower(), + "externalBytes": int(next((v.value for v in t.external_data if v.key == "length"), "0"))} + for t in model.graph.initializer], key=lambda t: t["externalBytes"], reverse=True)[:10], + }) +files = [{"name": str(p.relative_to(root)), "bytes": p.stat().st_size} for p in sorted(root.rglob("*")) if p.is_file() and p.name != "manifest.json"] +manifest = {"files": files, "totalBytes": sum(f["bytes"] for f in files), "graphs": graphs} +if (root / "browser-adaptation.json").exists(): + manifest["browserAdaptation"] = json.loads((root / "browser-adaptation.json").read_text()) + manifest["browserGraph"] = manifest["browserAdaptation"]["browserGraph"] +(root / "manifest.json").write_text(json.dumps(manifest, indent=2) + "\n") +(notes / "manifest.json").write_text(json.dumps(manifest, indent=2) + "\n") +for file in files: + print(f"{file['bytes']:>14,} bytes {file['name']}") +print(f"Total: {manifest['totalBytes']:,} bytes") +for graph in graphs: + print(json.dumps(graph, indent=2)) diff --git a/js/qwen38-webgpu/export/scripts/prepare_browser.py b/js/qwen38-webgpu/export/scripts/prepare_browser.py new file mode 100644 index 000000000..98f63626a --- /dev/null +++ b/js/qwen38-webgpu/export/scripts/prepare_browser.py @@ -0,0 +1,79 @@ +"""Expose GQA sequence metadata as host inputs; leave model math/weights intact. + +This adapter only supports batch size one, contiguous unpadded text. It removes +the six attention-mask metadata nodes that ORT explicitly places on CPU. The +original GenAI export is retained for native validation. +""" +import json +import sys +from pathlib import Path + +import onnx +from onnx import TensorProto, helper + +root = Path(sys.argv[1]).resolve() +notes = root.parent / (root.name + ".reports") +notes.mkdir(parents=True, exist_ok=True) +config = json.loads((root / "genai_config.json").read_text()) +original = config["model"]["decoder"]["filename"] +model = onnx.load(root / original, load_external_data=False) +prefix = "/model/attn_mask_reformat/attn_mask_subgraph/" +nodes = [n for n in model.graph.node if n.name.startswith(prefix)] +expected = {"ReduceSum", "Sub", "Sub/Cast", "Shape", "Gather", "Gather/Cast"} +assert {n.name.removeprefix(prefix) for n in nodes} == expected, "Unknown mask subgraph; inspect before adapting" +replacements = { + prefix + "Sub/Cast/output_0": "seqlens_k", + prefix + "Gather/Cast/output_0": "total_sequence_length", +} +removed_outputs = {o for n in nodes for o in n.output} +remaining = [n for n in model.graph.node if n not in nodes] +gqa = [n for n in remaining if n.op_type == "GroupQueryAttention"] +assert gqa and all(n.input[5] in replacements and n.input[6] in replacements for n in gqa) +for node in remaining: + for index, name in enumerate(node.input): + if name in replacements: + node.input[index] = replacements[name] + else: + assert name not in removed_outputs, f"Unexpected mask-subgraph consumer: {node.name}" +del model.graph.node[:] +model.graph.node.extend(remaining) +mask = config["model"]["decoder"]["inputs"]["attention_mask"] +inputs = [v for v in model.graph.input if v.name != mask] +inputs.extend([ + helper.make_tensor_value_info("seqlens_k", TensorProto.INT32, ["batch_size"]), + helper.make_tensor_value_info("total_sequence_length", TensorProto.INT32, []), +]) +del model.graph.input[:] +model.graph.input.extend(inputs) +values = [v for v in model.graph.value_info if v.name not in removed_outputs] +del model.graph.value_info[:] +model.graph.value_info.extend(values) +browser = "model.browser.onnx" +onnx.save_model(model, root / browser) +# ORT's exporter uses an ai.onnx SimplifiedLayerNormalization extension that the +# generic ONNX checker may not recognize. Require the same failure on the untouched +# export; semantic validation is performed by check_adaptation.py through ORT. +checker_warning = None +try: + onnx.checker.check_model(str(root / browser)) +except onnx.checker.ValidationError as error: + try: + onnx.checker.check_model(str(root / original)) + except onnx.checker.ValidationError as baseline: + if str(error).split("\n")[0] != str(baseline).split("\n")[0]: + raise + checker_warning = str(error).split("\n")[0] + else: + raise +adaptation = { + "originalGraph": original, "browserGraph": browser, + "removedNodes": [{"name": n.name, "op": n.op_type} for n in nodes], + "newInputs": {"seqlens_k": "int32[1] = total tokens processed including this step - 1", + "total_sequence_length": "int32 scalar = total tokens processed including this step"}, + "restriction": "batch size 1, contiguous unpadded text only", + "weightsModified": False, + "onnxCheckerWarningAlsoPresentInOriginal": checker_warning, +} +(root / "browser-adaptation.json").write_text(json.dumps(adaptation, indent=2) + "\n") +(notes / "browser-adaptation.json").write_text(json.dumps(adaptation, indent=2) + "\n") +print(json.dumps(adaptation, indent=2)) diff --git a/js/qwen38-webgpu/export/scripts/shard_model.py b/js/qwen38-webgpu/export/scripts/shard_model.py new file mode 100644 index 000000000..ebb2d6e87 --- /dev/null +++ b/js/qwen38-webgpu/export/scripts/shard_model.py @@ -0,0 +1,77 @@ +"""Repack an official single-file export into <=1 GiB tensor-aligned shards. + +Numerical tensors and graph nodes are unchanged. Verify every copied byte before +publishing the new graph and removing the now-unreferenced original data file. +""" +import hashlib +import json +import sys +import os +from pathlib import Path +import onnx +from onnx import external_data_helper + +root = Path(sys.argv[1]).resolve() +notes = root.parent / (root.name + ".reports") +notes.mkdir(parents=True, exist_ok=True) +model_path = root / 'model.onnx' +model = onnx.load(model_path, load_external_data=False) +locations = {entry.value for tensor in model.graph.initializer for entry in tensor.external_data if entry.key == 'location'} +assert locations == {'model.onnx.data'}, 'Expected an unsharded export; do not run this on an already sharded model.' +limit = 1024**3 +original_files = set() +shards = [] +writer = None +index = -1 +checks = [] +try: + for tensor in model.graph.initializer: + ext = {entry.key: entry.value for entry in tensor.external_data} + if 'location' not in ext: + continue + original_files.add(ext['location']) + length = int(ext['length']) + if length > limit: + raise ValueError(f'Tensor exceeds shard limit: {tensor.name}, {length}') + padding = (-writer.tell()) % 64 if writer else 0 + if writer is None or writer.tell() + padding + length > limit: + if writer: writer.close() + index += 1 + location = f'model.onnx.data_{index:03d}' + shards.append(location) + writer = (root / location).open('wb') + padding = 0 + writer.write(b'\0' * padding) + offset = writer.tell() + digest = hashlib.sha256() + with (root / ext['location']).open('rb') as source: + source.seek(int(ext.get('offset', 0))) + remaining = length + while remaining: + data = source.read(min(remaining, 8 * 1024 * 1024)) + if not data: raise EOFError(tensor.name) + writer.write(data); digest.update(data); remaining -= len(data) + tensor.raw_data = b'' + external_data_helper.set_external_data(tensor, location=location, offset=offset, length=length) + tensor.ClearField('raw_data') + tensor.data_location = onnx.TensorProto.EXTERNAL + checks.append({'tensor': tensor.name, 'file': location, 'offset': offset, + 'bytes': length, 'sha256': digest.hexdigest()}) +finally: + if writer: writer.close() +for check in checks: + digest = hashlib.sha256() + with (root / check['file']).open('rb') as source: + source.seek(check['offset']); remaining = check['bytes'] + while remaining: + data = source.read(min(remaining, 8 * 1024 * 1024)); assert data + digest.update(data); remaining -= len(data) + assert digest.hexdigest() == check['sha256'], check['tensor'] +onnx.save_model(model, root / 'model.sharded.part.onnx') +(root / 'model.sharded.part.onnx').replace(model_path) +for name in original_files: + if name not in shards: (root / name).unlink() +report = {'tensorBytesVerified': sum(check['bytes'] for check in checks), 'tensors': checks, + 'shards': [{'file': name, 'bytes': (root / name).stat().st_size} for name in shards]} +(notes / 'sharding.json').write_text(json.dumps(report, indent=2) + '\n') +print(json.dumps({'verifiedTensorCount': len(checks), 'shards': report['shards']}, indent=2)) diff --git a/js/qwen38-webgpu/export/scripts/streaming_export.py b/js/qwen38-webgpu/export/scripts/streaming_export.py new file mode 100644 index 000000000..2c15cfdc7 --- /dev/null +++ b/js/qwen38-webgpu/export/scripts/streaming_export.py @@ -0,0 +1,167 @@ +"""Memory-bounded save adapter around the official builder and ORT quantizer. + +Only use if the baseline exporter exceeds this host's memory ceiling. This +serializes/quantizes one weight at a time with the official DEFAULT quantizer, +writing quantized bytes directly to their final external-data file. No model +operators, quantization settings or inference kernels are replaced. +""" +import gc +import importlib.util +import json +import os +import runpy +import sys +import math +from pathlib import Path + +import onnx +import numpy as np +import onnx_ir as ir +from onnx import helper, numpy_helper, external_data_helper +from onnxruntime.quantization.matmul_nbits_quantizer import DefaultWeightOnlyQuantizer, DefaultWeightOnlyQuantConfig, QuantFormat + +source = Path(importlib.util.find_spec('onnxruntime_genai.models.builder').origin).parent +sys.path.insert(0, str(source)) +from builders.base import Model + + +def memory_bounded_save(self, out_dir): + assert self.onnx_dtype == ir.DataType.INT4 and self.ep == 'webgpu' + assert getattr(self, 'quantization_algo', 'default') == 'default' + assert not getattr(self, 'int4_customized_weight_config', {}) + assert not getattr(self, 'exact_quant_overrides', {}) + assert not self.quant_attrs['use_qdq'] + self.model.graph.sort() + Model.stamp_build_metadata(self.model) + originals = {name: value.const_value for name, value in self.model.graph.initializers.items()} + # Serialize graph metadata without materializing the full FP16 model. + for name, value in self.model.graph.initializers.items(): + tensor = originals[name] + value.const_value = ir.ExternalTensor('pending', 0, 0, tensor.dtype, + shape=tensor.shape, name=name) + proto = ir.to_proto(self.model) + for name, value in self.model.graph.initializers.items(): + value.const_value = originals[name] + del proto.graph.initializer[:] + nodes = list(proto.graph.node) + del proto.graph.node[:] + out = Path(out_dir) + out.mkdir(parents=True, exist_ok=True) + shard_limit = int(os.environ.get('SHARD_BYTES', 1024 ** 3)) + shard_index = 0 + location = self.filename + f'.data_{shard_index:03d}' + shard_paths = [out / location] + stored = {} + records = [] + + with (out / location).open('wb') as data_file: + def store(tensor): + nonlocal data_file, location, shard_index + if tensor.name in stored: + return + raw = tensor.raw_data or numpy_helper.to_array(tensor).tobytes() + if len(raw) > shard_limit: + raise ValueError(f'Tensor exceeds shard limit: {tensor.name}, {len(raw)} bytes') + padding = (-data_file.tell()) % 64 + if data_file.tell() + padding + len(raw) > shard_limit: + data_file.close() + shard_index += 1 + location = self.filename + f'.data_{shard_index:03d}' + shard_paths.append(out / location) + data_file = (out / location).open('wb') + padding = 0 + data_file.write(b'\0' * padding) + offset = data_file.tell() + data_file.write(raw) + length = len(raw) + if not tensor.HasField('raw_data'): + tensor.raw_data = raw + external_data_helper.set_external_data(tensor, location=location, offset=offset, length=length) + tensor.ClearField('raw_data') + for field in ['float_data', 'int32_data', 'int64_data', 'double_data', 'uint64_data', 'string_data']: + tensor.ClearField(field) + tensor.data_location = onnx.TensorProto.EXTERNAL + # A child protobuf retains its parent graph's allocation arena. + # Keeping the mini-graph initializer would therefore retain the + # original FP16 payload even after ClearField('raw_data'). Copy only + # the small external-data descriptor into an independent message. + descriptor = onnx.TensorProto() + descriptor.CopyFrom(tensor) + stored[tensor.name] = descriptor + + excluded = set(self.quant_attrs['nodes_to_exclude']) + ops = set(self.quant_attrs['op_types_to_quantize']) + for index, node in enumerate(nodes): + if node.op_type not in ops or node.name in excluded: + proto.graph.node.append(node) + continue + weight_name = node.input[0 if node.op_type == 'Gather' else 1] + if weight_name not in originals: + proto.graph.node.append(node) + continue + tensor = originals[weight_name] + temporary_weight = None + elements = math.prod(int(dim) for dim in tensor.shape) + if elements * tensor.dtype.bitwidth // 8 >= 2**31: + # Protobuf cannot copy a >2 GiB message into a GraphProto. + # The official quantizer can consume external-data descriptors. + temporary_weight = out / '.quantize-weight.tmp' + array = np.ascontiguousarray(tensor.numpy()) + array.tofile(temporary_weight) + del array + weight = ir.to_proto(ir.ExternalTensor( + str(temporary_weight), 0, temporary_weight.stat().st_size, + tensor.dtype, shape=tensor.shape, name=weight_name)) + else: + weight = ir.to_proto(tensor) + mini = helper.make_graph([node], 'one_weight', [], [], [weight]) + del weight + quantizer = DefaultWeightOnlyQuantizer(DefaultWeightOnlyQuantConfig( + bits=self.quant_attrs['bits'], block_size=self.quant_attrs['matmul_block_size'], + is_symmetric=self.quant_attrs['is_symmetric'], + accuracy_level=self.quant_attrs['accuracy_level'], + quant_format=QuantFormat.QOperator, op_types_to_quantize=tuple(ops))) + result_nodes = quantizer.quantize(node, [mini]) + if temporary_weight: + temporary_weight.unlink() + assert all(n.op_type != node.op_type for n in result_nodes), node.name + proto.graph.node.extend(result_nodes) + result = mini + used = {name for n in result_nodes for name in n.input} + for value in result.initializer: + if value.name in used: + store(value) + records.append({'name': node.name, 'weight': weight_name, 'shardBytes': data_file.tell()}) + print(f'Stream-quantized {node.name} ({index + 1}/{len(nodes)}), {data_file.tell():,} bytes', flush=True) + # LazyTensor caches its cast result; release the temporary FP16 copy. + if isinstance(tensor, ir.LazyTensor): + tensor._tensor = None + del mini, quantizer, result, result_nodes, tensor + gc.collect() + + # Emit only initializers still referenced by the transformed graph. + used = {name for node in proto.graph.node for name in node.input} + for name in sorted(used): + if name in originals and name not in stored: + tensor = originals[name] + store(ir.to_proto(tensor)) + if isinstance(tensor, ir.LazyTensor): + tensor._tensor = None + del tensor + gc.collect() + proto.graph.initializer.extend(stored.values()) + data_file.close() + onnx.save_model(proto, out / self.filename) + notes = out.parent / (out.name + ".reports") + notes.mkdir(parents=True, exist_ok=True) + (notes / "streaming-quantization.json").write_text(json.dumps({ + 'adapter': 'per-weight serialization with official DEFAULT MatMulNBitsQuantizer', + 'settings': self.quant_attrs, 'quantizedNodes': records, + 'externalBytes': sum(path.stat().st_size for path in shard_paths), + 'shards': [{'file': path.name, 'bytes': path.stat().st_size} for path in shard_paths], + }, indent=2, default=str) + '\n') + + +Model.save_model = memory_bounded_save +if __name__ == '__main__': + runpy.run_path(str(source / 'builder.py'), run_name='__main__') diff --git a/js/qwen38-webgpu/export/scripts/validate.py b/js/qwen38-webgpu/export/scripts/validate.py new file mode 100644 index 000000000..555e5c6b6 --- /dev/null +++ b/js/qwen38-webgpu/export/scripts/validate.py @@ -0,0 +1,67 @@ +"""Independent native GenAI sanity check. CPU here is validation, never serving.""" +import json +import os +import argparse +import time +from pathlib import Path + +import numpy as np +import onnxruntime_genai as og +from transformers import AutoTokenizer + +parser = argparse.ArgumentParser() +parser.add_argument("model_dir", type=Path) +parser.add_argument("--provider", choices=["cpu", "webgpu"], default="cpu") +args = parser.parse_args() +root = args.model_dir.resolve() +notes = root.parent / (root.name + ".reports") +notes.mkdir(parents=True, exist_ok=True) +os.chdir(notes) +result = {"provider": args.provider, "purpose": "pre-browser export validation", "tests": []} +try: + config = og.Config(str(root)) + config.clear_providers() + if args.provider != "cpu": + config.append_provider(args.provider) + model = og.Model(config) + tokenizer = AutoTokenizer.from_pretrained(root, local_files_only=True) + for prompt in ["What is the capital of France?", "What is 12 * 13? Answer with the number only."]: + input_ids = tokenizer.apply_chat_template( + [{"role": "user", "content": prompt}], tokenize=True, + add_generation_prompt=True, enable_thinking=False, return_dict=False, + ) + params = og.GeneratorParams(model) + params.set_search_options(do_sample=False, max_length=len(input_ids) + 32) + start = time.perf_counter() + generator = og.Generator(model, params) + generator.append_tokens(np.asarray(input_ids, dtype=np.int32)) + generated = [] + first = None + while not generator.is_done() and len(generated) < 32: + generator.generate_next_token() + generated.append(int(generator.get_next_tokens()[0])) + if first is None: + first = time.perf_counter() + end = time.perf_counter() + item = {"prompt": prompt, "input_ids": input_ids, "generated_ids": generated, + "text": tokenizer.decode(generated, skip_special_tokens=True), + "first_token_ms": (first - start) * 1000 if first else None, + "elapsed_ms": (end - start) * 1000} + result["tests"].append(item) + print(json.dumps(item, indent=2), flush=True) + del generator + result["checks"] = { + "capital": "paris" in result["tests"][0]["text"].lower(), + "arithmetic": "156" in result["tests"][1]["text"], + } + if not all(result["checks"].values()): + raise RuntimeError("Native generation completed but failed the answer sanity checks") + result["status"] = "passed" +except Exception as error: + result["status"] = "failed" + result["error"] = f"{type(error).__name__}: {error}" + print(result["error"], flush=True) +finally: + (notes / f"native-validation-{args.provider}.json").write_text(json.dumps(result, indent=2) + "\n") +if result["status"] != "passed": + raise SystemExit(1) diff --git a/js/qwen38-webgpu/export/setup.sh b/js/qwen38-webgpu/export/setup.sh new file mode 100644 index 000000000..ce1f1bdc8 --- /dev/null +++ b/js/qwen38-webgpu/export/setup.sh @@ -0,0 +1,5 @@ +#!/usr/bin/env bash +set -euo pipefail +cd "$(dirname "$0")" +uv venv --python 3.12 .venv +uv pip install --python .venv/bin/python -r requirements-export.lock diff --git a/js/qwen38-webgpu/index.html b/js/qwen38-webgpu/index.html new file mode 100644 index 000000000..db1b88dc3 --- /dev/null +++ b/js/qwen38-webgpu/index.html @@ -0,0 +1,12 @@ + +Qwen3.8 27B · ONNX WebGPU + +

Qwen3.8 27B · ONNX Runtime WebGPU

+

Text generation on your GPU. Model download: approximately 15.26 GB. Tested on a 48 GiB Mac with Chrome; other hardware and long contexts are unvalidated.

+ +

+ +
Download the model pack first; see README.md. Use current Chrome or Edge on localhost with hardware WebGPU and JSPI support.
+
Measurements

+
+
diff --git a/js/qwen38-webgpu/model.json b/js/qwen38-webgpu/model.json
new file mode 100644
index 000000000..f6cd574f3
--- /dev/null
+++ b/js/qwen38-webgpu/model.json
@@ -0,0 +1,142 @@
+{
+  "id": "Qwen3.8-27B-GenAI-WebGPU-INT4",
+  "repo": "Ruurd/Qwen3.8-27B-ONNX-WebGPU-INT4",
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+}
diff --git a/js/qwen38-webgpu/package-lock.json b/js/qwen38-webgpu/package-lock.json
new file mode 100644
index 000000000..70f8fb068
--- /dev/null
+++ b/js/qwen38-webgpu/package-lock.json
@@ -0,0 +1,2115 @@
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+      "integrity": "sha512-4XP60spRGjSZFf1qYH+dJIkK2znL3zQfl9KkOV9MkkRR/3Dls0dxaBsQPTloEc5BLXWPL9vsOxopxyKoMmDueg==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "esbuild": "^0.27.0 || ^0.28.0",
+        "fdir": "^6.5.0",
+        "picomatch": "^4.0.3",
+        "postcss": "^8.5.6",
+        "rollup": "^4.43.0",
+        "tinyglobby": "^0.2.15"
+      },
+      "bin": {
+        "vite": "bin/vite.js"
+      },
+      "engines": {
+        "node": "^20.19.0 || >=22.12.0"
+      },
+      "funding": {
+        "url": "https://github.com/vitejs/vite?sponsor=1"
+      },
+      "optionalDependencies": {
+        "fsevents": "~2.3.3"
+      },
+      "peerDependencies": {
+        "@types/node": "^20.19.0 || >=22.12.0",
+        "jiti": ">=1.21.0",
+        "less": "^4.0.0",
+        "lightningcss": "^1.21.0",
+        "sass": "^1.70.0",
+        "sass-embedded": "^1.70.0",
+        "stylus": ">=0.54.8",
+        "sugarss": "^5.0.0",
+        "terser": "^5.16.0",
+        "tsx": "^4.8.1",
+        "yaml": "^2.4.2"
+      },
+      "peerDependenciesMeta": {
+        "@types/node": {
+          "optional": true
+        },
+        "jiti": {
+          "optional": true
+        },
+        "less": {
+          "optional": true
+        },
+        "lightningcss": {
+          "optional": true
+        },
+        "sass": {
+          "optional": true
+        },
+        "sass-embedded": {
+          "optional": true
+        },
+        "stylus": {
+          "optional": true
+        },
+        "sugarss": {
+          "optional": true
+        },
+        "terser": {
+          "optional": true
+        },
+        "tsx": {
+          "optional": true
+        },
+        "yaml": {
+          "optional": true
+        }
+      }
+    }
+  }
+}
diff --git a/js/qwen38-webgpu/package.json b/js/qwen38-webgpu/package.json
new file mode 100644
index 000000000..606991d14
--- /dev/null
+++ b/js/qwen38-webgpu/package.json
@@ -0,0 +1,23 @@
+{
+  "name": "qwen38-27b-onnx-webgpu",
+  "version": "0.1.0",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "dev": "vite --host 127.0.0.1",
+    "build": "vite build",
+    "test:browser": "node test-browser.mjs",
+    "download:model": "node download-model.mjs"
+  },
+  "dependencies": {
+    "@huggingface/transformers": "4.3.0",
+    "onnxruntime-web": "1.31.0-dev.20260914-8d85527a0"
+  },
+  "devDependencies": {
+    "vite": "7.3.6",
+    "playwright": "1.63.0"
+  },
+  "engines": {
+    "node": ">=20.19"
+  }
+}
diff --git a/js/qwen38-webgpu/src/inference.worker.js b/js/qwen38-webgpu/src/inference.worker.js
new file mode 100644
index 000000000..e934be6af
--- /dev/null
+++ b/js/qwen38-webgpu/src/inference.worker.js
@@ -0,0 +1,284 @@
+import * as ort from 'onnxruntime-web/jspi';
+import wasmUrl from 'onnxruntime-web/ort-wasm-simd-threaded.jspi.wasm?url';
+import wasmModuleUrl from 'onnxruntime-web/ort-wasm-simd-threaded.jspi.mjs?url';
+import { AutoTokenizer, env } from '@huggingface/transformers';
+
+// Batch-one text generation for the official GenAI export. All model operators
+// run on WebGPU; only tokenization, sequence metadata and sampling run in JS.
+let session, tokenizer, chatTemplate, config, graph, bindings, device, modelRoot;
+let activeRequestId = 0;
+const report = { runtimeVersion: ort.env.versions.web, executionProviders: ['webgpu'], cpuFallbackDisabled: true, messages: [] };
+const tick = () => new Promise(resolve => setTimeout(resolve, 0));
+function log(level, ...args) {
+  const text = args.map(String).join(' ');
+  report.messages.push({ level, text });
+  if (level === 'info') self.postMessage({ type: 'progress', requestId: activeRequestId, message: text });
+  else console[level](...args);
+}
+function heap() { return performance.memory ? { usedJSHeapSize: performance.memory.usedJSHeapSize } : null; }
+async function json(url) {
+  const response = await fetch(url);
+  if (!response.ok) throw new Error(`${url}: HTTP ${response.status}`);
+  return response.json();
+}
+const modelUrl = file => `${modelRoot}/${file}`;
+// Match layer-specific names using the generated config, rather than guessing
+// that every decoder layer has an ordinary key/value cache.
+function cacheBindings() {
+  const pairs = [
+    ['past_key_names', 'present_key_names'], ['past_value_names', 'present_value_names'],
+    ['past_conv_names', 'present_conv_names'], ['past_recurrent_names', 'present_recurrent_names'],
+  ];
+  const result = [];
+  const decoder = config.model.decoder;
+  for (const [past, present] of pairs) {
+    if (!decoder.inputs[past]) continue;
+    for (let layer = 0; layer < decoder.num_hidden_layers; layer++) {
+      const input = decoder.inputs[past].replace('%d', layer);
+      if (!session.inputNames.includes(input)) continue;
+      const output = decoder.outputs[present].replace('%d', layer);
+      if (!session.outputNames.includes(output)) throw new Error(`Missing output for ${input}: ${output}`);
+      result.push({ input, output, metadata: graph.inputs.find((v) => v.name === input) });
+    }
+  }
+  const supported = new Set([...result.map((v) => v.input), decoder.inputs.input_ids,
+    decoder.inputs.attention_mask, decoder.inputs.position_ids, 'seqlens_k', 'total_sequence_length']);
+  for (const name of session.inputNames) {
+    if (!supported.has(name)) throw new Error(`Unimplemented graph input: ${name}`);
+  }
+  return result;
+}
+
+async function loadModel(modelId) {
+  modelRoot = `/models/onnx/${encodeURIComponent(modelId)}`;
+  if (session) return;
+  const loadStart = performance.now();
+  if (typeof WebAssembly.Suspending !== 'function') throw new Error('This large model requires WebAssembly JSPI. Use current Chrome or Edge.');
+  report.status = 'loading';
+  log('info', 'Preparing WebGPU device…');
+  if (!navigator.gpu) throw new Error('WebGPU unavailable. Use current Chrome or Edge on localhost.');
+  const adapter = await navigator.gpu.requestAdapter({ powerPreference: 'high-performance' });
+  if (!adapter || adapter.info.isFallbackAdapter) throw new Error('A hardware WebGPU adapter is required.');
+  if (!adapter.features.has('shader-f16')) throw new Error('This FP16/INT4 export requires shader-f16.');
+  const requiredFeatures = ['shader-f16'];
+  if (adapter.features.has('subgroups')) requiredFeatures.push('subgroups');
+  const requiredLimits = {
+    maxBufferSize: adapter.limits.maxBufferSize,
+    maxStorageBufferBindingSize: adapter.limits.maxStorageBufferBindingSize,
+  };
+  device = await adapter.requestDevice({ requiredFeatures, requiredLimits });
+  device.addEventListener('uncapturederror', (e) => log('error', 'WebGPU:', e.error.message));
+  device.lost.then((info) => log('error', 'WebGPU device lost:', info.reason, info.message));
+  report.gpu = { vendor: adapter.info.vendor, architecture: adapter.info.architecture,
+    device: adapter.info.device, description: adapter.info.description,
+    isFallbackAdapter: adapter.info.isFallbackAdapter,
+    adapterFeatures: [...adapter.features], deviceFeatures: [...device.features],
+    deviceLimits: requiredLimits };
+  ort.env.webgpu.device = device;
+  ort.env.wasm.numThreads = 1;
+  ort.env.wasm.wasmPaths = { wasm: wasmUrl, mjs: wasmModuleUrl };
+  ort.env.logLevel = 'warning';
+
+  const manifest = await json(modelUrl('manifest.json'));
+  config = await json(modelUrl('genai_config.json'));
+  const graphFile = manifest.browserGraph;
+  if (!graphFile) throw new Error("Browser-adapted graph missing. Run prepare_browser.py and inspect_model.py.");
+  graph = manifest.graphs.find((g) => g.file === graphFile);
+  if (!graph) throw new Error('Decoder graph missing from manifest. Run scripts/inspect_model.py.');
+  if (manifest.graphs.some((g) => g.file.includes('mtp')) || config.model.mtp) throw new Error('Export only the decoder with exclude_mtp=true.');
+  report.graphFile = graphFile;
+  report.browserAdaptation = graphFile !== config.model.decoder.filename ? manifest.browserAdaptation : null;
+  report.exportBytes = manifest.totalBytes;
+  report.files = manifest.files;
+  const downloadStart = performance.now();
+  const externalData = [];
+  let payloadBytes = 0;
+  // Fetch sequentially; retain exact external locations from the ONNX graph.
+  for (const path of graph.externalData) {
+    log('info', 'Downloading', path);
+    const response = await fetch(modelUrl(path));
+    if (!response.ok) throw new Error(`Weight download failed: ${response.status}`);
+    const data = await response.blob();
+    const expectedBytes = manifest.files.find(file => file.name === path)?.bytes;
+    if (expectedBytes !== undefined && data.size !== expectedBytes) throw new Error(`Incomplete weight file: ${path}`);
+    payloadBytes += data.size;
+    externalData.push({ path, data });
+  }
+  const response = await fetch(modelUrl(graph.file));
+  if (!response.ok) throw new Error(`Graph download failed: ${response.status}`);
+  const modelBytes = new Uint8Array(await response.arrayBuffer());
+  payloadBytes += modelBytes.byteLength;
+  report.graphAndWeightDownloadBytes = payloadBytes;
+  report.downloadMs = performance.now() - downloadStart;
+  const preferredOutputLocation = Object.fromEntries(graph.outputs.map((v) =>
+    [v.name, v.name === config.model.decoder.outputs.logits ? 'cpu' : 'gpu-buffer']));
+  log('info', 'Creating WebGPU inference session…');
+  const sessionStart = performance.now();
+  session = await ort.InferenceSession.create(modelBytes, {
+    executionProviders: ['webgpu'], externalData, preferredOutputLocation,
+    freeDimensionOverrides: { batch_size: 1 },
+    // Explicitly reject fallback to WASM CPU inference.
+    extra: { session: { disable_cpu_ep_fallback: '1' } },
+    logSeverityLevel: 2,
+  });
+  report.sessionCreateMs = performance.now() - sessionStart;
+  bindings = cacheBindings();
+  report.cacheTensorCount = bindings.length;
+  report.graphInputs = session.inputNames;
+  report.graphOutputs = session.outputNames;
+
+  env.allowRemoteModels = false;
+  env.allowLocalModels = true;
+  env.useBrowserCache = false;
+  env.localModelPath = '/models/onnx/';
+  tokenizer = await AutoTokenizer.from_pretrained(modelId, { local_files_only: true });
+  const templateResponse = await fetch(modelUrl('chat_template.jinja'));
+  if (!templateResponse.ok) throw new Error('Exported chat_template.jinja is missing.');
+  chatTemplate = await templateResponse.text();
+  report.loadMs = performance.now() - loadStart;
+  report.modelResources = performance.getEntriesByType('resource')
+    .filter((r) => new URL(r.name).pathname.startsWith(modelRoot + '/'))
+    .map((r) => ({ name: new URL(r.name).pathname, decodedBodySize: r.decodedBodySize, transferSize: r.transferSize }));
+  report.downloadedModelResourceBytes = report.modelResources.reduce((n, r) => n + r.decodedBodySize, 0);
+  report.heapAfterLoad = heap();
+  report.status = 'ready';
+}
+
+function typedTensor(type, values, dims) {
+  const constructors = { int64: BigInt64Array, int32: Int32Array, float16: Uint16Array,
+    float: Float32Array, float32: Float32Array };
+  const Constructor = constructors[type];
+  if (!Constructor) throw new Error(`Unsupported graph tensor type: ${type}`);
+  const data = typeof values === 'number' ? new Constructor(values) :
+    Constructor.from(values, type === 'int64' ? BigInt : (v) => v);
+  return new ort.Tensor(type === 'float' ? 'float32' : type, data, dims);
+}
+function initialState(metadata) {
+  const symbols = { batch_size: 1, past_sequence_length: 0, kv_cache_dim: config.model.decoder.head_size };
+  const dims = metadata.dims.map((v) => {
+    if (typeof v === 'number') return v;
+    if (v in symbols) return symbols[v];
+    throw new Error(`Unresolved state dimension: ${v}`);
+  });
+  return typedTensor(metadata.type, dims.reduce((a, b) => a * b, 1), dims);
+}
+function halfToFloat(bits) {
+  const sign = bits & 0x8000 ? -1 : 1;
+  const exponent = (bits >> 10) & 31;
+  const fraction = bits & 1023;
+  if (exponent === 31) return fraction ? NaN : sign * Infinity;
+  return sign * (exponent ? 2 ** (exponent - 15) * (1 + fraction / 1024) : 2 ** -14 * fraction / 1024);
+}
+async function sample(logits, temperature = 0) {
+  const data = await logits.getData();
+  const vocab = logits.dims.at(-1);
+  const offset = data.length - vocab;
+  let best = -Infinity, token = -1;
+  const scores = new Float32Array(vocab);
+  for (let i = 0; i < vocab; i++) {
+    const value = logits.type === 'float16' && data instanceof Uint16Array ? halfToFloat(data[offset + i]) : data[offset + i];
+    scores[i] = value;
+    if (Number.isNaN(value)) throw new Error('NaN in logits.');
+    if (value > best) { best = value; token = i; }
+  }
+  if (token < 0 || !Number.isFinite(best)) throw new Error('Invalid logits.');
+  if (temperature > 0) {
+    let total = 0;
+    for (let i = 0; i < vocab; i++) total += scores[i] = Math.exp((scores[i] - best) / temperature);
+    let target = Math.random() * total;
+    for (let i = 0; i < vocab; i++) if ((target -= scores[i]) <= 0) return i;
+  }
+  return token;
+}
+
+async function generate({ messages, maxTokens, temperature, enableThinking }) {
+  if (!Number.isInteger(maxTokens) || maxTokens < 1 || maxTokens > 1024) throw new Error("maxTokens must be between 1 and 1024.");
+  if (!Number.isFinite(temperature) || temperature < 0 || temperature > 2) throw new Error("Invalid temperature.");
+  if (!session || !tokenizer) throw new Error('Load the model first.');
+  const inputIds = tokenizer.apply_chat_template(messages, {
+    tokenize: true, return_tensor: false, return_dict: false,
+    add_generation_prompt: true, enable_thinking: enableThinking, chat_template: chatTemplate,
+  });
+  if (!Array.isArray(inputIds) || !inputIds.every(Number.isInteger)) throw new Error('Unexpected tokenizer output.');
+  if (inputIds.length + maxTokens > config.model.context_length) throw new Error("Prompt exceeds model context length.");
+  const decoder = config.model.decoder;
+  const byName = Object.fromEntries(graph.inputs.map((v) => [v.name, v]));
+  let state = Object.fromEntries(bindings.map((v) => [v.input, initialState(v.metadata)]));
+  let tokens = inputIds, pastLength = 0, firstTokenAt;
+  const generated = [], steps = [];
+  const start = performance.now();
+  const eos = new Set([config.model.eos_token_id].flat());
+  try {
+    for (let i = 0; i < maxTokens; i++) {
+      const feeds = { ...state };
+      const ephemeral = [];
+      const put = (name, values, dims) => {
+        if (!name || !session.inputNames.includes(name)) return;
+        const tensor = typedTensor(byName[name].type, values, dims);
+        feeds[name] = tensor; ephemeral.push(tensor);
+      };
+      put(decoder.inputs.input_ids, tokens, [1, tokens.length]);
+      put(decoder.inputs.attention_mask, Array(pastLength + tokens.length).fill(1), [1, pastLength + tokens.length]);
+      put('seqlens_k', [pastLength + tokens.length - 1], [1]);
+      put('total_sequence_length', [pastLength + tokens.length], []);
+      const positions = tokens.map((_, j) => pastLength + j);
+      put(decoder.inputs.position_ids, [...positions, ...positions, ...positions], [3, 1, tokens.length]);
+      let outputs;
+      try { outputs = await session.run(feeds); }
+      finally { for (const tensor of ephemeral) tensor.dispose(); }
+      try {
+        const token = await sample(outputs[decoder.outputs.logits], temperature);
+        if (firstTokenAt === undefined) firstTokenAt = performance.now();
+        generated.push(token);
+        const nextState = {};
+        for (const binding of bindings) {
+          const value = outputs[binding.output];
+          if (!value) throw new Error(`Missing state output: ${binding.output}`);
+          if (value.location !== 'gpu-buffer') throw new Error(`State left GPU: ${binding.output} (${value.location})`);
+          nextState[binding.input] = value;
+        }
+        steps.push({ step: i, inputTokens: tokens.length, pastLength, stateTensorsRetained: bindings.length,
+          allStateOnGPU: true, kvLength: nextState[bindings.find((v) => v.input.startsWith('past_key_values.')).input].dims[2] });
+        for (const tensor of Object.values(state)) tensor.dispose();
+        state = nextState;
+        pastLength += tokens.length;
+        tokens = [token];
+        if (eos.has(token)) break;
+      } finally {
+        const retained = new Set(Object.values(state));
+        for (const tensor of Object.values(outputs)) if (!retained.has(tensor)) tensor.dispose();
+      }
+      await tick();
+    }
+    const end = performance.now();
+    const result = { inputIds, generatedIds: generated,
+      promptTokens: inputIds.length, completionTokens: generated.length,
+      text: tokenizer.decode(generated, { skip_special_tokens: true }),
+      generatedTokens: generated.length, firstTokenMs: firstTokenAt - start,
+      tokensPerSecondAfterFirst: generated.length > 1 ? (generated.length - 1) * 1000 / (end - firstTokenAt) : null,
+      elapsedMs: end - start, heap: heap(), steps };
+    result.decodeTokensPerSecond = result.tokensPerSecondAfterFirst;
+    report.lastGeneration = result;
+    return result;
+  } finally { for (const tensor of Object.values(state)) tensor.dispose(); }
+}
+
+// Serialize requests so cache/session ownership cannot overlap.
+let queue = Promise.resolve();
+self.onmessage = ({ data }) => {
+  queue = queue.then(async () => {
+    activeRequestId = data.requestId;
+    try {
+      let value;
+      if (data.type === 'load') {
+        await loadModel(data.modelId);
+        value = report;
+      } else if (data.type === 'generate') {
+        value = await generate(data);
+      } else throw new Error('Unknown GenAI worker request.');
+      self.postMessage({ type: 'result', requestId: data.requestId, value });
+    } catch (error) {
+      self.postMessage({ type: 'error', requestId: data.requestId, error: String(error) });
+    }
+  });
+};
diff --git a/js/qwen38-webgpu/src/main.js b/js/qwen38-webgpu/src/main.js
new file mode 100644
index 000000000..7ef36eb4e
--- /dev/null
+++ b/js/qwen38-webgpu/src/main.js
@@ -0,0 +1,54 @@
+const modelId = 'Qwen3.8-27B-GenAI-WebGPU-INT4';
+let worker, ready = false, busy = false, requestId = 0;
+const pending = new Map();
+const el = id => document.getElementById(id);
+function controls() { el('load').disabled = busy || ready; el('generate').disabled = busy || !ready; el('unload').disabled = !worker; }
+function unload() {
+  worker?.terminate(); worker = null; ready = false; busy = false;
+  for (const { reject } of pending.values()) reject(new Error('Model unloaded.'));
+  pending.clear(); controls(); el('status').textContent = 'Model unloaded.';
+}
+function createWorker() {
+  worker = new Worker(new URL('./inference.worker.js', import.meta.url), { type: 'module' });
+  worker.onmessage = ({ data }) => {
+    if (data.type === 'progress') { el('status').textContent = data.message; return; }
+    const task = pending.get(data.requestId); if (!task) return;
+    pending.delete(data.requestId);
+    data.type === 'error' ? task.reject(new Error(data.error)) : task.resolve(data.value);
+  };
+  worker.onerror = event => {
+    for (const { reject } of pending.values()) reject(new Error(event.message));
+    pending.clear();
+  };
+}
+function request(message) {
+  return new Promise((resolve, reject) => {
+    const id = ++requestId; pending.set(id, { resolve, reject }); worker.postMessage({ ...message, requestId: id });
+  });
+}
+async function load() {
+  if (busy || ready) return;
+  busy = true; controls(); createWorker();
+  try {
+    const report = await request({ type: 'load', modelId });
+    el('metrics').textContent = JSON.stringify(report, null, 2); ready = true;
+    el('status').textContent = 'Model ready. All model operators run on WebGPU; CPU inference fallback is disabled.';
+    return report;
+  } catch (error) { unload(); el('status').textContent = String(error); throw error; }
+  finally { busy = false; controls(); }
+}
+async function generate(prompt = el('prompt').value) {
+  if (busy || !ready) throw new Error('Load the model first.');
+  busy = true; controls(); el('status').textContent = 'Generating locally…';
+  try {
+    const result = await request({ type: 'generate', messages: [{ role: 'user', content: prompt }], maxTokens: 32, temperature: 0, enableThinking: false });
+    el('output').textContent = result.text; el('metrics').textContent = JSON.stringify(result, null, 2);
+    el('status').textContent = 'Generation complete.'; return result;
+  } catch (error) { el('status').textContent = String(error); throw error; }
+  finally { busy = false; controls(); }
+}
+el('load').onclick = () => void load().catch(() => {});
+el('generate').onclick = () => void generate().catch(() => {});
+el('unload').onclick = unload;
+window.qwenDemo = { load, generate, unload };
+controls();
diff --git a/js/qwen38-webgpu/test-browser.mjs b/js/qwen38-webgpu/test-browser.mjs
new file mode 100644
index 000000000..055874469
--- /dev/null
+++ b/js/qwen38-webgpu/test-browser.mjs
@@ -0,0 +1,28 @@
+import { chromium } from 'playwright';
+import { mkdir, writeFile } from 'node:fs/promises';
+const origin = process.env.DEMO_URL ?? 'http://127.0.0.1:5174';
+const context = await chromium.launchPersistentContext('', { channel: 'chrome', headless: true, ignoreDefaultArgs: ['--enable-unsafe-swiftshader'] });
+const report = { chrome: context.browser().version(), requests: [], warnings: [], tests: [] };
+try {
+  const page = context.pages()[0];
+  page.on('console', m => { if (['warning', 'error'].includes(m.type())) report.warnings.push(m.text()); });
+  page.on('pageerror', e => report.warnings.push(String(e)));
+  await page.route('**/*', route => {
+    const request = route.request(); report.requests.push({ url: request.url(), method: request.method() });
+    return new URL(request.url()).origin === origin && ['GET', 'HEAD'].includes(request.method()) ? route.continue() : route.abort();
+  });
+  await page.goto(origin);
+  report.load = await page.evaluate(() => window.qwenDemo.load());
+  for (const prompt of ['What is 12 * 13? Answer with the number only.', 'Explain how canals shaped Utrecht in at least forty words.']) {
+    report.tests.push(await page.evaluate(prompt => window.qwenDemo.generate(prompt), prompt));
+  }
+  report.success = report.load.cpuFallbackDisabled && !report.load.gpu.isFallbackAdapter && report.load.cacheTensorCount === 128
+    && report.tests[0].text.trim() === '156' && report.tests[1].completionTokens >= 20
+    && report.tests.every(r => r.steps.every(s => s.allStateOnGPU && s.stateTensorsRetained === 128));
+  await page.evaluate(() => window.qwenDemo.unload());
+  console.log(JSON.stringify({ success: report.success, loadMs: report.load.loadMs, tests: report.tests.map(r => ({ text: r.text, tokens: r.completionTokens, tps: r.tokensPerSecondAfterFirst })) }, null, 2));
+} catch (error) { report.error = String(error); console.error(error); }
+finally {
+  await mkdir('results', { recursive: true }); await writeFile('results/browser.json', JSON.stringify(report, null, 2) + '\n'); await context.close();
+}
+if (!report.success) process.exitCode = 1;
diff --git a/js/qwen38-webgpu/validation.json b/js/qwen38-webgpu/validation.json
new file mode 100644
index 000000000..26e811819
--- /dev/null
+++ b/js/qwen38-webgpu/validation.json
@@ -0,0 +1,69 @@
+{
+  "date": "2026-10-06",
+  "chrome": "154.0.8037.93",
+  "runtime": "1.31.0-dev.20260914-8d85527a0",
+  "hardware": {
+    "vendor": "apple",
+    "architecture": "metal-3",
+    "device": "",
+    "description": "",
+    "isFallbackAdapter": false,
+    "adapterFeatures": [
+      "depth32float-stencil8",
+      "rg11b10ufloat-renderable",
+      "texture-formats-tier1",
+      "bgra8unorm-storage",
+      "texture-compression-bc",
+      "dual-source-blending",
+      "core-features-and-limits",
+      "float32-filterable",
+      "indirect-first-instance",
+      "texture-compression-astc-sliced-3d",
+      "float32-blendable",
+      "subgroup-size-control",
+      "texture-compression-astc",
+      "texture-compression-etc2",
+      "depth-clip-control",
+      "texture-compression-bc-sliced-3d",
+      "shader-f16",
+      "texture-formats-tier2",
+      "clip-distances",
+      "timestamp-query",
+      "primitive-index",
+      "texture-component-swizzle",
+      "subgroups"
+    ],
+    "deviceFeatures": [
+      "core-features-and-limits",
+      "shader-f16",
+      "subgroups"
+    ],
+    "deviceLimits": {
+      "maxBufferSize": 4294967292,
+      "maxStorageBufferBindingSize": 4294967292
+    }
+  },
+  "packBytes": 15256750346,
+  "downloadBytes": 15235781067,
+  "loadMs": 18399.5,
+  "sessionCreateMs": 7218,
+  "cacheTensorCount": 128,
+  "cpuFallbackDisabled": true,
+  "success": true,
+  "tests": [
+    {
+      "text": "156",
+      "completionTokens": 4,
+      "firstTokenMs": 1457.4000000022352,
+      "tokensPerSecondAfterFirst": 6.684491978654013,
+      "allStatesOnGpu": true
+    },
+    {
+      "text": "Utrecht\u2019s urban layout was fundamentally defined by its canal system, which created a distinctive concentric ring structure. These waterways facilitated trade and transportation, allowing the",
+      "completionTokens": 32,
+      "firstTokenMs": 496.80000000074506,
+      "tokensPerSecondAfterFirst": 10.018744748241053,
+      "allStatesOnGpu": true
+    }
+  ]
+}
diff --git a/js/qwen38-webgpu/vite.config.js b/js/qwen38-webgpu/vite.config.js
new file mode 100644
index 000000000..881e7f869
--- /dev/null
+++ b/js/qwen38-webgpu/vite.config.js
@@ -0,0 +1,26 @@
+import { createReadStream, statSync } from 'node:fs';
+import { resolve, sep } from 'node:path';
+import { fileURLToPath } from 'node:url';
+import { defineConfig } from 'vite';
+const id = 'Qwen3.8-27B-GenAI-WebGPU-INT4';
+const modelDir = resolve(process.env.MODEL_DIR ?? fileURLToPath(new URL(`./.models/${id}/`, import.meta.url)));
+export default defineConfig({
+  plugins: [{ name: 'local-model-files', configureServer(server) {
+    server.middlewares.use((req, res, next) => {
+      const prefix = `/models/onnx/${id}/`, path = new URL(req.url, 'http://localhost').pathname;
+      if (!path.startsWith(prefix) || !['GET', 'HEAD'].includes(req.method)) return next();
+      let file;
+      try { file = resolve(modelDir, decodeURIComponent(path.slice(prefix.length))); }
+      catch { res.writeHead(400).end(); return; }
+      if (!file.startsWith(modelDir + sep)) { res.writeHead(403).end(); return; }
+      let size;
+      try { const stat = statSync(file); if (!stat.isFile()) throw new Error(); size = stat.size; }
+      catch { res.writeHead(404).end('Model file missing; see README.md'); return; }
+      res.writeHead(200, { 'Content-Length': size, 'Content-Type': 'application/octet-stream', 'Cache-Control': 'no-store' });
+      if (req.method === 'HEAD') { res.end(); return; }
+      const stream = createReadStream(file, { highWaterMark: 4 * 1024 * 1024 });
+      stream.on('error', () => res.destroy()); res.on('close', () => stream.destroy()); stream.pipe(res);
+    });
+  } }],
+  build: { target: 'es2022' },
+});