feat: add ONNX conversion script for Transformers.js deployment

Add convert_onnx.py that mirrors convert_gguf.py's structure:
- Loads base Qwen3 model, merges SFT + GRPO adapters
- Exports to ONNX via Optimum (text-generation-with-past task)
- Supports Q4 (MatMulNBits), Q8, FP16, and FP32 output
- Uploads to separate HF repo (e.g. tobil/qmd-query-expansion-1.7B-ONNX)
- Writes Transformers.js compatibility config
- Includes model card with usage example

Usage:
    uv run convert_onnx.py --size 1.7B
    uv run convert_onnx.py --size 1.7B --quantize q4 --no-upload

Also adds `just convert-onnx` and `just convert-gguf` tasks.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

finetune/Justfile

@@ -21,6 +21,12 @@ analyze:
 prepare:
     QMD_BASE_MODEL=Qwen/Qwen3-1.7B uv run dataset/prepare_data.py --seed 42
 
+convert-onnx size="1.7B":
+    uv run convert_onnx.py --size {{size}}
+
+convert-gguf size="1.7B":
+    uv run convert_gguf.py --size {{size}}
+
 train-local:
     just prepare
     HF_TOKEN=${HF_TOKEN} uv run torchrun --standalone --nproc_per_node auto \

finetune/convert_onnx.py

@@ -0,0 +1,334 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "transformers>=4.36.0",
+#     "peft>=0.7.0",
+#     "torch>=2.0.0",
+#     "accelerate>=0.24.0",
+#     "huggingface_hub>=0.20.0",
+#     "sentencepiece>=0.1.99",
+#     "protobuf>=3.20.0",
+#     "numpy",
+#     "optimum>=1.17.0",
+#     "onnx>=1.15.0",
+#     "onnxruntime>=1.17.0",
+# ]
+# ///
+"""
+Convert QMD query expansion model to ONNX format for Transformers.js.
+
+Loads the base model, merges SFT and GRPO adapters, then exports to ONNX
+with quantization for browser deployment via Transformers.js + WebGPU.
+
+Usage:
+    uv run convert_onnx.py --size 1.7B
+    uv run convert_onnx.py --size 1.7B --no-upload
+    uv run convert_onnx.py --base Qwen/Qwen3-1.7B \
+                           --sft tobil/qmd-query-expansion-1.7B-sft \
+                           --grpo tobil/qmd-query-expansion-1.7B-grpo \
+                           --output tobil/qmd-query-expansion-1.7B-ONNX
+
+Quantization options:
+    --quantize q4    MatMulNBits 4-bit (default, smallest)
+    --quantize q8    8-bit dynamic quantization
+    --quantize fp16  FP16 (requires GPU export)
+    --quantize none  No quantization (FP32, ~7GB)
+"""
+
+import argparse
+import json
+import os
+import shutil
+import sys
+from pathlib import Path
+
+import torch
+from huggingface_hub import HfApi, login
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+PRESETS = {
+    "1.7B": {
+        "base": "Qwen/Qwen3-1.7B",
+        "sft": "tobil/qmd-query-expansion-1.7B-sft",
+        "grpo": "tobil/qmd-query-expansion-1.7B-grpo",
+        "output": "tobil/qmd-query-expansion-1.7B-ONNX",
+    },
+    "4B": {
+        "base": "Qwen/Qwen3-4B",
+        "sft": "tobil/qmd-query-expansion-4B-sft",
+        "grpo": "tobil/qmd-query-expansion-4B-grpo",
+        "output": "tobil/qmd-query-expansion-4B-ONNX",
+    },
+}
+
+
+def merge_adapters(base_model: str, sft_model: str, grpo_model: str) -> tuple:
+    """Load base model, merge SFT + GRPO adapters, return (model, tokenizer)."""
+    print(f"\nStep 1: Loading base model {base_model}...")
+    model = AutoModelForCausalLM.from_pretrained(
+        base_model, torch_dtype=torch.float32, trust_remote_code=True,
+    )
+
+    print(f"Step 2: Merging SFT adapter {sft_model}...")
+    model = PeftModel.from_pretrained(model, sft_model)
+    model = model.merge_and_unload()
+
+    print(f"Step 3: Merging GRPO adapter {grpo_model}...")
+    model = PeftModel.from_pretrained(model, grpo_model)
+    model = model.merge_and_unload()
+
+    tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+    return model, tokenizer
+
+
+def export_onnx(model, tokenizer, output_dir: str):
+    """Export merged model to ONNX using Optimum."""
+    from optimum.exporters.onnx import main_export
+
+    # Save merged model to temp dir first (Optimum needs HF format on disk)
+    merged_dir = "/tmp/merged_model_onnx"
+    print(f"\nStep 4: Saving merged model to {merged_dir}...")
+    model.save_pretrained(merged_dir, safe_serialization=True)
+    tokenizer.save_pretrained(merged_dir)
+
+    print(f"\nStep 5: Exporting to ONNX at {output_dir}...")
+    main_export(
+        model_name_or_path=merged_dir,
+        output=output_dir,
+        task="text-generation-with-past",
+        device="cpu",
+        fp16=False,
+    )
+
+    # Clean up temp merged dir
+    shutil.rmtree(merged_dir, ignore_errors=True)
+
+
+def quantize_onnx(onnx_dir: str, quantize_type: str):
+    """Quantize the exported ONNX model."""
+    if quantize_type == "none":
+        print("\nSkipping quantization (FP32).")
+        return
+
+    model_path = Path(onnx_dir) / "model.onnx"
+    if not model_path.exists():
+        # Optimum may produce decoder_model.onnx for text-generation-with-past
+        candidates = list(Path(onnx_dir).glob("*.onnx"))
+        if not candidates:
+            print("  WARNING: No .onnx files found to quantize.")
+            return
+        model_path = candidates[0]
+
+    print(f"\nStep 6: Quantizing {model_path.name} ({quantize_type})...")
+
+    if quantize_type == "q4":
+        try:
+            from onnxruntime.quantization import matmul_nbits_quantizer
+            quant = matmul_nbits_quantizer.MatMulNBitsQuantizer(
+                model=str(model_path),
+                block_size=32,
+                is_symmetric=True,
+                bits=4,
+            )
+            quant.process()
+            q_path = model_path.with_name(
+                model_path.stem + "_q4" + model_path.suffix,
+            )
+            quant.model.save(str(q_path))
+            size_mb = q_path.stat().st_size / (1024 * 1024)
+            print(f"  Q4: {size_mb:.1f} MB -> {q_path.name}")
+        except ImportError:
+            print("  WARNING: onnxruntime quantization not available, trying alternative...")
+            _quantize_dynamic(model_path, quantize_type)
+
+    elif quantize_type == "q8":
+        _quantize_dynamic(model_path, quantize_type)
+
+    elif quantize_type == "fp16":
+        _convert_fp16(model_path)
+
+
+def _quantize_dynamic(model_path: Path, qtype: str):
+    """Dynamic quantization fallback."""
+    from onnxruntime.quantization import quantize_dynamic, QuantType
+
+    weight_type = QuantType.QUInt8 if qtype == "q8" else QuantType.QInt8
+    q_path = model_path.with_name(
+        model_path.stem + f"_{qtype}" + model_path.suffix,
+    )
+    quantize_dynamic(
+        model_input=str(model_path),
+        model_output=str(q_path),
+        weight_type=weight_type,
+    )
+    size_mb = q_path.stat().st_size / (1024 * 1024)
+    print(f"  {qtype.upper()}: {size_mb:.1f} MB -> {q_path.name}")
+
+
+def _convert_fp16(model_path: Path):
+    """Convert ONNX model to FP16."""
+    import onnx
+    from onnx import numpy_helper
+
+    print("  Converting to FP16...")
+    model = onnx.load(str(model_path))
+    for initializer in model.graph.initializer:
+        if initializer.data_type == onnx.TensorProto.FLOAT:
+            np_data = numpy_helper.to_array(initializer)
+            initializer.CopyFrom(
+                numpy_helper.from_array(np_data.astype("float16"), initializer.name),
+            )
+    fp16_path = model_path.with_name(
+        model_path.stem + "_fp16" + model_path.suffix,
+    )
+    onnx.save(model, str(fp16_path))
+    size_mb = fp16_path.stat().st_size / (1024 * 1024)
+    print(f"  FP16: {size_mb:.1f} MB -> {fp16_path.name}")
+
+
+def write_transformers_js_config(onnx_dir: str):
+    """Write Transformers.js compatibility config."""
+    config_path = Path(onnx_dir) / "transformers_js_config.json"
+    config = {
+        "model_type": "text-generation",
+        "quantized": True,
+    }
+    config_path.write_text(json.dumps(config, indent=2) + "\n")
+    print(f"  Wrote {config_path.name}")
+
+
+def upload_to_hub(
+    onnx_dir: str,
+    output_repo: str,
+    base_model: str,
+    sft_model: str,
+    grpo_model: str,
+):
+    """Upload ONNX model to HuggingFace Hub."""
+    print(f"\nStep 7: Uploading to {output_repo}...")
+    api = HfApi()
+    api.create_repo(repo_id=output_repo, repo_type="model", exist_ok=True)
+
+    api.upload_folder(
+        folder_path=onnx_dir,
+        repo_id=output_repo,
+        commit_message="Upload ONNX model",
+    )
+
+    readme = f"""---
+base_model: {base_model}
+tags: [onnx, transformers.js, webgpu, query-expansion, qmd]
+library_name: transformers.js
+---
+# {output_repo.split("/")[-1]}
+
+ONNX conversion of the QMD Query Expansion model for use with
+[Transformers.js](https://huggingface.co/docs/transformers.js) and WebGPU.
+
+## Details
+- **Base:** {base_model}
+- **SFT:** {sft_model}
+- **GRPO:** {grpo_model}
+- **Task:** Query expansion (lex/vec/hyde format)
+- **Format:** ONNX with Q4 quantization
+
+## Usage with Transformers.js
+
+```javascript
+import {{ AutoTokenizer, AutoModelForCausalLM }} from "@huggingface/transformers";
+
+const tokenizer = await AutoTokenizer.from_pretrained("{output_repo}");
+const model = await AutoModelForCausalLM.from_pretrained("{output_repo}", {{
+  dtype: "q4",
+  device: "webgpu",
+}});
+```
+
+## Prompt Format
+```
+<|im_start|>user
+/no_think Expand this search query: your query here<|im_end|>
+<|im_start|>assistant
+```
+"""
+    api.upload_file(
+        path_or_fileobj=readme.encode(),
+        path_in_repo="README.md",
+        repo_id=output_repo,
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert QMD model to ONNX")
+    parser.add_argument(
+        "--size", choices=PRESETS.keys(), help="Use preset config for model size",
+    )
+    parser.add_argument("--base", help="Base model (overrides preset)")
+    parser.add_argument("--sft", help="SFT adapter (overrides preset)")
+    parser.add_argument("--grpo", help="GRPO adapter (overrides preset)")
+    parser.add_argument("--output", help="Output HF repo (overrides preset)")
+    parser.add_argument(
+        "--quantize",
+        choices=["q4", "q8", "fp16", "none"],
+        default="q4",
+        help="Quantization type (default: q4)",
+    )
+    parser.add_argument(
+        "--no-upload", action="store_true", help="Don't upload to HF Hub",
+    )
+    args = parser.parse_args()
+
+    # Resolve config
+    if args.size:
+        preset = PRESETS[args.size]
+        base_model = args.base or preset["base"]
+        sft_model = args.sft or preset["sft"]
+        grpo_model = args.grpo or preset["grpo"]
+        output_repo = args.output or preset["output"]
+    elif args.base and args.sft and args.grpo and args.output:
+        base_model = args.base
+        sft_model = args.sft
+        grpo_model = args.grpo
+        output_repo = args.output
+    else:
+        parser.error(
+            "Either --size or all of --base/--sft/--grpo/--output are required",
+        )
+
+    model_name = output_repo.split("/")[-1]
+    print(f"QMD ONNX Conversion: {model_name}")
+    print("=" * 60)
+
+    # Login
+    hf_token = os.environ.get("HF_TOKEN")
+    if hf_token:
+        print("Logging in to HuggingFace...")
+        login(token=hf_token)
+
+    # Merge adapters
+    model, tokenizer = merge_adapters(base_model, sft_model, grpo_model)
+
+    # Export to ONNX
+    onnx_dir = f"/tmp/onnx_output/{model_name}"
+    os.makedirs(onnx_dir, exist_ok=True)
+    export_onnx(model, tokenizer, onnx_dir)
+
+    # Quantize
+    quantize_onnx(onnx_dir, args.quantize)
+
+    # Write Transformers.js config
+    write_transformers_js_config(onnx_dir)
+
+    # Upload
+    if not args.no_upload:
+        upload_to_hub(onnx_dir, output_repo, base_model, sft_model, grpo_model)
+
+    print(f"\nDone! ONNX files at: {onnx_dir}")
+    if not args.no_upload:
+        print(f"Repository: https://huggingface.co/{output_repo}")
+
+
+if __name__ == "__main__":
+    main()