#!/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[onnxruntime]",
# "onnx>=1.15.0",
# "onnxruntime>=1.17.0",
# "onnxconverter-common>=1.14.0",
# ]
# ///
"""
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 subprocess
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, 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}...")
# no_post_process=True avoids the 2GB protobuf serialization limit
# that occurs during tied-weight deduplication on large FP32 models.
# The exported model still works correctly — the tied weights just
# aren't deduplicated in the graph, which is fine since we quantize next.
main_export(
model_name_or_path=merged_dir,
output=output_dir,
task="text-generation-with-past",
device="cpu",
fp16=False,
no_post_process=True,
)
# Clean up temp merged dir
shutil.rmtree(merged_dir, ignore_errors=True)
def _find_onnx_model(onnx_dir: str) -> Path:
"""Find the main ONNX model file in the output directory."""
model_path = Path(onnx_dir) / "model.onnx"
if model_path.exists():
return model_path
candidates = list(Path(onnx_dir).glob("*.onnx"))
if not candidates:
raise FileNotFoundError(f"No .onnx files found in {onnx_dir}")
return candidates[0]
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 = _find_onnx_model(onnx_dir)
print(f"\nStep 6: Quantizing {model_path.name} ({quantize_type})...")
if quantize_type == "q4":
_quantize_q4(model_path)
elif quantize_type == "q8":
_quantize_q8(model_path)
elif quantize_type == "fp16":
_convert_fp16(model_path)
def _quantize_q4(model_path: Path):
"""4-bit MatMulNBits quantization via onnxruntime. Needs ~16GB RAM for 1.7B models."""
from onnxruntime.quantization import matmul_nbits_quantizer
q_path = model_path.with_name(model_path.stem + "_q4" + model_path.suffix)
quant = matmul_nbits_quantizer.MatMulNBitsQuantizer(
model=str(model_path),
block_size=32,
is_symmetric=True,
bits=4,
)
quant.process()
quant.model.save(str(q_path))
# Remove original FP32 files, keep only quantized
if q_path.exists():
_report_size(q_path)
model_path.unlink(missing_ok=True)
data_path = model_path.with_name(model_path.name + "_data")
data_path.unlink(missing_ok=True)
# Rename quantized to model.onnx for Transformers.js compatibility
q_path.rename(model_path)
print(f" Renamed {q_path.name} -> {model_path.name}")
def _quantize_q8(model_path: Path):
"""8-bit dynamic quantization via onnxruntime."""
from onnxruntime.quantization import quantize_dynamic, QuantType
q_path = model_path.with_name(model_path.stem + "_q8" + model_path.suffix)
quantize_dynamic(
model_input=str(model_path),
model_output=str(q_path),
weight_type=QuantType.QUInt8,
)
if q_path.exists():
_report_size(q_path)
model_path.unlink(missing_ok=True)
data_path = model_path.with_name(model_path.name + "_data")
data_path.unlink(missing_ok=True)
q_path.rename(model_path)
print(f" Renamed {q_path.name} -> {model_path.name}")
def _convert_fp16(model_path: Path):
"""Convert ONNX model weights to FP16."""
from onnxconverter_common import float16
import onnx
print(" Converting to FP16...")
model = onnx.load(str(model_path), load_external_data=True)
model_fp16 = float16.convert_float_to_float16(model, keep_io_types=True)
fp16_path = model_path.with_name(model_path.stem + "_fp16" + model_path.suffix)
onnx.save(model_fp16, str(fp16_path))
if fp16_path.exists():
_report_size(fp16_path)
model_path.unlink(missing_ok=True)
data_path = model_path.with_name(model_path.name + "_data")
data_path.unlink(missing_ok=True)
fp16_path.rename(model_path)
print(f" Renamed {fp16_path.name} -> {model_path.name}")
def _report_size(path: Path):
"""Print file size in MB."""
size_mb = path.stat().st_size / (1024 * 1024)
print(f" {path.name}: {size_mb:.1f} MB")
def validate_onnx(onnx_dir: str, base_model: str):
"""Run a sample inference through the ONNX model to verify it works."""
import onnxruntime as ort
import numpy as np
model_path = _find_onnx_model(onnx_dir)
print(f"\nValidation: loading {model_path.name}...")
tokenizer = AutoTokenizer.from_pretrained(onnx_dir, trust_remote_code=True)
session = ort.InferenceSession(
str(model_path),
providers=["CPUExecutionProvider"],
)
# Tokenize a test prompt
test_query = "/no_think Expand this search query: distributed consensus"
chat_prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": test_query}],
add_generation_prompt=True,
tokenize=False,
)
inputs = tokenizer(chat_prompt, return_tensors="np")
input_ids = inputs["input_ids"].astype(np.int64)
attention_mask = inputs["attention_mask"].astype(np.int64)
# Build feed dict with all required inputs
seq_len = input_ids.shape[1]
feed = {"input_ids": input_ids, "attention_mask": attention_mask}
# Add position_ids if needed
all_inputs = {inp.name: inp for inp in session.get_inputs()}
if "position_ids" in all_inputs:
feed["position_ids"] = np.arange(seq_len, dtype=np.int64).reshape(1, -1)
# Initialize past_key_values to zeros if the model expects them
for name, inp in sorted(all_inputs.items()):
if name.startswith("past_key_values"):
shape = []
for dim in inp.shape:
shape.append(dim if isinstance(dim, int) else 0)
# batch dim = 1
if shape and shape[0] == 0:
shape[0] = 1
feed[name] = np.zeros(shape, dtype=np.float32)
# Run inference
output_names = [o.name for o in session.get_outputs()]
results = session.run(output_names, feed)
# Check logits shape
logits = results[0]
print(f" Input tokens: {input_ids.shape[1]}")
print(f" Output logits shape: {logits.shape}")
print(f" Logits range: [{logits.min():.2f}, {logits.max():.2f}]")
# Greedy decode next token
next_token_id = int(np.argmax(logits[0, -1, :]))
next_token = tokenizer.decode([next_token_id])
print(f" Next token: {repr(next_token)} (id={next_token_id})")
# Check KV cache outputs exist
kv_outputs = [n for n in output_names if n.startswith("present")]
if kv_outputs:
print(f" KV cache outputs: {len(kv_outputs)} tensors (generation-ready)")
else:
print(" WARNING: No KV cache outputs — model may not support efficient generation")
# Sanity checks
assert logits.shape[0] == 1, "Batch size mismatch"
assert logits.shape[1] == input_ids.shape[1], "Sequence length mismatch"
assert logits.max() > logits.min(), "Logits are constant (broken model)"
assert not np.isnan(logits).any(), "Logits contain NaN"
assert not np.isinf(logits).any(), "Logits contain Inf"
print(" Validation PASSED")
def write_transformers_js_config(onnx_dir: str, quantize_type: str = "q4"):
"""Write Transformers.js compatibility config."""
config_path = Path(onnx_dir) / "transformers_js_config.json"
config = {
"model_type": "text-generation",
"quantized": quantize_type != "none",
}
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,
quantize_type: str = "q4",
):
"""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",
)
# Map quantize_type to Transformers.js dtype values
dtype_map = {"q4": "q4", "q8": "q8", "fp16": "fp16", "none": "fp32"}
tj_dtype = dtype_map.get(quantize_type, "fp32")
format_desc = "FP32 (no quantization)" if quantize_type == "none" else f"{quantize_type.upper()} quantization"
repo_name = output_repo.split("/")[-1]
readme = f"""---
base_model: {base_model}
tags: [onnx, transformers.js, webgpu, query-expansion, qmd]
library_name: transformers.js
---
# {repo_name}
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 {format_desc}
## 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: "{tj_dtype}",
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",
)
parser.add_argument(
"--validate", action="store_true",
help="Run inference validation on exported model",
)
parser.add_argument(
"--validate-only", metavar="DIR",
help="Skip export, only validate an existing ONNX dir",
)
args = parser.parse_args()
# Validate-only mode: skip export, just run validation
if args.validate_only:
validate_onnx(args.validate_only, "")
return
# 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, args.quantize)
# Validate
if args.validate:
validate_onnx(onnx_dir, base_model)
# Upload
if not args.no_upload:
upload_to_hub(onnx_dir, output_repo, base_model, sft_model, grpo_model, args.quantize)
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()