Subgraph	Precision	Notes
Vision encoder (`vision.onnx`)	FP32	Unquantized
Token embedding (`embedding.onnx`)	FP32	Unquantized; bit-exact vs PyTorch
Text decoder (`text.onnx`)	INT4	Olive ModelBuilder `k_quant`, group/block size 128, `accuracy_level=4`. All 497 `MatMulNBits` weights are 4-bit.

This is a conversion of the base model, not a fine-tune. License and intended use follow the base model card (Apache 2.0).

Files

embedding.onnx + embedding.onnx.data   # FP32 token embedding + image-feature scatter (~5.1 GB)
vision.onnx + vision.onnx.data         # FP32 vision encoder (~3.7 GB)
text.onnx + text.onnx.data             # INT4 hybrid decoder (~12.9 GB)
genai_config.json
processor_config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja
config.json
generation_config.json

Usage

Requires a recent ONNX Runtime GenAI build with Qwen3.5 / Qwen3.8 (model.type: qwen3_5) support.

bash

pip install onnxruntime-genai huggingface_hub
huggingface-cli download amd/Qwen3.8-27B-fp32-ve-fp32-int4-k_quant-gs128-text-cpu-onnx --local-dir ./qwen38-27b-cpu-onnx

python

import json
import onnxruntime_genai as og

model = og.Model("./qwen38-27b-cpu-onnx")
tokenizer = og.Tokenizer(model)
processor = model.create_multimodal_processor()
stream = processor.create_stream()

messages = [{"role": "user", "content": "What is the capital of France?"}]
prompt = tokenizer.apply_chat_template(json.dumps(messages), add_generation_prompt=True)
inputs = processor(prompt)

params = og.GeneratorParams(model)
params.set_search_options(max_length=256)
generator = og.Generator(model, params)
generator.set_inputs(inputs)

while not generator.is_done():
    generator.generate_next_token()
    print(stream.decode(generator.get_next_tokens()[0]), end="", flush=True)

Image input: pass images=og.Images.open("photo.jpg") into the processor with a chat message that includes {"type": "image"}.

Notes

CPU-only: genai_config.json uses the default CPU execution provider (empty provider_options).
Search defaults in genai_config.json are greedy (do_sample: false, top_k: 1).
Chat stop tokens are <|im_end|> (248046) and <|endoftext|> (248044).
Tokenizer Split rules that used Unicode property escapes were replaced with ByteLevel (use_regex=true) so ONNX Runtime GenAI's C++ std::regex can load them.
Chat template uses is not defined instead of Jinja is undefined for ORT GenAI minja.

Export

Produced with Olive ModelBuilder (int4_algo_config: k_quant, int4_block_size: 128) from Qwen/Qwen3.8-27B. Vision and embedding were exported unquantized FP32.