ONNX export of dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1 — BD3LM, a block-diffusion language model — packaged to run client-side in a web browser on WebGPU via onnxruntime-web, driven by the kohra denoising loop (kohra.js).

Block diffusion sits between autoregressive and fully-masked (MDLM) generation: the sequence is split into blocks of 32 tokens with a block-causal attention pattern — a block attends bidirectionally within itself and causally to all earlier blocks — and is denoised block-by-block, left to right. Same Tiny-A2D Qwen3-0.6B architecture as the MDLM sibling, but a separately-trained checkpoint with stronger reasoning: GSM8K 46.3 vs 29.3, HumanEval 46.3 vs 30.5.

Use it

Same kohra.js loader as the MDLM repo — block diffusion is one extra generate flag, blockCausal: true (the graph takes a 2nd input, a block-causal attention mask, built for you):

import { pipeline } from './kohra.js';

const generate = await pipeline('text-diffusion', {
  model: 'https://huggingface.co/naklitechie/Qwen3-0.6B-diffusion-bd3lm-ONNX/resolve/main/onnx/model_fp16_fused.onnx',
  tokenizer: 'naklitechie/Qwen3-0.6B-diffusion-bd3lm-ONNX',
});

const { text } = await generate('Lily runs 12 km/h for 4 hours. How far in 8 hours?', { blockCausal: true });
console.log(text);   // -> "...48 * 2 = 96 km. Thus, Lily runs \boxed{96} km in 8 hours."

Requires a WebGPU browser (Chrome/Edge 121+) over https or localhost. ~1.5 GB download (fp16, cached after first load); ~2.8 tok/s on an M-series Mac at 128 denoise steps.

Files

onnx/model_fp16_fused.onnx (+ .onnx.data) — 2-input graph: input_ids and a [1,1,T,T] additive block-causal attention_mask (0 = attend, -1e9 = block, on the pos // 32 grid). RMSNorm is fused to SimplifiedLayerNormalization (the WebGPU fp16 fix); attention stays decomposed so the 4D mask is honored.
onnx/model_q4f16_rtn_sym.onnx (+ .onnx.data) — 4-bit (RTN, symmetric) q4f16, ~680 MB. Coherent on WebGPU; fp16 stays the default at 0.6B (q4's dequant overhead and a small quality dip aren't worth it until the model is too big for fp16).
Tokenizer files (Qwen3 ChatML).

Notes

No KV cache — one full forward per denoise step over the whole canvas. Block-causality is what makes the cache-free forward correct: a still-masked future block can't influence an earlier one, so the current block's logits are right regardless of what's downstream.
The loop relies on the model's default arange positions (the graph has no position_ids input), so the prompt sits at [0,P) with no padding and the first generated tokens complete the prompt's last partial block — exactly the configuration the ONNX-vs-torch parity verified.

Attribution & license

Derivative of dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1 (built on Qwen/Qwen3-0.6B, Apache-2.0; block-diffusion adaptation by dLLM). See the source repos.