DesignCoder

Checkpoint collection for DesignCoder, a family of full-parameter SFT models for UI design research and end-to-end HTML/CSS/JavaScript implementation.

Each subfolder in this repository is a self-contained, directly loadable checkpoint.

Naming convention

designcoder_{basemodel}_{size}_{optimizer}_bs{global_batch}[_{extra_axes}]_step{global_step}

• basemodel / size: base model family and parameter scale
• optimizer: muon or adamw
• bs: global batch size (per_device × grad_accum × world_size)
• extra_axes: any hyper-parameter that deviates from the default recipe, e.g. wd0.05 (weight decay, default 0.0), ep20 (epochs, default 2), or data41287 (dataset revision)
• step: trainer global_step of the exported weights

Dataset revisions

Checkpoints in this repository come from two different dataset revisions. Scores and loss values are only comparable within the same revision.

Tag	Samples	Used by
(untagged) data37865	37,865	*_step1900, *_step3800
data41287	41,287	*_data41287_step200, *_data41287_step400

Checkpoints

Subfolder	Base model	Optimizer	LR	Global batch	Dataset	Step	bench-200	Notes
designcoder_qwen3.5_4b_muon_bs32_step1900	Qwen3.5-4B	Muon	1e-5	32	37,865	1900	–	smallest of the first release
designcoder_qwen3.5_9b_muon_bs16_step3800	Qwen3.5-9B	Muon	1e-5	16	37,865	3800	–	optimizer ablation (Muon arm)
designcoder_qwen3.5_9b_adamw_bs16_step3800	Qwen3.5-9B	AdamW	2e-5	16	37,865	3800	–	optimizer ablation (AdamW arm)
designcoder_qwen3.6_27b_adamw_bs32_step1900	Qwen3.6-27B	AdamW	1e-5	32	37,865	1900	–	largest of the first release
designcoder_qwen3.5_4b_adamw_bs256_data41287_step200	Qwen3.5-4B	AdamW	2e-5	256	41,287	200	84.22	best 4B / AdamW
designcoder_qwen3.5_4b_muon_bs256_data41287_step200	Qwen3.5-4B	Muon	2e-5	256	41,287	200	83.36	best 4B / Muon; degrades less late in training
designcoder_qwen3.5_9b_adamw_bs256_data41287_step200	Qwen3.5-9B	AdamW	2e-5	256	41,287	200	84.40	only checkpoint scored on all 200 cases
designcoder_qwen3.8_27b_adamw_bs128_data41287_step400	Qwen3.8-27B	AdamW	1e-5	128	41,287	400	91.19	strongest checkpoint in the collection

Benchmark

bench-200 is the frozen 200-case DesignCoder benchmark (100 Track A landing, 40 Track A dashboard, 30 Track B landing, 30 Track B dashboard). Every rubric item is a binary screenshot check scored by a vision judge over full-page renders; the reported number is the unweighted mean of Prompt Fit and the six rubric dimensions (Alignment, Layout, Typography, Components, Assets, Aesthetics).

The 9B score is a full 200-case run. The 4B and 27B scores come from an 8-case subset reweighted to the benchmark's real landing/dashboard split, so they are indicative rather than final — and the subset was sampled around the 9B mid-range, which understates 9B relative to 4B. Use the 9B full-run number (84.40) when comparing against 27B (91.19).

Checkpoint selection

The data41287 checkpoints were selected by running the benchmark, not by taking the lowest training loss. In all four runs the best checkpoint sits at roughly 75% of training, and loss kept improving while benchmark scores fell:

Run	Step	Train loss	bench-200
4B AdamW	200	0.2696	84.22
4B AdamW	266	0.2682	68.35
4B Muon	200	0.3339	83.36
4B Muon	266	0.3349	81.27
9B AdamW	200	0.2518	84.40
9B AdamW	266	0.2504	lowest of the three
27B AdamW	400	0.2067	91.19
27B AdamW	530	0.2059	86.37

The 4B AdamW pair is the clearest example: loss improved from 0.2696 to 0.2682 while the score collapsed from 84.22 to 68.35. Do not pick checkpoints from this family by loss.

Shared training setup

• Objective: full-parameter supervised fine-tuning (no LoRA / adapters)
• Dataset: designcoder_sft_v2_train in ShareGPT format (see revision table above)
• Chat template: qwen3_5 with thinking enabled
• Context length: 32,768
• Sequence packing: enabled, with neat packing (no cross-sample attention)
• LR schedule: cosine, warmup ratio 0.1

Usage

from transformers import AutoModelForCausalLM, AutoProcessor

repo = "xingxm/DesignCoder"
subfolder = "designcoder_qwen3.8_27b_adamw_bs128_data41287_step400"

model = AutoModelForCausalLM.from_pretrained(repo, subfolder=subfolder, dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(repo, subfolder=subfolder)

To download a single checkpoint only:

hf download xingxm/DesignCoder --include "designcoder_qwen3.8_27b_adamw_bs128_data41287_step400/*" --local-dir ./DesignCoder

Inference contract

These models are trained as tool-using agents, not single-turn generators. A case runs design_search → (websearch, landing only) → a final answer containing exactly three code blocks in the order html, css, js. Reproduce the system prompts and tool observation format from examples/designcoder/runtime/infer_designcoder.py; prompting with a bare instruction and no tool turns does not match the training distribution and will score far below the numbers above.

Provenance

Each subfolder additionally ships trainer_state.json / trainer_log.jsonl (and training_loss.png where available) so that the loss curve and exact step schedule of the run can be recovered from the checkpoint itself.