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) or ep20 (epochs, default 2)
• step: trainer global_step of the exported weights

Checkpoints

Subfolder	Base model	Optimizer	LR	Global batch	Epochs	Weight decay	Step	Notes
designcoder_qwen3.5_4b_muon_bs32_step1900	Qwen3.5-4B	Muon	1e-5	32	2	0.0	1900	smallest release
designcoder_qwen3.5_9b_muon_bs16_step3800	Qwen3.5-9B	Muon	1e-5	16	2	0.0	3800	optimizer ablation (Muon arm)
designcoder_qwen3.5_9b_adamw_bs16_step3800	Qwen3.5-9B	AdamW	2e-5	16	2	0.0	3800	optimizer ablation (AdamW arm)
designcoder_qwen3.6_27b_adamw_bs32_step1900	Qwen3.6-27B	AdamW	1e-5	32	2	0.0	1900	largest release

Shared training setup

• Objective: full-parameter supervised fine-tuning (no LoRA / adapters)
• Dataset: designcoder_sft_v2_train, 41,287 ShareGPT-format records
• 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_qwen3.5_4b_muon_bs32_step1900"

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_qwen3.5_4b_muon_bs32_step1900/*" --local-dir ./DesignCoder

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.