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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3.5-2B
+tags:
+  - gui-grounding
+  - sdft
+  - self-distillation
+  - qwen3.5
+---
+
+# WebOS SDFT 2B
+
+Full-parameter SDFT (Self-Distillation Fine-Tuning, paper-faithful Shenfeld et al. 2601.19897 Algorithm 1) of Qwen3.5-2B on the WebOS GUI-grounding dataset.
+
+## Layout
+
+| Subfolder | Description | Click-acc on full test set |
+|---|---|---|
+| `checkpoint-200/` | Step 200 / 616 (32% of training) | 18.41% |
+| `checkpoint-400/` | Step 400 / 616 (65%) | 18.75% |
+| `checkpoint-600/` | Step 600 / 616 (97%) | **19.95%** |
+| `final/` | Step 616 / 616 (100%) | ~20% |
+
+For comparison: base Qwen3.5-2B on the same eval is 18.84%, and Qwen3.5-2B + privileged GT-demo teacher prompt is 26.46%.
+
+## Training config (16b_train_sdft_full_ft.py)
+- bf16, full-param FT (vision frozen)
+- bs=1, ga=32 (effective batch 32)
+- lr=5e-6, warmup=10 steps, cosine
+- AdamW, wd=0, max_grad_norm=1.0
+- ema-α=0.01, kl-temperature=2.0, reverse KL
+- 2 epochs × 9,864 train samples = 616 optimizer steps
+- on-policy max_new_tokens=96, temperature=1.0
+
+## Loading
+
+```python
+from transformers import AutoModelForImageTextToText, AutoProcessor
+model = AutoModelForImageTextToText.from_pretrained(
+    "Chengheng/webos-sdft-2b", subfolder="checkpoint-600",
+    torch_dtype="bfloat16", device_map="cuda:0",
+)
+processor = AutoProcessor.from_pretrained(
+    "Chengheng/webos-sdft-2b", subfolder="checkpoint-600",
+)
+```
+
+## Code
+
+Training and eval scripts: https://github.com/ChenghengLi/WebOS