File size: 2,072 Bytes
1e64107 c5d9d5f 1e64107 c5d9d5f 1e64107 c5d9d5f 1e64107 a33cbf1 1e64107 c5d9d5f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ---
license: apple-amlr
base_model:
- Qwen/Qwen3-4B-Thinking-2507
tags:
- self-distillation
- code-generation
- ssd
library_name: transformers
---
# SSD-Qwen3-4B-Thinking
This model was produced using **Simple Self-Distillation (SSD)**, a method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning.
- **Base model:** [Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
- **Variant:** thinking
- **Self-distillation sampling:** temperature=1.1, top_p=0.95, top_k=20
- **Evaluation sampling:** temperature=0.7, top_p=0.95, top_k=20
## Method
SSD samples solutions from the base model using non-unit temperature and top-k/top-p truncation, then fine-tunes on those samples via standard supervised learning. Despite its simplicity, SSD yields large gains on competitive programming benchmarks, with improvements concentrating on harder problems. The mechanism traces to resolving a *precision–exploration conflict*: SSD reshapes token distributions in a context-dependent way so that a single global decoding configuration becomes far more effective at evaluation time.
## Results
LiveCodeBench (%)
| Model | LCBv6 pass@1 | LCBv6 pass@5 | LCBv5 pass@1 | LCBv5 pass@5 |
|---|---|---|---|---|
| Qwen3-4B-Thinking-2507 (base) | 54.5 | 67.5 | 59.6 | 70.3 |
| **+ SSD (this model)** | **57.8** (+3.3) | **71.4** (+3.9) | **63.1** (+3.5) | **74.7** (+4.4) |
## Paper
**Embarrassingly Simple Self-Distillation Improves Code Generation**
Ruixiang Zhang, Richard He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("apple/SSD-Qwen3-4B-Thinking")
tokenizer = AutoTokenizer.from_pretrained("apple/SSD-Qwen3-4B-Thinking")
```
## License
This model is released under the [Apple Machine Learning Research Model License](https://huggingface.co/apple/SSD-Qwen3-4B-Thinking/blob/main/LICENSE).
|