Update Readme

README.md

@@ -9,12 +9,10 @@ tags:
 library_name: transformers
 ---
 
-# SSD-Qwen3-30B-A3B-Instruct
+# SimpleSD-30B-instruct
 
 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/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507)
-- **Variant:** instruct
 - **Self-distillation sampling:** temperature=1.6, top_p=0.8, top_k=20
 - **Evaluation sampling:** temperature=0.9, top_p=0.8, top_k=20
 
@@ -23,6 +21,15 @@ This model was produced using **Simple Self-Distillation (SSD)**, a method that
 - They are not optimized Qwen releases.
 - They don't represent a broader open-source model strategy.
 
+## Usage
+
+ ```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+ model = AutoModelForCausalLM.from_pretrained("apple/SimpleSD-30B-instruct")
+tokenizer = AutoTokenizer.from_pretrained("apple/SimpleSD-30B-instruct")
+```
+
 ## 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.
@@ -37,20 +44,21 @@ LiveCodeBench (%)
 | **+ SSD (this model)** | **55.3** (+12.9) | **71.6** (+18.1) | **54.3** (+8.5) | **70.7** (+12.0) |
 
 ## 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-Qwen3-30B-A3B-Instruct")
-tokenizer = AutoTokenizer.from_pretrained("apple/SSD-Qwen3-30B-A3B-Instruct")
+ [**Embarrassingly Simple Self-Distillation Improves Code Generation**](https://arxiv.org/abs/2604.01193)
+ 
+ ```bibtex
+@misc{zhang2026embarrassinglysimpleselfdistillationimproves,
+      title={Embarrassingly Simple Self-Distillation Improves Code Generation},
+      author={Ruixiang Zhang and Richard He Bai and Huangjie Zheng and Navdeep Jaitly and Ronan Collobert and Yizhe Zhang},
+      year={2026},
+      eprint={2604.01193},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2604.01193},
+}
 ```
 
+
 ## License
 
-This model is released under the [Apple Machine Learning Research Model License](https://huggingface.co/apple/SSD-Qwen3-30B-A3B-Instruct/blob/main/LICENSE).
+This model is released under the [Apple Machine Learning Research Model License](https://huggingface.co/apple/SimpleSD-30B-instruct/blob/main/LICENSE).