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README.md

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 license: apache-2.0
 library_name: transformers
 ---
+
+# SDAR
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/JetAstra/SDAR/main/assets/SDAR_doc_head.png">
+
+
+<div>&nbsp;</div>
+
+[💻Github Repo](https://github.com/JetAstra/SDAR) • [🤗Model Collections](https://huggingface.co/collections/JetLM/sdar-689b1b6d392a4eeb2664f8ff)
+
+</div>
+
 # Introduction
 
-**SDAR**(**S**ynergy of **D**iffusion and **A**uto**R**egression)-model is a new large language model that integrates autoregressive (AR) and discrete diffusion modeling strategies. It combines the efficient training paradigm of AR models with the highly parallel inference capability of diffusion models, while delivering performance fully on par with SOTA opensource AR models. At the same time, SDAR sets a new benchmark as the most powerful diffusion language model to date.
-
-> The SDAR series models undergo continued training on Qwen3 models.
-
-# Performance of SDAR-1.7B-Chat on various benchmarks
-
-evaluation settings:
-- MMLU: 5-shot
-- Math500: 0-shot
-- GSM8K: 0-shot
-- HumanEval: 0-shot
-- Sanitized_MBPP: 0-shot
-- IFEval: 0-shot
-- MathBench: 0-shot
-
-
-| Model             | MMLU | Math500 | GSM8K | HumanEval | Sanitized_MBPP | IFEval | MathBench |
-|-------------------|------|---------|-------|-----------|----------------|--------|-----------|
-| SDAR-1.7B-Chat    | 62.9 | 63.2    | 80.06 | 61.59     | 61.09          | 43.44  | 63.55     |
-| SDAR-4B-Chat |  |     |  |      |           |   |      |
-| SDAR-8B-Chat |  |     |  |      |           |   |      |
-| SDAR-30B-A3B-Chat |  |     |  |      |           |   |      |
-
-
-
-**Note**: The 4B, 8B, and 30B models are coming soon. Performance results for these models will be released in the near future.
-
-## Inference
-
-### Using the tailored inference engine [JetEngine](https://github.com/Labman42/JetEngine)
-
-JetEngine enables more efficient inference compared to the built-in implementation.
-
-```bash
-git clone https://github.com/Labman42/JetEngine.git
-cd JetEngine
-pip install .
-```
-
-The following example shows how to quickly load a model with JetEngine and run a prompt end-to-end.
-
-```python
-import os
-from jetengine import LLM, SamplingParams
-from transformers import AutoTokenizer
-
-model_path = os.path.expanduser("/path/to/your/sdar-model")
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-# Initialize the LLM
-llm = LLM(
-    model_path,
-    enforce_eager=True,
-    tensor_parallel_size=1,
-    mask_token_id=151669,   # Optional: only needed for masked/diffusion models
-    block_length=4
-)
-
-# Set sampling/generation parameters
-sampling_params = SamplingParams(
-    temperature=1.0,
-    topk=0,
-    topp=1.0,
-    max_tokens=256,
-    remasking_strategy="low_confidence_dynamic",
-    block_length=4,
-    denoising_steps=4,
-    dynamic_threshold=0.9
-)
-
-# Prepare a simple chat-style prompt
-prompt = tokenizer.apply_chat_template(
-    [{"role": "user", "content": "Explain what reinforcement learning is in simple terms."}],
-    tokenize=False,
-    add_generation_prompt=True
-)
-
-# Generate text
-outputs = llm.generate_streaming([prompt], sampling_params)
-```
-
-## Hightlights
-- **Performance**: SDAR-1.7B-Chat achieves state-of-the-art.
-- **Efficiency**: SDAR provides over 2× faster inference speed compared to the same-size AR models, while maintaining comparable performance.
+**SDAR** (**S**ynergy of **D**iffusion and **A**uto**R**egression) model is a new large language model that integrates autoregressive (AR) and discrete diffusion modeling strategies. It combines the efficient training paradigm of AR models with the highly parallel inference capability of diffusion models, while delivering performance fully on par with SOTA open-source AR models. At the same time, SDAR sets a new benchmark as the most powerful diffusion language model to date. We highlight three major conclusions from our study:
+
+> [!IMPORTANT]
+> Take-home message
+>
+> - **Balanced Efficiency:** SDAR unifies the **efficient training** of AR models with the **parallel inference** of diffusion, achieving both fast training and inference.  
+> - **Fair Comparisons:** In rigorously controlled experiments, SDAR achieves **on-par general task performance** with strong AR baselines, ensuring credibility and reproducibility.  
+> - **Superior Learning Efficiency:** On complex scientific reasoning tasks (e.g., GPQA, ChemBench, Physics), SDAR shows **clear gains over AR models** of the same scale, approaching or even exceeding leading closed-source systems.
+
+# Performance
+
+### SDAR v.s. Qwen
+
+For **SDAR** models, inference hyperparameters are set to: `block_length = 4`, `denoising_steps = 4`, greedy decoding.
+
+For **Qwen3-1.7B-AR-SFT** and **Qwen3-30B-AR-SFT**, we use *greedy decoding*, and the base models **Qwen3-1.7B-Base** and **Qwen3-30B-Base** are derived from the [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388).
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/JetAstra/SDAR/main/assets/table1.png" style="max-width:100%; height:auto;">
+<p align="center">
+
+### SDAR-Sci v.s. AR Baseline
+
+This table presents a **controlled comparison** between AR and SDAR under the same backbone and dataset settings.
+The results are averaged over 8 runs for GPQA, and over 32 runs each for AIME 2024, AIME 2025, and LiveMathBench.
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/JetAstra/SDAR/main/assets/table2.png" style="max-width:100%; height:auto;">
+<p align="center">
+
+#### SDAR-Sci v.s. Other Models
+
+This table positions **SDAR-30B-A3B-Sci(sample)** against leading open-source and closed-source LLMs.
+Scores for external models are sourced from the [InternLM/Intern-S1](https://github.com/InternLM/Intern-S1) repository.
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/JetAstra/SDAR/main/assets/table3.png" style="max-width:100%; height:auto;">
+<p align="center">