Update README.md

README.md

@@ -1,181 +1,181 @@
----
-license: other
-license_link: LICENSE
-library_name: transformers
-pipeline_tag: text-generation
-datasets:
-  - amd/SAND-Post-Training-Dataset
-
-language:
-  - en
-base_model:
-  - Qwen/Qwen2.5-32B-Instruct
----
-
-# SAND-Reasoning: Best-in-class Large Reasoning Model Built with Synthetic Data only using AMD GPUs
-
-<div align="center">
-
-| [**📄 Technical Report**](https://arxiv.org/pdf/2507.20527) | [**💾 Synthetic Datasets**](https://huggingface.co/datasets/amd/SAND-Post-Training-Dataset) | [**💻 GitHub Repository**](https://huggingface.co/datasets/amd/SAND-Post-Training-Dataset) | [**📝 Blog Post**](https://rocm.blogs.amd.com/artificial-intelligence/sand-math/README.html) |
-| :---: | :---: | :---: | :---: |
-
-</div>
-
----
-
-## Model Summary
-
-We introduce **SAND-Math-Qwen2.5-32B** and **SAND-MathScience-DeepSeek-Qwen32B**, reasoning models built entirely using a synthetic data pipeline running on the **AMD ROCm™ stack** and **AMD Instinct™ MI325 GPUs**.
-
-By prioritizing data difficulty along with quantity, we demonstrate that high-difficulty synthetic data can elevate prior-generation models to match or exceed modern proprietary models. `SAND-Math-Qwen2.5-32B` is fine-tuned from **Qwen2.5-32B-Instruct** on just **14k synthetic math samples**, achieving strong reasoning capabilities with minimal data outperforming other data distillation and post training approaches. `SAND-MathScience-DeepSeek-Qwen32B` is fine-tuned from **DeepSeek-R1-Distill-Qwen-32B** on a compact dataset of **27k samples** (15k Math + 12k Science), achieving a generational leap in performance that rivals **Qwen3-32B**.
-
-We are releasing the models, datasets, and code to empower the community to build their own state-of-the-art reasoning models using AMD hardware.
-
-## 📊 Benchmark Results
-
-We conducted extensive experiments to validate that our pipeline yields superior results compared to models trained on significantly larger datasets.
-
-### 1. Bridging the Generational Gap
-Fine-tuning the Qwen2.5-based **DeepSeek-R1-Distill-Qwen-32B** on our mixed Math/Science dataset allows it to rival and even surpass the next-generation **Qwen3-32B** on key benchmarks.
-
-| Model | AIME24 | AIME25 | MATH500 | GPQA |
-| :--- | :---: | :---: | :---: | :---: |
-| DeepSeek-Distilled-Qwen32B (Base) | 72.6 | 54.9 | 94.3 | 62.1 |
-| EXAONE Deep 32B | 72.1 | 65.8 | 95.8 | 66.1 |
-| Qwen3-32B (Thinking mode) | 81.4 | 72.9 | **97.0** | 68.4 |
-| **SAND-MathScience-DeepSeek-Qwen32B (Ours)** | **83.85** | **78.33** | 93.85 | **68.72** |
-
-### 2. Efficiency: Unlocking Reasoning with Less Data
-Using only **14k synthetic math samples** and standard SFT (no RL), our approach outperforms models trained on datasets 5x to 50x larger.
-
-| Model | Data Size | AIME24 | AIME25 | MATH500 | GPQA |
-| :--- | :--- | :---: | :---: | :---: | :---: |
-| Qwen2.5-32B-Instruct (Base) | - | 16.7 | 13.3 | 83.4 | 53.5 |
-| DeepSeek-R1-Distill-Qwen-32B | 800k | 72.6 | 54.9 | 94.3 | 62.1 |
-| Light-R1-32B | 79k | 73.0 | 64.3 | 93.3 | 60.6 |
-| OpenThinker-32B | 114k | 66.0 | 53.3 | 89.4 | 57.6 |
-| **SAND-Math-Qwen2.5-32B (Ours)** | **14k** | **74.01** | **68.18** | **92.05** | **60.8** |
-
----
-
-## ⚙️ The Synthetic Data Pipeline
-
-Our results are powered by a 4-stage automated pipeline running on AMD hardware that prioritizes **difficulty and novelty** over volume. Unlike datasets that recycle easy problems, our pipeline leverages a Teacher Model (`GPT-OSS120b`) to generate, validate, and systematically "hike" the difficulty of reasoning problems.
-
-![Pipeline Overview](PipelineSimple.png)
-
-### Pipeline Stages
-
-1. **Stage 1: QA Generation & Consistency** 🛠️
-   - Generates novel problems from scratch
-   - Enforces correctness by requiring the teacher to generate multiple independent solution paths
-   - Only questions where all answers align are kept
-
-2. **Stage 2: De-duplication & Decontamination** 🧹
-   - Removes internal duplicates via embedding similarity
-   - **Crucial Step:** Scans against known test sets (AIME, MATH, GPQA) to ensure zero contamination
-
-3. **Stage 3: Difficulty Hiking** 🏔️
-   - Moderately challenging questions are rewritten by the teacher model
-   - Introduces deeper reasoning chains, added constraints, or cross-domain logic
-   - Systematically elevates complexity
-   - Configurable step primarily used when initial generation yields insufficient volume of high-difficulty samples
-
----
-
-## 🚀 Quick Start
-
-### Python Inference (Transformers)
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "amd/SAND-Math-Qwen2.5-32B"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Example prompt
-prompt = "Find the number of pairs of positive integers $(m, n)$ such that $m^2 + n < 22$ and $n^2 + m < 22$."
-messages = [
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=4096,
-    temperature=0.7, # Recommended temperature
-    do_sample=True
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-print("Response:", response)
-```
-
-### Serving (vLLM & SGLang)
-
-You can easily serve this model as an OpenAI-compatible API endpoint.
-
-**Using SGLang:**
-```bash
-python -m sglang.launch_server --model-path amd/SAND-Math-Qwen2.5-32B --max-model-len 32768
-```
-
-**Using vLLM:**
-```bash
-vllm serve amd/SAND-Math-Qwen2.5-32B --max-model-len 32768
-```
-
----
-
-## 💡 Usage Recommendations
-
-To replicate our performance benchmarks and achieve the best reasoning results, we strongly recommend the following configurations:
-
-*   **Temperature:** Set `temperature=0.7`. **DO NOT use greedy decoding**, as it can lead to performance degradation and repetitive loops.
-*   **Prompting:** For mathematical problems, include a directive to enforce structure:
-    > "Please reason step by step, and put your final answer within \boxed{}."
-*   **Context Length:** We recommend allowing an output length of **32,768 tokens**. This ensures the model has sufficient space for long Chain-of-Thought (CoT) generation.
-*   **Thinking Token:** It is recommended to enforce the model to initiate its response with the `<think>\n` token to trigger the reasoning mode effectively.
-*   **Evaluation:** When benchmarking, conduct multiple passes (Pass@K) and average the results for stability.
-
----
-
-## 📜 License
-
-This project is licensed under the **Open RAIL-MSD** license. This is an open, royalty-free license that permits commercial use, modification, and distribution of the dataset, models, and source code.
-
-The license includes standard use-based restrictions to prevent harmful applications (e.g., illegal activities, generating harmful content, high-risk applications). These restrictions are designed to promote responsible AI development while keeping the license permissive for legitimate use cases.
-
-For full license terms and conditions, please see the [LICENSE](https://github.com/AMD-AGI/sand-pipeline/blob/main/LICENSE.txt) file.
-
----
-
-## Citation
-
-If you use this model, dataset, or pipeline in your research, please cite our work:
-
-```bibtex
-@misc{manem025sandmathusingllmsgenerate,
-      title={SAND-Math: Using LLMs to Generate Novel, Difficult and Useful Mathematics Questions and Answers},
-      author={Chaitanya Manem and Pratik Prabhanjan Brahma and Prakamya Mishra and Zicheng Liu and Emad Barsoum},
-      year={2025},
-      eprint={2507.20527},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2507.20527},
-}
-```
+---
+license: other
+license_link: LICENSE
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+  - amd/SAND-Post-Training-Dataset
+
+language:
+  - en
+base_model:
+  - Qwen/Qwen2.5-32B-Instruct
+---
+
+# SAND-Reasoning: Best-in-class Large Reasoning Model Built with Synthetic Data only using AMD GPUs
+
+<div align="center">
+
+| [**📄 Technical Report**](https://arxiv.org/pdf/2507.20527) | [**💾 Synthetic Datasets**](https://huggingface.co/datasets/amd/SAND-Post-Training-Dataset) | [**💻 GitHub Repository**](https://huggingface.co/datasets/amd/SAND-Post-Training-Dataset) | [**📝 Blog Post**](https://rocm.blogs.amd.com/artificial-intelligence/sand-math/README.html) |
+| :---: | :---: | :---: | :---: |
+
+</div>
+
+---
+
+## Model Summary
+
+We introduce **SAND-Math-Qwen2.5-32B** and **SAND-MathScience-DeepSeek-Qwen32B**, reasoning models built entirely using a synthetic data pipeline running on the **AMD ROCm™ stack** and **AMD Instinct™ MI325 GPUs**.
+
+By prioritizing data difficulty along with quantity, we demonstrate that high-difficulty synthetic data can elevate prior-generation models to match or exceed modern proprietary models. `SAND-Math-Qwen2.5-32B` is fine-tuned from **Qwen2.5-32B-Instruct** on just **14k synthetic math samples**, achieving strong reasoning capabilities with minimal data outperforming other data distillation and post training approaches. `SAND-MathScience-DeepSeek-Qwen32B` is fine-tuned from **DeepSeek-R1-Distill-Qwen-32B** on a compact dataset of **27k samples** (15k Math + 12k Science), achieving a generational leap in performance that rivals **Qwen3-32B**.
+
+We are releasing the models, datasets, and code to empower the community to build their own state-of-the-art reasoning models using AMD hardware.
+
+## 📊 Benchmark Results
+
+We conducted extensive experiments to validate that our pipeline yields superior results compared to models trained on significantly larger datasets.
+
+### 1. Bridging the Generational Gap
+Fine-tuning the Qwen2.5-based **DeepSeek-R1-Distill-Qwen-32B** on our mixed Math/Science dataset allows it to rival and even surpass the next-generation **Qwen3-32B** on key benchmarks.
+
+| Model | AIME24 | AIME25 | MATH500 | GPQA |
+| :--- | :---: | :---: | :---: | :---: |
+| DeepSeek-Distilled-Qwen32B (Base) | 72.6 | 54.9 | 94.3 | 62.1 |
+| EXAONE Deep 32B | 72.1 | 65.8 | 95.8 | 66.1 |
+| Qwen3-32B (Thinking mode) | 81.4 | 72.9 | **97.0** | 68.4 |
+| **SAND-MathScience-DeepSeek-Qwen32B (Ours)** | **83.85** | **78.33** | 93.85 | **68.72** |
+
+### 2. Efficiency: Unlocking Reasoning with Less Data
+Using only **14k synthetic math samples** and standard SFT (no RL), our approach outperforms models trained on datasets 5x to 50x larger.
+
+| Model | Data Size | AIME24 | AIME25 | MATH500 | GPQA |
+| :--- | :--- | :---: | :---: | :---: | :---: |
+| Qwen2.5-32B-Instruct (Base) | - | 16.7 | 13.3 | 83.4 | 53.5 |
+| DeepSeek-R1-Distill-Qwen-32B | 800k | 72.6 | 54.9 | 94.3 | 62.1 |
+| Light-R1-32B | 79k | 73.0 | 64.3 | 93.3 | 60.6 |
+| OpenThinker-32B | 114k | 66.0 | 53.3 | 89.4 | 57.6 |
+| **SAND-Math-Qwen2.5-32B (Ours)** | **14k** | **74.01** | **68.18** | **92.05** | **60.8** |
+
+---
+
+## ⚙️ The Synthetic Data Pipeline
+
+Our results are powered by a 4-stage automated pipeline running on AMD hardware that prioritizes **difficulty and novelty** over volume. Unlike datasets that recycle easy problems, our pipeline leverages a Teacher Model (`GPT-OSS120b`) to generate, validate, and systematically "hike" the difficulty of reasoning problems.
+
+![Pipeline Overview](PipelineSimple.png)
+
+### Pipeline Stages
+
+1. **Stage 1: QA Generation & Consistency** 🛠️
+   - Generates novel problems from scratch
+   - Enforces correctness by requiring the teacher to generate multiple independent solution paths
+   - Only questions where all answers align are kept
+
+2. **Stage 2: De-duplication & Decontamination** 🧹
+   - Removes internal duplicates via embedding similarity
+   - **Crucial Step:** Scans against known test sets (AIME, MATH, GPQA) to ensure zero contamination
+
+3. **Stage 3: Difficulty Hiking** 🏔️
+   - Moderately challenging questions are rewritten by the teacher model
+   - Introduces deeper reasoning chains, added constraints, or cross-domain logic
+   - Systematically elevates complexity
+   - Configurable step primarily used when initial generation yields insufficient volume of high-difficulty samples
+
+---
+
+## 🚀 Quick Start
+
+### Python Inference (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "amd/SAND-Math-Qwen2.5-32B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example prompt
+prompt = "Find the number of pairs of positive integers $(m, n)$ such that $m^2 + n < 22$ and $n^2 + m < 22$."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=4096,
+    temperature=0.7, # Recommended temperature
+    do_sample=True
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print("Response:", response)
+```
+
+### Serving (vLLM & SGLang)
+
+You can easily serve this model as an OpenAI-compatible API endpoint.
+
+**Using SGLang:**
+```bash
+python -m sglang.launch_server --model-path amd/SAND-Math-Qwen2.5-32B --max-model-len 32768
+```
+
+**Using vLLM:**
+```bash
+vllm serve amd/SAND-Math-Qwen2.5-32B --max-model-len 32768
+```
+
+---
+
+## 💡 Usage Recommendations
+
+To replicate our performance benchmarks and achieve the best reasoning results, we strongly recommend the following configurations:
+
+*   **Temperature:** Set `temperature=0.7`. **DO NOT use greedy decoding**, as it can lead to performance degradation and repetitive loops.
+*   **Prompting:** For mathematical problems, include a directive to enforce structure:
+    > "Please reason step by step, and put your final answer within \boxed{}."
+*   **Context Length:** We recommend allowing an output length of **32,768 tokens**. This ensures the model has sufficient space for long Chain-of-Thought (CoT) generation.
+*   **Thinking Token:** It is recommended to enforce the model to initiate its response with the `<think>\n` token to trigger the reasoning mode effectively.
+*   **Evaluation:** When benchmarking, conduct multiple passes (Pass@K) and average the results for stability.
+
+---
+
+## 📜 License
+
+This project is licensed under the **Open RAIL-MSD** license. This is an open, royalty-free license that permits commercial use, modification, and distribution of the dataset, models, and source code.
+
+The license includes standard use-based restrictions to prevent harmful applications (e.g., illegal activities, generating harmful content, high-risk applications). These restrictions are designed to promote responsible AI development while keeping the license permissive for legitimate use cases.
+
+For full license terms and conditions, please see the [LICENSE](https://github.com/AMD-AGI/sand-pipeline/blob/main/LICENSE.txt) file.
+
+---
+
+## Citation
+
+If you use this model, dataset, or pipeline in your research, please cite our work:
+
+```bibtex
+@misc{manem025sandmathusingllmsgenerate,
+      title={SAND-Math: Using LLMs to Generate Novel, Difficult and Useful Mathematics Questions and Answers},
+      author={Chaitanya Manem and Pratik Prabhanjan Brahma and Prakamya Mishra and Zicheng Liu and Emad Barsoum},
+      year={2025},
+      eprint={2507.20527},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2507.20527},
+}
+```