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README.md

@@ -1,33 +1,53 @@
+---
+license: apache-2.0
+---
+
+
+
 <p align="center">
-  <img src="img/img_logo.png" alt="ReasonLite" width="200">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/66a056d0229269a861ac1245/9xXXms4ub9dzbcsN1IGqq.png" alt="ReasonLite" width="200">
 </p>
 
+<p align="center">
+<a href="https://github.com/AMD-AIG-AIMA/ReasonLite"><b>GitHub</b></a> |
+<a href="https://huggingface.co/datasets/amd/ReasonLite-Dataset"><b>Dataset</b></a> |
+<b>Blog</b></a> 
+
+</p>
 
-## Introduction
 
 * **ReasonLite is an ultra-lightweight math reasoning model.** With only 0.6B parameters, it leverages **high-quality data distillation** to achieve performance comparable to models over 10× its size, such as Qwen3-8B, **reaching 75.2 on AIME24 and extending the scaling law of small models.**
-* The project is fully open-source, including **model weights**, **training scripts**, **training data**, and the **data synthesis + filtering pipeline**.
+
+* 🔥 **Best-performing 0.6B reasoning model**
+* 🔓 Fully open-source — weights, scripts, datasets, synthesis pipeline
+* ⚙️ Distilled in two stages for both **efficiency** and **high performance**
 
 <p align="center">
-  <img src="img/img_acc.png" alt="ReasonLite" height="500">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/66a056d0229269a861ac1245/2VZPy7mlgpq9vFvwDc00Q.png"" alt="ReasonLite" height="500">
 </p>
 
-## Model
+
+---
+
+# 🚀 Model
 
 The model is trained in **two progressive distillation stages**.
 First, short-CoT data is used to distill **Qwen3-0.6B** into **AMD-0.6B-Turbo**, improving **AIME24 accuracy from 11.0 → 57.1**.
 Then, long-CoT data is used to obtain **AMD-0.6B**, further boosting accuracy to **75.2**.
 
-<p align="center">
-  <img src="img/img_model.png" alt="ReasonLite" height="500">
-</p>
+| Model                     | Description                                   | AIME24 | Link |
+| ------------------------- | ----------------------------------------------| ------ | ---- |
+| **amd/ReasonLite-0.6B-Turbo** | Short CoT balancing performance and efficiency | 57.1  | [🤗 HuggingFace](https://huggingface.co/amd/ReasonLite-0.6B-Turbo) |
+| **amd/ReasonLite-0.6B**       | Long CoT for high performance                  | 75.2  | [🤗 HuggingFace](https://huggingface.co/amd/ReasonLite-0.6B) |
 
+---
 
+# 📊 Evaluation Results
 
-**Evaluation Results**: We evaluate model performance on math reasoning tasks.  
-- **avg@16**: The average accuracy over 16 independently generated answers.  
-- **pass@8**: The probability that at least one correct answer appears among 8 generated samples.
+**Metrics**
 
+* **avg@16** — average accuracy from 16 sampled answers
+* **pass@8** — probability at least one correct answer appears among 8 samples
 
 | Model                     | Parameters | AMC23 avg@16 | AMC23 pass@8 | AIME25 avg@16 | AIME25 pass@8 | AIME24 avg@16 | AIME24 pass@8 |
 |---------------------------|------------|-------------|-------------|---------------|---------------|---------------|---------------|
@@ -44,204 +64,25 @@ Then, long-CoT data is used to obtain **AMD-0.6B**, further boosting accuracy to
 | ReasonLite-0.6B-Turbo     | 0.6B       | 81.6        | 99.3        | 42.7          | 69.2          | 57.1          | 79.6          |
 | **ReasonLite-0.6B**       | **0.6B**   | **95.2**    | **100**     | **62.9**      | **84.1**      | **75.2**      | **90.2**      |
 
-**Model Link**
 
-| Model                     | Description                                   | AIME24 | Link |
-| ------------------------- | ----------------------------------------------| ------ | ---- |
-| **amd/ReasonLite-0.6B-Turbo** | Short CoT balancing performance and efficiency | 57.1  | [🤗 HuggingFace](https://huggingface.co/amd/ReasonLite-0.6B-Turbo) |
-| **amd/ReasonLite-0.6B**       | Long CoT for high performance                  | 75.2  | [🤗 HuggingFace](https://huggingface.co/amd/ReasonLite-0.6B) |
-
-## Dataset
+---
 
-* A total of **343K math problems** originated from [Polaris](https://huggingface.co/datasets/POLARIS-Project/Polaris-Dataset-53K) and [OpenMathReasoni](https://huggingface.co/datasets/nvidia/ngOpenMathReasoni).
-* Using the **GPT-OSS** model as the teacher, **9.1M** model-generated raw answers were collected under both medium and high reasoning modes.
-* Pseudo-labels are created by majority voting over the model outputs.
-* Finally, **6.1M samples** were retained, including **4.3M medium-level** and **1.8M high-level** reasoning data.
-
-<p align="center">
-  <img src="img/img_data.png" alt="ReasonLite" height="500">
-</p>
-
-**Dataset Link**
+# 📚 Dataset
 
 | Dataset                | Description | Size | Link |
 | ---------------------- | ------      |---- | ---- |
 | **amd/ReasonLite-Dataset** | Short CoT | 4.3M  | [🤗 HuggingFace](https://huggingface.co/datasets/amd/ReasonLite-Dataset/viewer/default/medium) |
-| **amd/ReasonLite-Dataset**  | Long CoT | 1.8M  | [🤗 HuggingFace](https://huggingface.co/datasets/amd/ReasonLite-Dataset/viewer/default/high) |
-
-
-## Setup Environment
-
-Docker:
-
-```bash
-# Recommended ROCm Docker
-rocm/vllm-dev:open-mi300-08052025
-
-# Recommended CUDA Docker (latest vLLM)
-docker.io/vllm/vllm-openai:latest
-```
-
-Install Python packages for data synthesis and evaluation:
-
-```bash
-pip install -r requirements.txt
-```
-
-## Model Training
-
-This project uses [open-r1](https://github.com/huggingface/open-r1?tab=readme-ov-file#sft-distillation) as the training codebase, but you can use any other training framework as well.
-Below are example launch scripts and YAML configurations used in our experiments.
-
-```
-# Stage 1 SFT
-
-# Example training script:
-train/stage1.sh
-
-# Example YAML config:
-train/config_stage1.yaml
-
-
-# Stage 2 SFT
-
-# Example training script:
-train/stage2.sh
-
-# Example YAML config:
-train/config_stage2.yaml
-```
-
-## Model Evaluation
-
-Example for evaluating **ReasonLite-0.6B on AIME24**. The evaluation scripts are based on [DeepMath](https://github.com/zwhe99/DeepMath).
-
-```bash
-cd eval
-python3 start.py -c config/eval.yaml
-```
-
-## Data Generation Pipeline
-
-You will need to edit the configuration file `config/oss.yaml`, or create a new one based on your own needs.
-Then you may run through the full pipeline as follows.
-
-### Start vLLM Server
-
-```bash
-python3 vllm_start.py -c config/oss.yaml
-```
-
-### Synthetic Data
+| **amd/ReasonLite-Dataset**  | Long Cot | 1.8M  | [🤗 HuggingFace](https://huggingface.co/datasets/amd/ReasonLite-Dataset/viewer/default/high) |
 
-**Generate model answers**
+---
 
-```bash
-python3 infer.py -c config/oss.yaml -m infer
-```
-
-### Pseudo-Labels via Voting (Optional)
-
-**Obtain pseudo-labels through majority voting**
-
-```bash
-python3 infer.py -c config/oss.yaml -m vote
-```
-
-### Judging Answer Correctness
+# 📌 Citation
 
-**Judge correctness using provided labels**
-
-```bash
-python3 infer.py -c config/oss.yaml -m judge
-```
-
-**Judge correctness using pseudo-labels** (requires running `vote` first)
-
-```bash
-python3 infer.py -c config/oss.yaml -m judge_vote
-```
-
-### Filtering and Converting to Training Format
-
-**Filter out incorrect solutions and convert the correct ones to training format.**
-Specify the path to the judged data:
-
-```bash
-python3 utils/saving_to_training_format.py -d path/to/judged/data.jsonl
-```
-
-### Directory Structure for Data Storage
-
-```
-datas/
-└── <experiment_name>/
-    ├── info.jsonl           # input prompts
-    ├── answer_origin/       # raw generations
-    │   └── <timestamp>/
-    │       └── 0_1.jsonl
-    ├── answer_judge/        # judged generations
-    │   └── <timestamp>/
-    │       └── 0_1.jsonl
-    ├── vote/                # majority votes per prompt
-    │   └── <timestamp>/
-    │       └── 0_1.jsonl
-    └── answer_judge_vote/   # judged using vote labels
-        └── <timestamp>/
-            └── 0_1.jsonl
-```
-
-### JSONL Examples
-
-#### info.jsonl
-
-The files `info.jsonl` and `vote/<timestamp>/0_1.jsonl` follow this format:
-
-```jsonl
-{
-    "prompt": "Solve the equation (x^2 - x - 2 = 0).",
-    "expected_answer": "5",
-    "index": "pol:0",
-    "vote": {"x=2,-1": 5, "x=2": 1}  // Only in vote/<timestamp>/0_1.jsonl
-}
-```
-
-* `prompt` contains the math problem
-* `expected_answer` is the ground-truth label
-* `index` is the global ID using the format `"dataset_name:index"`
-* `vote` shows the voting results (only in vote files)
-
-#### Intermediate Result JSONL
-
-Files under
-`answer_origin/<timestamp>/0_1.jsonl`,
-`answer_judge/<timestamp>/0_1.jsonl`,
-`answer_judge_vote/<timestamp>/0_1.jsonl`
-follow this format:
-
-```jsonl
-{
-    "info": "<input info from info.jsonl>",
-    "index": "pol:0_3",
-    "model_input": "<full input ...>",
-    "model_output": "<full output with input prepended ...>",
-    "prompt": "The front tires of a car wear out after 25,000 km, ...",
-    "answer": "<model output ...>",
-    "judge": true  // Only in answer_judge and answer_judge_vote
-}
-```
-
-* Files under `answer_origin` contain raw model trajectories
-* `answer_judge` and `answer_judge_vote` add the boolean `judge` flag indicating correctness
-
-## Citation
-
-```
+```bibtex
 @misc{reasonlite2025,
-  title    = {ReasonLite: An Ultra-Lightweight 0.6B Reasoning Model},
-  author   = {An, Zihao and Chen, Chushi and Liu, Ziqiong and Li, Dong and Barsoum, Emad},
-  year     = {2025},
-  url      = {https://github.com/amd/ReasonLite},
-  note     = {Open-source project}
+  title={ReasonLite: Ultra-Lightweight Math Reasoning Model},
+  author={AMD AI Lab},
+  year={2025},
+  url={https://huggingface.co/amd/ReasonLite-0.6B}
 }
 ```