Update README.md

README.md

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 ---
 license: mit
+language:
+- en
+base_model: Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- qwen3
+- math
+- reasoning
+- olympiad-math
+- supervised-fine-tuning
+- lora
+- cs-552
+datasets:
+- EleutherAI/hendrycks_math
+- open-r1/OpenR1-Math-220k
+- AI-MO/NuminaMath-CoT
+metrics:
+- accuracy
 ---
+
+# Math Model — Olympiad-Focused SFT Checkpoint
+
+This model is a fine-tuned version of **Qwen/Qwen3-1.7B** for mathematical reasoning, developed for the CS-552 standard project math track.
+
+The model was trained to solve competition-style mathematics problems and produce final answers in boxed LaTeX format.
+
+## Base Model
+
+- Base model: `Qwen/Qwen3-1.7B`
+- Fine-tuning method: LoRA supervised fine-tuning
+- Target task: mathematical reasoning and short-answer competition problems
+
+## Training Data
+
+The final submitted checkpoint was trained on approximately **25,165** examples from hard mathematical reasoning datasets:
+
+| Dataset / Source | Examples |
+|---|---:|
+| Hendrycks MATH | 4,759 |
+| OpenR1-Math-220k | 7,999 |
+| NuminaMath-CoT | 12,407 |
+| **Total** | **25,165** |
+
+The NuminaMath subset was filtered to focus on harder mathematical sources:
+
+- Olympiads
+- AoPS Forum
+- AMC/AIME
+- MATH
+
+The OpenR1 subset was filtered to competition-relevant categories:
+
+- Algebra
+- Geometry
+- Number Theory
+- Combinatorics
+- Inequalities
+
+## Training Details
+
+| Setting | Value |
+|---|---|
+| Base model | Qwen/Qwen3-1.7B |
+| Fine-tuning method | LoRA |
+| Epochs | 1 |
+| LoRA rank | 32 |
+| LoRA alpha | 64 |
+| LoRA dropout | 0.05 |
+| Learning rate | 1e-4 |
+| Batch size | 1 |
+| Gradient accumulation steps | 8 |
+| Precision | bfloat16 |
+| Hardware | 1 × NVIDIA A100 40GB |
+| Training steps | 3,146 |
+| Runtime | 6,761 seconds |
+| Tokens processed | 14.7M |
+| Final training loss | 0.6114 |
+| Mean token accuracy | 0.8302 |
+
+## Generation Configuration
+
+The submitted generation configuration uses sampling:
+
+```json
+{
+  "do_sample": true,
+  "temperature": 0.25,
+  "top_p": 0.85,
+  "top_k": 30
+}