Add comprehensive model card

README.md

@@ -1,12 +1,57 @@
 ---
-base_model: Qwen/Qwen2.5-72B-Instruct
-library_name: peft
 license: apache-2.0
+base_model: Qwen/Qwen2.5-72B-Instruct
 tags:
-  - elle
   - math
+  - reasoning
+  - qwen2
   - lora
-  - qwen
+  - peft
+  - numina
+library_name: peft
+pipeline_tag: text-generation
+datasets:
+  - AI-MO/NuminaMath-CoT
 ---
+
 # Elle-72B-Math-v2
-Fine-tuned on NuminaMath-CoT for mathematical reasoning.
+
+## Model Description
+
+Elle-72B-Math-v2 is a LoRA adapter fine-tuned on [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) for mathematical reasoning using the NuminaMath-CoT dataset.
+
+## Model Details
+
+- **Base Model**: Qwen/Qwen2.5-72B-Instruct
+- **Adapter Type**: LoRA
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 128
+- **Target Modules**: All linear layers
+- **Training Data**: NuminaMath-CoT
+
+## Training
+
+Fine-tuned using:
+- Chain-of-thought mathematical reasoning examples
+- Step-by-step problem decomposition
+- Multiple solution strategies (algebraic, numerical, symbolic)
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-72B-Instruct",
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+model = PeftModel.from_pretrained(base_model, "aphoticshaman/elle-72b-math-v2")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-72B-Instruct")
+```
+
+## License
+
+Apache 2.0