Update README.md

README.md

@@ -1,70 +1,70 @@
----
-title: PromptWizard Qwen Training
-emoji: 🧙
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 4.14.0
-app_file: app.py
-pinned: false
-license: mit
-suggested_hardware: zero-a10g
----
-
-# PromptWizard Qwen Fine-tuning
-
-This Space fine-tunes Qwen models using the GSM8K dataset with PromptWizard optimization methodology.
-
-## Features
-
-- **GPU-Accelerated Training**: Uses HuggingFace's GPU infrastructure for fast training
-- **LoRA Fine-tuning**: Efficient parameter-efficient fine-tuning
-- **GSM8K Dataset**: High-quality mathematical reasoning dataset
-- **PromptWizard Integration**: Uses Microsoft's PromptWizard evaluation methodology
-- **Auto Push to Hub**: Trained models are automatically uploaded to HuggingFace Hub
-
-## How to Use
-
-1. Select your base model (default: Qwen/Qwen2.5-7B)
-2. Configure training parameters:
-   - Number of epochs (3-5 recommended)
-   - Batch size (4-8 for T4 GPU)
-   - Learning rate (2e-5 is a good default)
-3. Click "Start Training" and monitor the output
-4. The trained model will be pushed to HuggingFace Hub
-
-## Training Data
-
-The Space uses the GSM8K dataset, which contains grade school math problems. The data is formatted according to PromptWizard specifications for optimal prompt optimization.
-
-## Model Output
-
-After training, the model will be available at:
-- HuggingFace Hub: `your-username/promptwizard-qwen-gsm8k`
-- Local download: Available in the Space's output directory
-
-## Technical Details
-
-- **Base Model**: Qwen2.5-7B (or your choice)
-- **Training Method**: LoRA with rank 16
-- **Quantization**: 8-bit for memory efficiency
-- **Mixed Precision**: FP16 for faster training
-- **Gradient Checkpointing**: Enabled for memory savings
-
-## Resource Requirements
-
-- **GPU**: T4 or better recommended
-- **Memory**: 16GB+ GPU memory
-- **Training Time**: ~30-60 minutes on T4
-
-## Citation
-
-If you use this training setup, please cite:
-
-```bibtex
-@misc{promptwizard2024,
-  title={PromptWizard: Task-Aware Prompt Optimization},
-  author={Microsoft Research},
-  year={2024}
-}
+---
+title: PromptWizard Qwen Training
+emoji: 🧙
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: false
+license: mit
+suggested_hardware: zero-a10g
+---
+
+# PromptWizard Qwen Fine-tuning
+
+This Space fine-tunes Qwen models using the GSM8K dataset with PromptWizard optimization methodology.
+
+## Features
+
+- **GPU-Accelerated Training**: Uses HuggingFace's GPU infrastructure for fast training
+- **LoRA Fine-tuning**: Efficient parameter-efficient fine-tuning
+- **GSM8K Dataset**: High-quality mathematical reasoning dataset
+- **PromptWizard Integration**: Uses Microsoft's PromptWizard evaluation methodology
+- **Auto Push to Hub**: Trained models are automatically uploaded to HuggingFace Hub
+
+## How to Use
+
+1. Select your base model (default: Qwen/Qwen2.5-7B)
+2. Configure training parameters:
+   - Number of epochs (3-5 recommended)
+   - Batch size (4-8 for T4 GPU)
+   - Learning rate (2e-5 is a good default)
+3. Click "Start Training" and monitor the output
+4. The trained model will be pushed to HuggingFace Hub
+
+## Training Data
+
+The Space uses the GSM8K dataset, which contains grade school math problems. The data is formatted according to PromptWizard specifications for optimal prompt optimization.
+
+## Model Output
+
+After training, the model will be available at:
+- HuggingFace Hub: `your-username/promptwizard-qwen-gsm8k`
+- Local download: Available in the Space's output directory
+
+## Technical Details
+
+- **Base Model**: Qwen2.5-7B (or your choice)
+- **Training Method**: LoRA with rank 16
+- **Quantization**: 8-bit for memory efficiency
+- **Mixed Precision**: FP16 for faster training
+- **Gradient Checkpointing**: Enabled for memory savings
+
+## Resource Requirements
+
+- **GPU**: T4 or better recommended
+- **Memory**: 16GB+ GPU memory
+- **Training Time**: ~30-60 minutes on T4
+
+## Citation
+
+If you use this training setup, please cite:
+
+```bibtex
+@misc{promptwizard2024,
+  title={PromptWizard: Task-Aware Prompt Optimization},
+  author={Microsoft Research},
+  year={2024}
+}
 ```