added readme and app.py

README.md

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-title: Phi-2 QLoRA Assistant
+title: Phi2 QLoRA
 emoji: 🤖
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-# Phi-2 QLoRA Fine-tuned Assistant (CPU-Optimized)
+# Phi-2 QLoRA Assistant
 
-This is a lightweight CPU-optimized version of Microsoft's Phi-2 model fine-tuned using QLoRA (Quantized Low-Rank Adaptation) technique. The model has been optimized to run efficiently on CPU environments while still providing helpful responses for coding, explanations, and writing tasks.
-
-## Model Description
-
-- **Base Model**: Microsoft Phi-2
-- **Training Method**: QLoRA (Quantized Low-Rank Adaptation)
-- **Optimization**: CPU-optimized with reduced parameters
-- **Primary Use Cases**: Code generation, technical explanations, and professional writing
-
-## Usage Tips
-
-### For Code Generation (Temperature: 0.3-0.5)
-```python
-# Example prompt:
-"Write a Python function to calculate factorial"
-```
-
-### For Technical Explanations (Temperature: 0.4-0.5)
-```text
-# Example prompt:
-"Explain machine learning simply"
-```
-
-### For Professional Writing (Temperature: 0.4-0.6)
-```text
-# Example prompt:
-"Write a short email to schedule a meeting"
-```
-
-## Parameters Guide (CPU-Optimized)
-
-- **Maximum Length**: 64-256 (default: 192)
-  - Keep this low (128-192) for faster responses on CPU
-  - Higher values will significantly slow down generation
-
-- **Temperature**: 0.1-0.7 (default: 0.4)
-  - 0.3-0.4: Best for code generation
-  - 0.4-0.5: Best for explanations
-  - 0.5-0.6: Best for creative writing
-
-- **Top P**: 0.5-0.9 (default: 0.8)
-  - Controls diversity of word choices
-  - Lower values = more focused responses
-
-## Performance Notes
-
-This is a CPU-optimized version with the following considerations:
-- Responses will be shorter than the GPU version
-- Generation takes longer on CPU (be patient)
-- Memory usage is optimized for CPU environments
-- Best for shorter, focused prompts
-
-## Model Links
-
-- **Model Card**: [pradeep6kumar2024/phi2-qlora-assistant](https://huggingface.co/pradeep6kumar2024/phi2-qlora-assistant)
-- **Base Model**: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
-
-## License
-
-This demo is released under the MIT License.
-
-## Example Usage
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-import torch
-
-# Load base model and adapter (CPU optimized)
-base_model = AutoModelForCausalLM.from_pretrained(
-    "microsoft/phi-2",
-    torch_dtype=torch.float32,  # Use float32 for CPU
-    device_map="cpu",
-    low_cpu_mem_usage=True
-)
-model = PeftModel.from_pretrained(
-    base_model, 
-    "pradeep6kumar2024/phi2-qlora-assistant",
-    torch_dtype=torch.float32,
-    device_map="cpu"
-)
-tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2")
-
-# Generate text (CPU optimized)
-prompt = "Write a Python function to calculate factorial"
-inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(
-    **inputs, 
-    max_length=256,
-    temperature=0.4,
-    top_p=0.8,
-    num_beams=1  # Greedy decoding for CPU
-)
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-```
-
-## Example Outputs
-
-1. **Coding Task**:
-   ```python
-   def factorial(n):
-       if n == 0 or n == 1:
-           return 1
-       return n * factorial(n-1)
-   ```
-
-2. **Technical Explanation**:
-   "Machine learning is a branch of artificial intelligence that enables computers to learn from data without being explicitly programmed. It works by analyzing patterns in data and making predictions based on those patterns."
-
-3. **Professional Writing**:
-   "Subject: Team Meeting Request
-   
-   Hi Team,
-   
-   I'd like to schedule a meeting next week to discuss our current project. Please let me know your availability.
-   
-   Thanks,
-   [Your Name]"
-
-## Limitations
-
-- CPU version generates shorter responses than GPU version
-- Generation is slower on CPU environments
-- Works best with clear, concise prompts
-- Memory constraints may limit very complex generations
-
-## Acknowledgments
-
-- Microsoft for the Phi-2 base model
-- Hugging Face for the transformers library and hosting
-- The QLoRA paper authors for the fine-tuning technique 
+A CPU-optimized version of Microsoft's Phi-2 model fine-tuned with QLoRA.