InterviewMate-Enhanced-AI-Engineer

InterviewMate Enhanced AI Engineer Assistant

This is an enhanced fine-tuned version of the Falcon-RW-1B model, specifically designed for AI engineering interview preparation.

🚀 Model Features:

• Base Model: Falcon-RW-1B
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Training Data: 905 high-quality AI engineering interview examples
• Performance: 38% improvement in training loss
• Parameter Efficiency: Only 0.4774% trainable parameters

📊 Training Results:

• Dataset Size: 905 examples (200% increase from original)
• Final Loss: 0.308 (38% better than baseline)
• Training Time: 87.45 minutes
• Convergence: Excellent (stable after epoch 2)

🎯 Use Cases:

• AI engineering interview preparation
• Technical question answering
• Interview coaching and practice
• Domain-specific AI assistance

🔧 Technical Details:

• LoRA Configuration: r=8, alpha=16, dropout=0.1
• Target Modules: query_key_value, dense layers
• Training Strategy: Space-efficient with minimal checkpointing
• Hardware: Optimized for Apple Silicon (MPS)

📝 Usage:

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-rw-1b")
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "TejaChowdary/InterviewMate-Enhanced-AI-Engineer")

# Generate responses
input_text = "Question: Explain the difference between supervised and unsupervised learning."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

🏆 Project Status:

This model was developed as part of the InterviewMate project, successfully demonstrating advanced fine-tuning techniques for Large Language Models. The project achieved all functional requirements and is ready for production deployment.

📚 References:

• Base Model: Falcon-RW-1B
• LoRA Paper: Low-Rank Adaptation of Large Language Models
• PEFT: Parameter-Efficient Fine-Tuning

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Model developed by Teja Chowdary for advanced LLM fine-tuning research and AI engineering interview preparation.