Tanaybh/my-first-lora-trash-model

🗑️ My First LoRA "Trash" Model - Educational Failure Case

⚠️ Warning: This model produces hilariously incoherent outputs!

This is my very first attempt at LoRA fine-tuning, shared for educational purposes. The model generates mostly gibberish, making it a perfect example of what can go wrong when learning parameter-efficient fine-tuning.

🤖 Sample "Trash" Outputs

Q: "What is deep learning?"
A: "Deep learning is a way to understand the data that is being collected. It is a way to display the data that is used to analyze the data..."

Q: "How do you debug a Python program?"
A: "The debug code is :"

Q: "Explain overfitting"
A: "Overfitting the size of the car is a very common technique for removing a car from the vehicle..."

Yes, it really thinks overfitting is about cars! 🚗

🔍 What Went Wrong?

1. Poor Input Formatting: Used plain text instead of structured instruction format
2. Bad Generation Parameters: Temperature too high, no stopping criteria
3. Wrong Model Choice: DialoGPT isn't ideal for instruction following
4. Missing Special Tokens: No clear instruction/response boundaries

🧠 What I Learned

This beautiful failure taught me:

• The critical importance of data formatting in LLM fine-tuning
• How generation parameters dramatically affect output quality
• Why model architecture choice matters for different tasks
• That LoRA training can succeed technically while failing practically

📊 Technical Details

• Base Model: microsoft/DialoGPT-small (117M params)
• LoRA Rank: 8
• Target Modules: ["c_attn", "c_proj"]
• Training Data: Alpaca dataset (poorly formatted)
• Training Loss: Actually decreased! (But outputs still terrible)
• Trainable Parameters: ~262k (0.2% of total)

🚀 How to Use (For Science!)

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load the trash model
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
base_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
model = PeftModel.from_pretrained(base_model, "Tanaybh/my-first-lora-trash-model")

# Generate hilariously bad responses
def generate_trash(prompt):
    inputs = tokenizer.encode(f"Instruction: {prompt}\nResponse:", return_tensors="pt")
    outputs = model.generate(inputs, max_length=100, temperature=0.7, do_sample=True)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Try it out!
print(generate_trash("What is machine learning?"))
# Expect something like: "Machine learning is when computers learn to computer the learning..."

🛠️ The Fix

After this failure, I learned to:

• Use proper instruction formatting with special tokens
• Lower generation temperature (0.1 instead of 0.7)
• Add clear start/stop markers
• Choose better base models for instruction following

🎯 Educational Value

This model is perfect for:

• Understanding common LoRA fine-tuning pitfalls
• Demonstrating the importance of proper data formatting
• Teaching debugging skills for LLM training
• Showing that technical success ≠ practical success

🔗 Links

• Fixed Version: [Coming soon after I improve it!]
• Training Code: See files in this repo
• Discussion: Feel free to open issues with questions!

🏷️ Tags

#LoRA #EducationalFailure #MachineLearning #LearningJourney #InstructionTuning

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Remember: Every expert was once a beginner who made mistakes like this! Share your failures, they're often more valuable than your successes. 🌟