---
language:
- en
license: apache-2.0
base_model: microsoft/DialoGPT-small
tags:
- peft
- lora
- instruction-tuning
- educational
- failure-case
- learning-journey
library_name: peft
---

# 🗑️ 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!)

```python
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`

---

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