---
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 Model - An Educational Failure Case

## Warning: This model produces hilariously incoherent outputs!

This is my very first attempt at fine-tuning a language model using LoRA, or Low-Rank Adaptation. I’m sharing it as a prime example of what can go wrong when you're just starting out with parameter-efficient fine-tuning. The model generates mostly gibberish, which is a great lesson in what not to do.

## Sample "Trash" Outputs

Here are some examples of the kind of gibberish this model produces:

**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 has something to do with cars.

## What Went Wrong?

I made several common mistakes during this learning process:

1.  **Poor Input Formatting**: My training data was in plain text, not a structured instruction format.
2.  **Bad Generation Parameters**: The temperature was too high, and I didn't set any stopping criteria.
3.  **Wrong Model Choice**: The base model, DialoGPT, isn't designed for instruction following.
4.  **Missing Special Tokens**: I didn't include clear instruction and response boundaries.

## What I Learned

This beautiful failure was a powerful learning experience that taught me:

- The critical importance of data formatting when fine-tuning a large language model.
- How generation parameters, like temperature, can dramatically affect the quality of the output.
- Why the choice of model architecture matters for different tasks.
- That LoRA training can technically succeed (in terms of loss reduction) while still being a practical failure.

## Technical Details

- **Base Model**: microsoft/DialoGPT-small (117M params)
- **LoRA Rank**: 8
- **Target Modules**: ["c\_attn", "c\_proj"]
- **Training Data**: A poorly formatted version of the Alpaca dataset.
- **Training Loss**: The loss actually decreased, even though the outputs were terrible.
- **Trainable Parameters**: Around 262k (0.2% of the total model).

## How to Use (For Science!)

If you're curious to see this model's amusingly bad performance for yourself, you can use the code below.

```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, "yourusername/my-first-lora-model")

# Generate a bad response
def generate_trash(prompt):
    inputs = tokenizer.encode(f"Instruction: {prompt}
Response:", 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 a response like: "Machine learning is when computers learn to computer the learning..."
```

## The Fix

After this experience, I know what to do differently next time. I plan to:

- Use a proper instruction format with special tokens.
- Lower the generation temperature from 0.7 to a more suitable value like 0.1.
- Add clear start and stop markers.
- Choose a better base model for instruction-following tasks.

## Educational Value

This model is a perfect resource for anyone who wants to:

- Understand common pitfalls of LoRA fine-tuning.
- See a practical demonstration of how important data formatting is.
- Learn debugging skills for language model training.
- Understand that technical success doesn't always equal practical success.

## Links

- **Fixed Version**: Coming soon after I improve my process.
- **Training Code**: See the files in this repository.
- **Discussion**: Feel free to open issues with any questions.

---

Remember, every expert was once a beginner who made mistakes like this. Sharing your failures is often more valuable than sharing your successes.