Slide 1:

Introduction to Fine-tuning Large Language Models (LLMs)

Fine-tuning is a process of adapting a pre-trained language model to a specific task or domain by updating its parameters on a smaller, task-specific dataset. This cool method allows you to leverage the knowledge and capabilities of the base model while tailoring it to your specific needs. In this presentation, we’ll explore five popular fine-tuning techniques: LoRA, LoRA-FA, VeRA, Delta-LoRA, and LoRA+.

Slide 2:

LoRA (Low-Rank Adaptation)

LoRA is a lightweight and efficient fine-tuning technique that introduces task-specific knowledge to the pre-trained model by adding small rank modifications to the model’s weights. This way reduces the number of trainable parameters, making it memory-efficient and suitable for resource-constrained environments.

Here’s a handy trick you’ll love! Here’s how we can tackle this:

import torch
from peft import LoraModel, get_peft_model

base_model = ... # Load your pre-trained LLM
lora_model = LoraModel(base_model, r=8, lora_alpha=32, target_modules=['q_proj', 'v_proj'])
model = get_peft_model(lora_model, base_model)

# Fine-tune the model using your task-specific dataset
# ...

Slide 3:

LoRA-FA (LoRA with Frobenius Norm Regularization)

LoRA-FA is an extension of the LoRA technique that introduces a regularization term based on the Frobenius norm of the rank decomposition matrices. This regularization helps to improve the stability and performance of the fine-tuned model by preventing the rank decomposition matrices from becoming too large.

Let me walk you through this step by step! Here’s how we can tackle this:

import torch
from peft import LoraModel, get_peft_model

base_model = ... # Load your pre-trained LLM
lora_model = LoraModel(base_model, r=8, lora_alpha=32, target_modules=['q_proj', 'v_proj'], lora_dropout=0.1)
model = get_peft_model(lora_model, base_model)

# Fine-tune the model using your task-specific dataset with Frobenius norm regularization
# ...

Slide 4:

VeRA (Virtual Residual Adapters)

VeRA is a fine-tuning technique that introduces virtual residual adapters to the pre-trained model. These adapters are learned during fine-tuning and added to the base model’s outputs, enabling the model to adapt to the target task while preserving the original knowledge.

Let’s break this down together! Here’s how we can tackle this:

import torch
from peft import VeRAModel, get_peft_model

base_model = ... # Load your pre-trained LLM
vera_model = VeRAModel(base_model, d_model=768, alpha=16)
model = get_peft_model(vera_model, base_model)

# Fine-tune the model using your task-specific dataset
# ...

Slide 5:

Delta-LoRA

Delta-LoRA is a variant of the LoRA technique that aims to improve the fine-tuning performance by introducing an additional set of parameters called delta weights. These delta weights are learned during fine-tuning and combined with the base model’s weights, allowing for more flexibility and expressiveness.

Here’s where it gets exciting! Here’s how we can tackle this:

import torch
from peft import DeltaLoraModel, get_peft_model

base_model = ... # Load your pre-trained LLM
delta_lora_model = DeltaLoraModel(base_model, r=8, lora_alpha=32, target_modules=['q_proj', 'v_proj'])
model = get_peft_model(delta_lora_model, base_model)

# Fine-tune the model using your task-specific dataset
# ...

Slide 6:

LoRA+ (LoRA with Normalization)

LoRA+ is an extension of the LoRA technique that introduces a normalization step to the rank decomposition matrices. This normalization helps to improve the stability and performance of the fine-tuned model by preventing the rank decomposition matrices from becoming too large or too small.

Here’s a handy trick you’ll love! Here’s how we can tackle this:

import torch
from peft import LoraModel, get_peft_model

base_model = ... # Load your pre-trained LLM
lora_model = LoraModel(base_model, r=8, lora_alpha=32, target_modules=['q_proj', 'v_proj'], lora_normalization='batchnorm')
model = get_peft_model(lora_model, base_model)

# Fine-tune the model using your task-specific dataset
# ...

Slide 7:

Practical Considerations

When fine-tuning large language models using these techniques, there are several practical considerations to keep in mind:

1. Choose an appropriate fine-tuning technique based on your resource constraints and performance requirements.
2. Experiment with different hyperparameters (e.g., learning rate, batch size, epochs) to optimize the fine-tuning process.
3. Monitor the training process and evaluate the model’s performance on a validation dataset.
4. Implement techniques like early stopping and checkpointing to prevent overfitting and save computational resources.

Let me walk you through this step by step! Here’s how we can tackle this:

# Example of implementing early stopping
patience = 5
best_val_loss = float('inf')
early_stopping_counter = 0

for epoch in range(num_epochs):
    train_loss = train_one_epoch(model, train_loader)
    val_loss = evaluate(model, val_loader)

    if val_loss < best_val_loss:
        best_val_loss = val_loss
        early_stopping_counter = 0
    else:
        early_stopping_counter += 1

    if early_stopping_counter >= patience:
        print("Early stopping triggered.")
        break

Slide 8:

Fine-tuning on Custom Datasets

To fine-tune a pre-trained LLM on a custom dataset, you need to prepare your data in the appropriate format. Here’s an example of how you can load and preprocess your dataset for fine-tuning:

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

import torch
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("your_lm_name")

def preprocess_data(data):
    inputs = tokenizer(data["input_text"], padding="max_length", truncation=True, return_tensors="pt")
    labels = tokenizer(data["target_text"], padding="max_length", truncation=True, return_tensors="pt")["input_ids"]
    return inputs, labels

train_dataset = Dataset.from_dict(train_data, preprocess_data)
val_dataset = Dataset.from_dict(val_data, preprocess_data)

Slide 9:

Fine-tuning with LoRA

Here’s an example of how you can fine-tune a pre-trained LLM using the LoRA technique:

Let’s make this super clear! Here’s how we can tackle this:

import torch
from peft import LoraModel, get_peft_model, prepare_model_for_int8_training
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("your_lm_name")
tokenizer = AutoTokenizer.from_pretrained("your_lm_name")

lora_model = LoraModel(base_model, r=8, lora_alpha=32, target_modules=['q_proj', 'v_proj'])
model = get_peft_model(lora_model, base_model)
model = prepare_model_for_int8_training(model)

# Fine-tune the model using your task-specific dataset
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)

for epoch in range(num_epochs):
    for inputs, labels in train_loader:
        # Fine-tuning loop
        # ...

SSlide 10:

Fine-tuning with VeRA

Here’s an example of how you can fine-tune a pre-trained LLM using the VeRA technique:

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

import torch
from peft import VeRAModel, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("your_lm_name")
tokenizer = AutoTokenizer.from_pretrained("your_lm_name")

vera_model = VeRAModel(base_model, d_model=768, alpha=16)
model = get_peft_model(vera_model, base_model)

# Fine-tune the model using your task-specific dataset
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)

for epoch in range(num_epochs):
    for inputs, labels in train_loader:
        outputs = model(**inputs, labels=labels)
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

In this example, we first load the pre-trained LLM and tokenizer using the AutoModelForCausalLM and AutoTokenizer classes from the Hugging Face Transformers library. We then create a VeRAModel instance with the pre-trained model and the desired hyperparameters (e.g., d_model and alpha).

Next, we obtain the fine-tunable model by calling get_peft_model with the VeRAModel instance and the pre-trained model.

We then set up the training loop, where we iterate over batches of data from our task-specific dataset. For each batch, we pass the inputs and labels through the model to obtain the output logits and compute the loss. We then backpropagate the loss, update the model’s parameters using the optimizer, and reset the gradients for the next iteration.

Slide 11:

Fine-tuning with Delta-LoRA

Here’s an example of how you can fine-tune a pre-trained LLM using the Delta-LoRA technique:

Let’s break this down together! Here’s how we can tackle this:

import torch
from peft import DeltaLoraModel, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("your_lm_name")
tokenizer = AutoTokenizer.from_pretrained("your_lm_name")

delta_lora_model = DeltaLoraModel(base_model, r=8, lora_alpha=32, target_modules=['q_proj', 'v_proj'])
model = get_peft_model(delta_lora_model, base_model)

# Fine-tune the model using your task-specific dataset
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)

for epoch in range(num_epochs):
    for inputs, labels in train_loader:
        # Fine-tuning loop
        # ...

Slide 12:

Fine-tuning with LoRA+

Here’s an example of how you can fine-tune a pre-trained LLM using the LoRA+ technique:

Here’s where it gets exciting! Here’s how we can tackle this:

import torch
from peft import LoraModel, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("your_lm_name")
tokenizer = AutoTokenizer.from_pretrained("your_lm_name")

lora_model = LoraModel(base_model, r=8, lora_alpha=32, target_modules=['q_proj', 'v_proj'], lora_normalization='batchnorm')
model = get_peft_model(lora_model, base_model)

# Fine-tune the model using your task-specific dataset
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)

for epoch in range(num_epochs):
    for inputs, labels in train_loader:
        # Fine-tuning loop
        # ...

Slide 13:

Evaluation and Deployment

After fine-tuning your LLM, it’s essential to evaluate its performance on a held-out test dataset. You can use metrics like perplexity, BLEU score, or task-specific metrics to assess the model’s performance. Once you’re satisfied with the results, you can save the fine-tuned model and deploy it for inference or further fine-tuning on new tasks.

Ready for some cool stuff? Here’s how we can tackle this:

from transformers import AutoModelForCausalLM

fine_tuned_model = AutoModelForCausalLM.from_pretrained("path/to/fine-tuned-model")

# Evaluate the model on a test dataset
test_loader = DataLoader(test_dataset, batch_size=16)
metrics = evaluate(fine_tuned_model, test_loader)

# Save the fine-tuned model for deployment
fine_tuned_model.save_pretrained("path/to/save/fine-tuned-model")

Slide 14:

Additional Resources

Here are some additional resources for further learning and exploration:

1. LoRA: Arxiv link: https://arxiv.org/abs/2106.09685
2. LoRA-FA: Arxiv link: https://arxiv.org/abs/2302.08677
3. VeRA: Arxiv link: https://arxiv.org/abs/2303.03493
4. Delta-LoRA: Arxiv link: https://arxiv.org/abs/2303.01554
5. LoRA+: Arxiv link: https://arxiv.org/abs/2305.11209

These papers provide in-depth explanations, implementation details, and experimental results for the respective fine-tuning techniques.

🎊 Awesome Work!

You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.

What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.

Keep coding, keep learning, and keep being awesome! 🚀