How to Fine Tune Large Language Models (LLMs)

What does LLM fine tuning mean?

LLM fine-tuning is the process of taking a pre-trained LLM and training it on a specific dataset to improve its performance for a particular use case. This approach is cost-effective, as it adapts an existing model to a new domain without the need for extensive computational resources. Instead of building and training a model from scratch, an open-source model can be fine-tuned with a custom dataset to achieve the desired results.

We can understand the LLM fine-tuning process using the following diagram:

Here, you can see that we take a pre-trained LLM with a dataset and fine-tune the model. After fine-tuning, we get a fine-tuned LLM for our use case, specifically trained on our custom dataset.

Now that we understand what fine-tuning LLM means, let’s discuss the different ways to fine-tune a large language model.

Different ways to fine-tune LLMs

LLM fine-tuning involves adjusting the weights of a pre-trained LLM by training it on a domain-specific or task-specific dataset. Depending on the available computational resources, the level of customization needed, and the task requirements, we can fine-tune LLMs using techniques like supervised fine-tuning, reinforcement learning with human feedback(RLHF), low-rank adaptation (LoRA), etc. Let’s discuss these methods individually.

Supervised fine-tuning (SFT)

Supervised Fine-Tuning (SFT) is LLM fine-tuning method to adapt a pre-trained LLM for a specific task by training it on a task-specific dataset using supervised learning. In SFT, we take a pre-trained LLM and train it further on a labeled dataset specific to our use case such as sentiment analysis, document classification, or question-answering. After fine-tuning, the LLM’s parameters are fully updated for the specific use case.

SFT helps us build fine-tuned LLMs with high accuracy for domain-specific tasks. However, it requires large, labeled datasets and has a high computational cost, as all the model parameters are updated. To reduce the training costs, we can use parameter-efficient fine-tuning methods.

Parameter efficient fine-tuning (PEFT)

Parameter Efficient Fine-Tuning (PEFT) includes LLM fine-tuning methods that fine-tune only a small subset of the LLM parameters instead of the entire model. In PEFT, we can fine-tune LLMs using one of the following ways:

• Add additional layers to the model: We can freeze the parameters of the pre-trained models and fine-tune them by adding additional layers. In this case, only the newly added layers are trained while fine-tuning, and we get a fine-tuned model specific to our use case. Low-Rank Adaptation (LoRA) is one of the techniques that we can use to fine-tune LLMs by adding new layers.
• Prefix tuning: In prefix tuning, we append special trainable tokens to each layer of the LLM instead of modifying the model weights. During fine-tuning, the newly added tokens are trained to adapt to the training data instead of changing the original model parameters. Thus, prefix-tuning allows us to fine-tune LLMs by encoding task-specific information in a minimal number of tokens and updating only the attention layers in the model.
• Prompt tuning: In prompt tuning, we fine-tune the LLMs by introducing trainable embeddings called soft prompts to the input before passing it to the model. The LLM learns a small set of task-specific trainable prompt embeddings and uses them for the tasks.

Reinforcement learning with human feedback (RLHF)

Reinforcement learning with human feedback (RLHF) is an LLM fine-tuning technique that we use to align LLMs with human preferences. RLHF is used in ChatGPT, Gemini, and other conversational AI models to make responses more helpful and aligned with human values.

RLHF consists of three main steps:

1. Fine-tune a pre-trained model using SFT on human-annotated responses.
2. Train a reward model that ranks the responses of the fine-tuned LLM.
3. Use reinforcement learning to optimize the model based on the reward model’s rankings.

You can use any of these methods to fine-tune LLMs based on the use case and available resources. To help you understand how LLM fine-tuning works, let’s fine-tune an LLM for a sentiment analysis task using SFT.

Step-by-step process to fine-tune LLMs

We will fine-tune the DistilBERT base model using the IMDB reviews dataset for sentiment analysis task. To do this, we will use the supervised fine-tuning technique. To begin with, let’s first install the necessary modules for fine-tuning.

Step 1- Setting up the environment

To fine-tune the LLM, we need the following modules:

• The datasets module to download the sentiment analysis dataset.
• The evaluate module for model evaluation.
• The huggingface-hub module to download the pre-trained DistilBERT model.
• The trl module for using SFTTrainer for supervised fine-tuning.
• The huggingface-cli module to sign in to Hugging Face Hub using the command-line terminal.

You can install all these modules by executing the following command in the command-line terminal:

pip install datasets evaluate transformers transformers[torch] huggingface-hub huggingface-cli trl  

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Next, we need to generate a HuggingFace token with write access from the HuggingFace tokens page to download pre-trained models.

After generating the token, we can log in to HuggingFace Hub using the login() function. The login() function defined in the huggingface_hub module takes the huggingface token as input to its token parameter.

import huggingface_hub  
huggingface_hub.login(token='your_HuggingFace_token')  

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Alternatively, we can execute the huggingface-cli command in the command-line terminal and input the token to initialize a session to access models and datasets from HuggingFace Hub. After logging in, we can access and download the pre-trained LLMs from the HuggingFace model repository.

Step 2 - Download the pre-trained LLM

Next, we will download and fine-tune the DistilBERT base LLM for the sentiment analysis task. To do this, we will use the AutoModelForSequenceClassification wrapper defined in the transformers module.

• AutoModelForSequenceClassification is a wrapper model from the transformers library specially designed for sequence classification tasks like sentiment analysis, spam detection, and topic classification. This wrapper automatically loads the appropriate model architecture (e.g., BERT, DistilBERT, RoBERTa) based on the specified model.
• To download the model, we will pass the pre-trained model name distilbert-base-uncased to the from_pretrained() method by invoking it on the AutoModelForSequenceClassification wrapper.

After execution, the from_pretrained() method returns the specified pre-trained model. We will assign this model to the pretrained_model variable.

from transformers import AutoModelForSequenceClassification  
pretrained_model_name = "distilbert-base-uncased"  
pretrained_model = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name)  

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After executing the from_pretrained() function, we also get this text output that suggests we can train this model to use it for predictions and inference.

Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']  
You should probably TRAIN this model on a downstream task to be able to use it for predictions and inference.  

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Now that we have the pre-trained model, let’s download the dataset.

Step 3- Download the IMDB dataset

We will fine-tune the DistilBERT base LLM for sentiment analysis task using the IMDB dataset. To download the dataset, we will use the load_dataset() function defined in the datasets module. The load_dataset() function takes the dataset name as its first input argument. To load the training set, we can pass the string “train” as input to the split parameter. To load the test dataset, we can pass the string “test” as input to the split parameter, as shown in the following example:

from datasets import load_dataset  
training_data = load_dataset("imdb",split="train")  
test_data = load_dataset("imdb",split="test")  
print(training_data)  
print(test_data)  

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Output:

Training data: Dataset({  
    features: ['text', 'label'],  
    num_rows: 25000  
})  
Test data: Dataset({  
    features: ['text', 'label'],  
    num_rows: 25000  
})  

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In the output, observe that the training and the test data contain two columns, namely text and label. The text column contains reviews from IMDB. The label column contains the values 0 and 1, which specify the sentiment for the reviews, as shown in the following example:

# Get the first row in the training data  
training_text=training_data['text'][0]  
 
# Get the first label in the training data  
training_label=training_data['label'][0]  
 
# Get the first row in the test data  
test_text=test_data['text'][0]  
 
# Get the first label in the test data  
test_label=test_data['label'][0]  
 
print("The first review in the training data:", training_text)  
print("The sentiment label for the first review in the training data:", training_label)  
print("The first review in the text data:", training_text)  
print("The sentiment label for the first review in the test data:", test_label)  

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Output:

The first review in the training data: I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first, it was seized by U.S. customs if it ever tried to enter this......  
The sentiment label for the first review in the training data: 0  
The first review in the text data: I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first, it was seized by U.S.......  
The sentiment label for the first review in the test data: 0  

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Having downloaded the pre-trained model and the dataset, let’s create a tokenizer and a data collator to pre-process the dataset and make it suitable for the pre-trained model.

Step 4 - Create a tokenizer and a data collator

We need a tokenizer that will tokenize the data in the same format as the dataset used to train the pre-trained model. To create the tokenizer, we will use the AutoTokenizer class. The from_pretrained() method in the AutoTokenizer class takes the pre-trained model name as input and automatically loads the correct tokenizer for the pre-trained model.

from transformers import AutoTokenizer  
 
pretrained_model_name = "distilbert-base-uncased"  
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name)  

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After getting the tokenizer, let’s write a function that takes a row from the dataset as its input and tokenizes the text in the input row.

def tokenize(record):  
    outputs = tokenizer(record['text'], truncation=True, padding="max_length", max_length=512)  
    return outputs  

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In this code,

• The parameter record represents a row in the dataset and text from the record is the input that we want to tokenize.
• Setting the truncation parameter to True ensures that if the text is longer than the maximum length allowed by the tokenizer (max_length = 512 in this case), it will be truncated to fit within that limit.
• The padding parameter is set to max_length to pad the tokenized output to exactly max_length tokens.
• We have set the max_length parameter to 512, as transformer models like BERT and DistilBERT have a maximum context length of 512 tokens.

After implementing the tokenizer, let’s also implement a data collator that dynamically pads the tokenized inputs to the longest sequence in a batch. This ensures that all sequences are of the same length, allowing efficient batch processing in fine-tuning LLMs.

To implement the data collator, we will use the DataCollatorWithPadding() function that takes the tokenizer as its input and returns a data collator.

from transformers import DataCollatorWithPadding  
data_collator = DataCollatorWithPadding(tokenizer)  

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Now that we have created the tokenizer and the data collator, let’s pre-process the data for fine-tuning.

Step 5- Pre-process the dataset for LLM fine-tuning

For pre-processing the data, we will apply the tokenize() function on the text column in the dataset using the map() method.

tokenized_training_data = training_data.map(tokenize, batched=True)  
tokenized_test_data = test_data.map(tokenize, batched=True)  
 
print("Tokenized training data:",tokenized_training_data)  
print("Tokenized test data:",tokenized_test_data)  

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After tokenization, we get a dataset with two additional columns, i.e., ‘input_ids’ and ‘attention_mask’, as shown in the output.

Tokenized training data: Dataset({  
    features: ['text', 'label', 'input_ids', 'attention_mask'],  
    num_rows: 25000  
})  
Tokenized test data: Dataset({  
    features: ['text', 'label', 'input_ids', 'attention_mask'],  
    num_rows: 25000  
})  

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• The input_ids feature contains a vector representing the tokenized format, i.e., the numerical representation of the text from the text column of the dataset.
• The attention_mask feature contains a binary mask for each token in the input_ids feature that tells the model which tokens to pay attention to. The attention_mask feature prevents the model from processing padding tokens without meaning during fine-tuning.

# Get the tokenized_version of the first row in the training data  
training_input_id=tokenized_training_data['input_ids'][0]  
 
# Get the attention mask of the first row in the training data  
training_attention_mask=tokenized_training_data['attention_mask'][0]  
 
# Get the tokenized_version of the first row in the text data  
test_input_id=tokenized_test_data['input_ids'][0]  
 
# Get the attention mask of the first row in the text data  
test_attention_mask=tokenized_test_data['attention_mask'][0]  
 
print("The tokenized version of the first review in the training data:", training_input_id)  
print("The attention mask for the first review in the training data:", training_attention_mask)  
print("The tokenized version of the first review in the test data:", test_input_id)  
print("The attention mask for the first review in the test data:", test_attention_mask)  

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Output:

The tokenized version of the first review in the training data: [101, 1045, 12524, 1045, 2572, 8025, 1011, 3756, 2013, 2026, 2678, 3573, 2138,5129, 2009, 2043, 2009,...... 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  
The attention mask for the first review in the training data: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,.....0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  
The tokenized version of the first review in the test data: [101, 1045, 2293, 16596, 1011, 10882, 1998, 2572, 5627, 2000, 2404,......, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  
The attention mask for the first review in the test data: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,......, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  

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Next, we will implement a function to evaluate the model’s performance while fine-tuning.

Step 6- Create a function to evaluate the model

To evaluate the model while fine-tuning the LLM, we’ll write a function that computes its accuracy from the predictions and labels in the training data.

import numpy as np  
import evaluate  
 
def compute_metrics(predictions_and_labels):  
    metric = evaluate.load("accuracy")  
    logits, labels = predictions_and_labels  
    predictions = np.argmax(logits, axis=-1)  
    return metric.compute(predictions=predictions, references=labels)  

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Having implemented the function to evaluate the model performance, let’s create a supervised model trainer to fine-tune the pre-trained LLM using the SFTTrainer() function.

Step 7- Create a supervised model trainer to fine-tune the pre-trained LLM

To create the model trainer, we first need to define the training parameters such as number of epochs while training, output directory for the fine-tuned model, batch size for training and evaluation, and the evaluation strategy. Then, we need to create a model trainer using the tokenizer, data collator, pretrained model, training dataset, test dataset, and other training parameters.

To define the training arguments, we will use the TrainingArguments() function defined in the transformers module.

• The TrainingArguments() function takes the number of epochs, i.e., num_train_epochs, the directory for saving the fine-tuned model, i.e., output_dir, the batch size of training and evaluating the model, and the evaluation strategy as its input. We will set the batch size for training and evaluating the models to 16. Also, we will set the eval_strategy parameter to ‘epoch’ so that the model evaluation is done after every epoch.
• To create the model trainer, we will use the SFTTrainer() function defined in the trl module. The SFTTrainer() function takes the pre-trained model, the tokenizer, the data collator, the training dataset, the test dataset, the function to evaluate the model, and other training arguments as inputs to the model, processing_class, data_collator, train_dataset, eval_dataset, compute_metrics, and the args parameter respectively.

After executing the SFTTrainer() function, we get a trainer object ready for fine-tuning.

from transformers import TrainingArguments  
from trl import SFTTrainer  
  
# Create training arguments  
training_args = TrainingArguments(num_train_epochs=10,  
                                  output_dir="text-classifier-supervised-codecademy",  
                                  per_device_train_batch_size=16,  
                                  per_device_eval_batch_size=16,  
                                  eval_strategy="epoch")  
  
# Create a supervised model trainer for fine-tuning the LLM model  
trainer = SFTTrainer(model=pretrained_model,   
                     processing_class=tokenizer,  
                     data_collator=data_collator,  
                     args=training_args,  
                     train_dataset=tokenized_training_data,  
                     eval_dataset=tokenized_test_data,   
                     compute_metrics=compute_metrics)  

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After creating the trainer, let’s fine-tune the LLM.

Step 8- Train and save the fine-tuned LLM

To fine-tune the LLM using the SFTTrainer, we will invoke the train() method on the trainer object returned by the SFTTrainer() function.

trainer.train()  

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After training the model, we can save it to the local machine using the save_model() method. The save_model() method, when invoked on the trainer object, takes the file path for the model as its input and saves the model to the local storage.

file_path_to_save_the_model =  '/home/aditya1117/codes/text-classifier-supervised-codecademy'  
trainer.save_model(file_path_to_save_the_model)  

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We can also push the fine-tuned LLM model to the HuggingFace models repository. For this, we need to invoke the push_to_hub() method on the trainer and pass a model name for the fine-tuned model:

model_name =  "text-classifier-supervised-codecademy"  
trainer.push_to_hub(model_name)  

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After successful execution, the push_to_hub() function returns a commit message that contains the link to the fine-tuned model in the format https://huggingface.co/<user_name>/<model_name> , as shown in the following output:

CommitInfo(commit_url='https://huggingface.co/raditya1117/text-classifier-supervised-codecademy/commit/5a04ca039661b209c0c4cedaf7f680f10e1945d1', commit_message='text-classifier-supervised-codecademy', commit_description='', oid='5a04ca039661b209c0c4cedaf7f680f10e1945d1', pr_url=None, repo_url=RepoUrl('https://huggingface.co/raditya1117/text-classifier-supervised-codecademy', endpoint='https://huggingface.co', repo_type='model', repo_id='raditya1117/text-classifier-supervised-codecademy'), pr_revision=None, pr_num=None)  

In the output, you can see that our fine-tuned LLM is stored at the address https://huggingface.co/raditya1117/text-classifier-supervised-codecademy. Here, raditya1117 is the username of the HuggingFace account, and text-classifier-supervised-codecademy is the model name.

If you go to the above link, you will see all the details of the model in the model card. You can also update the details to provide more information about the training dataset and other metrics.

Picture

Step 9- Load the fine-tuned model

After saving the fine-tuned model, you can use the AutoModelForSequenceClassification.from_pretrained() function to load and use it. To load the fine-tuned model from the HuggingFace repository, you need to pass the model name in the format ‘your_huggingface_username/model_name’, as shown below:

model_name = "raditya1117/text-classifier-supervised-codecademy"  
model = AutoModelForSequenceClassification.from_pretrained(model_name)  

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To load the fine-tuned model from the local storage, you can pass the model’s file path to the from_pretrained() method.

model_filepath = "/home/aditya1117/codes/text-classifier-supervised-codecademy"  
model = AutoModelForSequenceClassification.from_pretrained(model_filepath)  

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After loading the model, you can use it for inference or fine-tune it further to improve its accuracy and performance.

Why fine tune LLMs?

Pre-trained LLMs like GPT, GPT-2, GPT-4, LLaMA, or BERT are trained on massive datasets and excel at general tasks. Training these models from scratch requires huge computational resources and monetary expenses, often beyond the capabilities of individuals and small organizations. Also, these LLMs aren’t domain or task-specific. Fine-tuning these pre-trained LLMs helps us in many ways.

• LLM fine-tuning saves costs: Instead of training an LLM from scratch, fine-tuning allows us to use pre-trained models for specialized tasks at a fraction of the cost. Fine-tuning requires significantly fewer computational resources, making it a more accessible option.
• Improves model performance for specific domains: Fine-tuning also allows us to improve an LLM’s accuracy, efficiency, and reliability by training it on specialized data. For example, if we want to build an application for the legal domain, we can train an LLM like BERT on legal documents, historical case documents, and laws. After this, the fine-tuned model will understand legal jargon and context. We can then use the fine-tuned model to assist lawyers or judges.
• Improve model performance for specific use cases: In addition to domain-specific fine-tuning, we can also fine-tune LLMs for specific use cases like sentiment analysis, text summarization, or question answering. For instance, if we want to create an LLM application for a chatbot, we can fine-tune the model for question-answering tasks.
• Fine-tuning can help adapt LLMs for different languages: LLMs are primarily trained on datasets that are predominantly in the English language. Due to this, LLMs often struggle with multilingual tasks. We can fine-tune the pre-trained LLMs to support other languages with a low amount of available training materials. Fine-tuning LLMs for different languages will help use LLMs in specific geographical areas as the fine-tuned model can handle the user’s language accurately.
• Align models to human values and preferences: Due to the inherent bias in the training data, LLMs can generate biased, harmful, or irrelevant responses. We can fine-tune the LLMs to improve the response quality and align the model’s behavior with human values to enhance user experience.

Now that we understand the reasons for fine-tuning an LLM, let’s discuss the difference between LLM fine-tuning and RAG

Difference between LLM fine-tuning and RAG

Both Retrieval augmented generation (RAG) and LLM fine-tuning are useful in adapting LLM models for specific use cases and enterprise applications.

RAG retrieves relevant documents from a given document corpus at query time and uses them as context to answer questions. In contrast, fine-tuning involves training an existing LLM on a specific dataset so it learns the information and patterns within the data. Due to this, RAG is best suited for dynamic data as it offers low hallucination risk by using up-to-date context during inference. Fine-tuning is more appropriate for static datasets and improves the base model by embedding domain knowledge.

The major differences between RAG and LLM fine-tuning are as follows:

LLM fine-tuning can be helpful if you want to build an application for tasks like classification, tagging, and text summarization, or if you want the LLM to adapt to a specific answer generation style. Let’s look at some of the best practices for LLM fine-tuning.

LLM fine-tuning best practices

The following are some best practices you should follow while fine-tuning LLMs.

• Define a clear objective: You should first define the task, expected output format, tone, and behavior of the fine-tuned model. For example, you should determine if the fine-tuned LLM will be used for classification, translation, summarization, or reasoning. Then, you should define the format and tone of the generated output.
• Select an appropriate model: To minimize training costs while fine-tuning, select an open-source model that aligns with your use case. For example, if you want to fine-tune a code generation model using your codebase, you should choose a model like CodeLlama or starcoder2. For classification or NLP tasks, you can use models like BERT, DistilBERT, and RoBERTa.
• Use high-quality and task-specific dataset: The fine-tuned model will be only as good as your dataset. Hence, ensure that the dataset used for LLM fine-tuning is structured correctly and free from biases or errors. The dataset should align with the defined objective and shouldn’t contain confusing, contradictory, or noisy examples.
• Use parameter-efficient fine-tuning (PEFT) techniques: Fine-tuning an LLM requires huge computational resources. Hence, you should use parameter-efficient LLM fine-tuning techniques like LoRA or prefix tuning to optimize computational resources and training time.
• Evaluate model performance after each epoch: After each epoch, you should evaluate the model performance on a validation dataset so that issues like overfitting can be identified and addressed promptly.
• Avoid catastrophic forgetting: The base LLM can lose the initial knowledge if the model weights are drastically changed during fine-tuning. Thus, you should use layer-freezing techniques to preserve the foundational knowledge. Combining the PEFT techniques and layer freezing can be the best choice to fine-tune LLM models while keeping the foundational knowledge intact.

Conclusion

Fine-tuning Large Language Models (LLMs) is a game-changer for customizing AI models to specific tasks like sentiment analysis, chatbots, and text summarization. Instead of training from scratch, fine-tuning adapts powerful pre-trained models for higher accuracy, better efficiency, and domain-specific performance while saving computational resources.

To learn more about using LLMs, you can go through this course on fine-tuning transformer models. You might also like this course on building your own LLM using PyTorch.

Frequently asked questions

1. What is the difference between RAG and LLM agents?

RAG only provides additional information to the LLM to generate accurate responses for a query. In contrast, LLM agents use different tools, memory, and planning capabilities to perform tasks without human assistance. For example, you can build an LLM agent to book flight tickets, but RAG only helps answer queries.

2. What is the difference between fine-tuning and LLM training?

Fine-tuning and LLM training are both used to train LLMs. However, their scope and goals are different. LLM training involves training a large language model from scratch on a large and diverse dataset to learn general language understanding. Fine-tuning uses a pre-trained LLM and trains it on a smaller, more specific dataset to specialize it for a particular task or domain.

3. What are the alternatives to RAG in LLM?

LLM fine-tuning, Knowledge-Augmented Generation (KAG), and Real-time Information Generation (RIG) are some of the alternatives to RAG in LLM.

• KAG integrates structured and factual information from knowledge graphs or databases into the LLM’s response process.

• RIG combines real-time data retrieval with text generation by iteratively querying external data sources and generating text based on the retrieved information.

4. Is RAG better than LLM?

RAG is better than using a standalone LLM when you need accurate, up-to-date, or domain-specific information. However, LLMs are sufficient for general tasks that do not require domain-specific or task-specific information.

5. What is the difference between fine-tuning and LoRA?

LoRA is a technique for fine-tuning large language models that is more efficient than traditional fine-tuning.