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Author :

Ampcome CEO
Mohamed Sarfraz Nawaz
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Mohamed Sarfraz Nawaz is the CEO and founder of Ampcome, which is at the forefront of Artificial Intelligence (AI) Development. Nawaz's passion for technology is matched by his commitment to creating solutions that drive real-world results. Under his leadership, Ampcome's team of talented engineers and developers craft innovative IT solutions that empower businesses to thrive in the ever-evolving technological landscape.Ampcome's success is a testament to Nawaz's dedication to excellence and his unwavering belief in the transformative power of technology.


Fine Tuning Large Language Models (LLMs) In 2024

Stay ahead of the curve in 2024 with our website on fine-tuning large language models (LLMs). Explore the latest techniques and advancements to optimize your language processing.
LLM Finetuning
Author :
Ampcome CEO
Mohamed Sarfraz Nawaz
Ampcome linkedIn.svg

Mohamed Sarfraz Nawaz is the CEO and founder of Ampcome, which is at the forefront of Artificial Intelligence (AI) Development. Nawaz's passion for technology is matched by his commitment to creating solutions that drive real-world results. Under his leadership, Ampcome's team of talented engineers and developers craft innovative IT solutions that empower businesses to thrive in the ever-evolving technological landscape.Ampcome's success is a testament to Nawaz's dedication to excellence and his unwavering belief in the transformative power of technology.


For the past one and a half years the tech world has been going crazy with LLMs. Large Language Models are the new heroes of the town who have given applications natural language skills that seemed impossible a few years ago.

From OpenAI GPT models to Llama 3, Palm, Gemini, Claude and Mistral, we have a variety of powerful LLMs at our disposal. These LLMs trained on massive datasets are proficient in a wide range of natural language processing tasks. These NLP tasks include text generation, translation, summarizations, and question and answer.

Thanks to these NLP skills businesses are putting LLMs to amazing use cases. Some of them are:

-     Customer support

-     Product enhancements

-     Process automation

-     Business Analytics

-     Sentiment analysis

-     Documentation & Reporting

…and more.

However, a pre-trained LLM does not generate business-relevant and context-specific accurate responses. Due to a lack of domain-specific and outdated data, these LLMs are prone to hallucinations.

Solution? – fine-tuning LLMs

Fine tuning an LLM improves the model’s efficiency ingenerating quality context-relevant responses or executing specialized tasks.

LLM finetuning:

-     Improves accuracy & performance

-     Gives domain-specific expertise

-     Reduce data requirements

-     Results in faster training & deployment

-     Efficient use of resources

In this blog, we will explore how you can fine tune LLM. We will also talk about common methods and approaches you can use to fine tune LLM as per your business needs.


What is LLM Finetuning?

Fine tuning LLM is the process of training pre-trained models on domain or business-specific datasets to refine its capabilities. This process improves the performance of the LLMs, avoids hallucinations and transforms them into specialized agents capable of executing business-specific tasks.

The core purpose of fine-tuning LLM is to align a general language model to the specific requirements of certain applications. This ensures that the language model caters to the unique requirements, executes tasks and produces desired results.

For example, a legal organization wants to use GPT-3 to draft case summaries, court orders, affidavits and more. Now a pre-trainedGPT-3 model is not well-versed in legal laws and terms. Without fine-tuning, there is a high possibility for the model to make errors.

But when the organization fine tunes GPT-3 on legal datasets, the model becomes more familiar with legal terms. Now it can assist the legal team in drafting accurate, precise and well-formatted court summaries, cases, notices and orders.

So, finetuning LLM is more like refining the knowledge of the model to make it a subject matter expert in a particular domain.

Why Fine Tune LLM?

LLMs are great but when you apply them to your business use cases, it's highly possible for them to hallucinate.

Hallucinations are instances where LLMs produce outdated, factually wrong or absurd responses. It happens because LLMs don’t have knowledge of domain-specific and updated data.

Fine tuning LLM to domain-specific datasets bridges the gap between a general language model and a specialized one. Exposing LLM to task-specific examples during finetuning enables the model to gain the required expertise in the intended domain. Therefore turning them into hyper-focused models capable of executing the intended task with high accuracy and efficiency.

Here's why fine-tuning large language models (LLMs) is crucial:

·       Boosts Accuracy for Specific Tasks: LLMs excel at general understanding, but for specific tasks like writing legal documents or summarizing medical reports, they need more focus. Fine-tuning targeted datasets hones their ability in those areas, leading to more accurate and relevant outputs.

·       Tailored Interactions:  Imagine a customer service chatbot. Fine-tuning with customer interaction data ensures the chatbot aligns with your brand voice and provides consistent, high-quality experiences.

·       Domain Expertise: LLMs trained on general data might not understand industry-specific nuances. Fine-tuning domain-specific data like financial reports or legal documents equips them with specialized knowledge for superior performance in those fields.

·       Data Efficiency:  Large, labelled datasets for specific tasks can be expensive and time-consuming to create. Fine-tuning leverages a pre-trained LLM's foundation, allowing it to learn from a smaller, targeted dataset and still achieve significant improvement in task-specific accuracy.

·       Reduced Computational Cost: Training LLMs from scratch requires immense computational resources. Fine-tuning utilizes a pre-trained model, significantly reducing the training time and power needed to create a task-specific LLM.

In essence, fine-tuning unlocks the true potential of LLMs by transforming them from general-purpose tools into highly effective solutions for specific needs.

LLM Finetuning Approaches

Fine-tuning LLM means adjusting its parameters and giving it more knowledge. Now the scale of adjustment and level of knowledge you want to expose your model to depends on your requirements. It all depends on the specific tasks that you want the model to perform, the size of the data set and  the level of adaptation.

Coming back to LLM finetuning approaches, there are two –Feature extraction and Full finetuning.

Feature Extraction (Repurposing)

Feature extraction also known as repurposing is the cost-effective way to leverage the existing features of the model to execute specific tasks.

In this method, we treat the LLM as a feature extractor. LLMs being trained on massive datasets have significant knowledge and are well adapted to several language features.

What we do in feature extraction is keep the LLM weights frozen. We only use the final layers of the model for training. These final layers, then, learn to interpret the pre-trained features in the context of the specific task.

This approach is effective because it leverages the knowledge of the pre-trained model and uses it to execute specific tasks. Therefore, it takes less time to fine tune LLM, lowering the training cost and computational resources.

Full Finetuning

Full fine tuning is the primary approach to fine tune LLM on domain datasets. In this approach, we work with all the layers of the model and train them on specific datasets to be able to execute intended tasks accurately.

Unlike feature extraction, this approach uses all the layers of the model for parameter adjustment and training. It’s a large scale finetuning approach that’s advisable only when your task dataset is large and different from what the pre-trained model has been trained on.

Full finetuning LLM requires more time, cost and computational resources as compared to feature extraction. However, the biggest advantage of full finetuning is the superior performance of the model.

However, there's a risk of "catastrophic forgetting" where the model forgets what it learned during pre-training as it focuses on the new task.

Different Types of Fine-tuning LLMs

There are several methods to tailor Large Language Models(LLMs) to specific tasks. Broadly, we have two prominent methods to fine tune LLMs – Supervised Finetuning and RLHF (Reinforcement learning from human feedback).

Let’s discuss each of them in detail.

Supervised Finetuning

Supervised finetuning is a technique used to adapt apre-trained large language model (LLM) to a specific task by leveraging labelled data. Imagine you have a powerful machine that can understand language generally, but you want it to excel at writing different kinds of creative content. Supervised finetuning equips it with that specific skill.

Here's a breakdown of how it works:

·       The Foundation: Pre-trained LLM: We start with a pre-trained LLM. This model has already been trained on a massive dataset of text and code, allowing it to understand the general nuances of language.

·       Labelled Data for the Task:  We then prepare a dataset specifically designed for the target task. This data consists of examples where the in put and the desired output (label) are provided. For creative writing, this could be a dataset of story prompts paired with complete stories written in different genres.

·       Fine-tuning the Model:  The pre-trained LLM is then fine-tuned on this labelled data. This involves adjusting the weights of the LLM's internal parameters based on how well it performs on the task. It's like tweaking the dials on the machine to make it better at a specific job.

Benefits of Supervised Fine-Tuning:

·      Improved Task Performance: By focusing on labelled data for the specific task, the LLM becomes more accurate and relevant in its outputs.

·       Leverages-trained Knowledge: There-trained LLM provides a strong foundation, allowing the model to learn from a smaller amount of task-specific data compared to training from scratch.

·       EfficientApproach: Supervised finetuning is a well-established technique with readily available tools and libraries, making it a practical choice for many tasks.

In essence, supervised finetuning LLM takes a powerful general-purpose LLM and tailors it into a highly effective tool for your specific creative writing needs.

Reinforcement Learning From Human Feedback (RLHF)

Reinforcement Learning from Human Feedback (RLHF) is a technique for training machine learning models, particularly large language models (LLMs), by incorporating human feedback into the learning process.

It's like having a human trainer provide rewards or penalties to the model, guiding it towards desired behaviours.

Here's a break down of how it works:

The Process:

·      Reward Model Training:

This initial stage involves creating a system, called a reward model or preference model, that can interpret human feedback and translate it into numerical rewards or penalties for the LLM.

Imagine training a human judge to evaluate different creative text formats generated by the LLM. The judge would provide feedback (positive or negative) on each format.

·      Interaction and Learning:

The LLM interacts with the environment (e.g., generates creative text) and receives feedback from the reward model based on the human's prior evaluation.

This feedback loop allows the LLM to learn what kind of outputs are considered desirable by humans.

·       Policy Update:

Based on the rewards or penalties, the LLM's internal policy, which dictates its behaviour(text generation in this case), is updated.

Over time, the LLM learns to prioritize actions that lead to higher rewards (more human-preferred outputs).

Subcategories of RLHF:

·       Reward Function Design: This in volves defining a clear system for assigning rewards or penalties based on human preferences. It's like establishing judging criteria for different creative writing styles.

·       Human-in-the-Loop Training: Here, humans directly interact with the LLM, providing feedback on its outputs. Imagine humans reading and rating different story openings generated by the LLM.

·       Preference Learning: The LLM learns from human preferences between different outputs. It's like showing the LLM various writing samples and having humans indicate which ones they prefer.

Benefits of RLHF:

·       Effective for Complex Tasks: For tasks with subjective or nuanced goals (like creative writing styles), human feedback can be invaluable in guiding the LLM towards desired outcomes.

·       Flexibility: RLHF can be adapted to various tasks by adjusting the reward function and human feedback mechanisms.

·       Human-Centered Approach: This technique explicitly incorporates human preferences in the training process, potentially leading to models that better align with human expectations.

Challenges of RLHF:

·       Cost and Time: Human feed back can be expensive and time-consuming to obtain, especially for large-scale training.

·       Subjectivity: Human preferences can be subjective and vary between individuals. This requires careful design of the reward function and feedback mechanisms.

·       Interpretability: Understanding why the LLM generates specific outputs based on human feedback can be challenging.

Overall, RLHF offers a valuable approach for training machine learning models, particularly LLMs, where human judgment plays a crucial role in defining success.

Types of Supervised Finetuning LLM Techniques

There are five common supervised finetuning LLM techniques.

Basic Hyperparameter Tuning:

Imagine you're baking a cake. The recipe (model architecture) is set, but the success depends on getting things like temperature (learning rate) and baking time (number of training epochs) just right.

These are hyperparameters - settings that control the training process of a machine learning model but aren't directly learned from the data.

Basic hyperparameter tuning involves trying different combinations of these settings to find the configuration that yields the best performance on your specific task.

It's like testing different temperatures and baking times to see which results in the fluffiest cake. Here are some common techniques:

·       Grid Search: This method systematically evaluates a predefined set of hyperparameter values and chooses the combination that leads to the best outcome.

·       Random Search: This approach randomly samples different hyperparameter combinations and selects the one that performs best. It can be more efficient than a grid search for large search spaces.

Transfer Learning:

This technique leverages knowledge gained from one task to improve performance on a related but different task. Imagine training a chef on making cakes (source task) and then having them use that knowledge to learn how to bake muffins (target task).

The chef (model) already understands the basics of baking(general features), which helps them learn the specifics of muffins(task-specific features) faster.

In machine learning, a pre-trained model on a large dataset(source task) is used as a starting point for a new task (target task). This pre-trained model's weights (learned knowledge) are either partially or entirely reused and fine-tuned on the new data set, often requiring less data and training time compared to training from scratch.

Multi-task Learning:

This approach trains a single model on multiple related tasks simultaneously.

Imagine training a chef to make cakes, muffins, and pies(multiple tasks) at the same time. While each recipe (task) has unique aspects, they likely share some underlying culinary concepts (shared features).

In multi-task learning, the model learns a shared representation for the tasks, capturing the common features, while also developing specific functionalities for each individual task.

This can be beneficial when dealing with limited data foreach task, as the model can leverage knowledge transfer between tasks.

Few-shot Learning:

This technique tackles situations where only a very small amount of labelled data is available for the target task.

Imagine having just a few examples (shots) of each type of exotic pastry (task) and needing the chef to learn how to make them effectively.

Few-shot learning algorithms are designed to learn from these limited examples. They often involve techniques like metric learning(learning similarity measures) and meta-learning (learning how to learn quickly from a few examples).

Task-specific Fine-tuning:

This is a specialized form of transfer learning where apre-trained model is fine-tuned for a specific task. It's like taking the pre-trained chef (model) with general baking knowledge and further training them on a specific pastry recipe (target task).

Here, the pre-trained model's weights are adjusted based on the new task's data. This leverages the pre-trained knowledge as a foundation while allowing the model to specialize in the nuances of the specific task.

Task-specific fine-tuning is a common approach for adapting large language models (LLMs) to generate different creative text formats or perform specific NLP tasks.

How To Fine-tune LLM?

Here's a roadmap for fine-tuning large language model (LLM):

1.Define the Task and Dataset:

·       Task: Clearly identify the specific task you want the LLM to excel at. Is it writing different creative content formats, translating languages, or summarizing factual topics?

·       Dataset: Prepare a high-quality dataset relevant to your task. This data should consist of labelled examples where the input and the desired output (label) are provided. The size and quality of the data set significantly impact the fine-tuned model's performance.

2.Choose a Pre-trained LLM:

·       Selecta pre-trained LLM that aligns with your task and data. Popular options includeGPT-3, Jurassic-1 Jumbo, or T5. Consider factors like the LLM's size, training data, and capabilities relevant to your task.

3.Select a Fine-Tuning Approach:

There are two main approaches:

·       Supervised Fine-Tuning: This is efficient when you have a large amount of labelled data. The pre-trained LLM is adjusted based on the labelled data for your specific task. Subcategories include classification (e.g., genre classification for creative writing) and regression (e.g., predicting creativity scores).

·       Reinforcement Learning from Human Feedback (RLHF): This is useful for complex tasks where human judgment is crucial. Humans provide feedback on the LLM's outputs, shaping its behavior over time. Subcategories include reward function design, human-in-the-loop training, and preference learning.

4.Prepare the Training Environment:

·       Choose a suitable hardware platform with sufficient processing power and memory to handle the training process. Popular options include cloud-based platforms or specialized AI hardware.

·       Selecta deep learning framework like TensorFlow or PyTorch that provides tools and libraries for working with LLMs.

5.Implement the Fine-Tuning Process:

·       Data Preprocessing: Clean and format your dataset to ensure the LLM can understand and process it effectively.

·       Fine-Tuning Architecture: Decide on the fine-tuning architecture (feature extraction or full fine-tuning) based on your task and data availability.

·       Training: Train the LLM using the chosen fine-tuning approach and monitor its performance using relevant metrics (e.g., accuracy for classification).

·       Evaluation: Evaluate the fine-tuned model's performance on a separate validation set to assess its generalizability and avoid overfitting.

6.Hyperparameter Tuning (Optional):

·       Experiment with different hyperparameter settings (learning rate, batch size) to optimize the training process and potentially improve the model's performance.

Additional Tips:

·       Consider using transfer learning if a large dataset for your specific task is unavailable. Fine-tune the LLM on a related task with abundant data first, then further fine-tune it on your specific task with limited data.

·       Leverage available tools and libraries within your chosen deep learning framework to streamline the fine-tuning process.

By following these steps and considering the different approaches and techniques, you can effectively fine-tune an LLM to excel at your specific task.

Remember, the success of fine-tuning depends on the quality of your data, the chosen LLM, and the training process optimization.

Current Best Practices For Fine Tuning LLMs From Scratch

Here are some best practices to keep in mind when fine-tuning large language models (LLMs):

1.Data is King:

·       Quality over Quantity: Focus on acquiring high-quality data that are well-labeled, relevant to your specific task, and reflect the real-world use case. A smaller dataset of clean, accurate data can outperform a larger dataset with noise or inconsistencies.

·       Data Augmentation: If LaBelle data is limited, consider techniques like back-translation (machine translation and then back again) or paraphrasing to artificially expand your dataset.

2.Choosing the Right Tools:

·       Pre-trained LLM Selection: Pick apre-trained LLM that aligns with your task and data. Consider the LLM's size, training data, and capabilities relevant to your specific domain (e.g., factual language for summarization vs. creative text for story writing).

·       Deep Learning Framework: Utilize established deep learning frameworks like TensorFlow or PyTorch that offer tools and libraries specifically designed for working with LLMs. These frameworks can streamline the fine-tuning process.

3.Fine-Tuning Approach:

·       Start Simple: If you have a large amount of labeled data, supervised fine-tuning is a well-established and efficient approach. For complex tasks with subjective goals or limited data, consider RLHF to incorporate human feedback into the training process.

·       Feature Extraction vs. Full Fine-Tuning: If your task is related to the pre-trained LLM's data, feature extraction (freezing initial layers) can be a good starting point. It leverages the LLM's existing knowledge and requires less fine-tuning data. Fullfine-tuning offers more flexibility but might require a larger dataset and carries the risk of "catastrophic forgetting."

4.Training Efficiency:

·       Hyperparameter Tuning: Experiment with different hyperparameters (learning rate, batch size) to optimize the training process. This can significantly impact the fine-tuned model's performance. Tools like grid search or random search can help you efficiently explore different hyperparameter combinations.

·       TransferLearning: If a large dataset for your specific task is unavailable, leverage transfer learning. Fine-tune the LLM on a related task with abundant data first, then further fine-tune it on your target task with limited data.

5.Monitoring and Evaluation:

·       ValidationSet: Always use a separate validation set unseen by the model during training to assess its generalizability and prevent overfitting. Overfitting occurs when the model becomes too focused on the training data and performs poorly on unseen data.

·       Task-Specific Metrics: Evaluate the model's performance using metrics relevant to your task. For classification tasks, this could be accuracy, precision, recall, or F1 score. For regression tasks, mean squared error (MSE) or R-squared are common metrics.

Additional Tips:

·       Early Stopping: Implement early stopping to prevent the model from overfitting. This technique stops the training process when the model's performance on the validation set starts to decline.

·       Version Control: Maintain good version control practices to track changes made during the fine-tuning process. This allows you to easily revert to previous configurations if needed.

·       Ethical Considerations: When using RLHF, ensure the reward function design and human feedback mechanisms are unbiased and fair. Be mindful of potential biases in the training data and address them if necessary.

By following these best practices, you can effectively fine-tune LLMs to achieve optimal performance for your specific needs. Remember, fine-tuning is an iterative process, so be prepared to experiment and adjust your approach based on there sults you observe.


Fine-tuning LLMs has become the favorite approach for businesses looking to integrate LLMs into their operations and products. Inmost cases finetuning LLM offers businesses a cost-effective way to streamline processes and enrich customer experience for quantifiable outcomes.

But finetuning LLMs and integrating them into your existing system is work for experts. It requires extensive knowledge of data science, machine learning algorithms and AI techniques.

Ampcome, a leading AI development company have the skillset to seamlessly fine tune LLMs and integrate them into your business systems. Our expert engineers can tailor powerful large language models (LLMs) to your specific needs. We understand the intricacies offline-tuning these models to revolutionize your business operations.

Book a call with our LLM specialists today and discover how we can transform your business with the power of language

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