Guide To Fine Tune LLM Models Based On Use Cases

LLMs (language models) fine-tuning has become a critical element in the natural language process (NLP), thus empowering tailored implementations within different areas. The pre-trained models like GPT-2 or GPT-3 that are largely used for their general-purpose ability most of the time have to be customized to meet the specialized needs. Adjusting the actual LLMs to the specific tasks is achieved through particular data involving the specific domain exposure, refining the output accuracy, and better understanding.
This guide takes a de-mystifying approach towards fine-tuning, emphasizing the significance and actionable steps involved in an effective fine-tuning process. It explains rather how narrowing the field makes systems comprehend artifacts like legal document analysis, chatbots, and medical text comprehension a bit more efficient.
Fine-tuning helps to utilize the large-scale pre-trained models, made for a specific optimization and also saves time and computational costs. This keeps the domain-specific model linguistically rich and fluid as opposed to migrating to the pre-trained model which may have removed some subtle linguistic characteristics.
Furthermore, fine-tuning provides several main advantages other than merely adapting to the specialized domain. Thus, it gives an ability to acquire of those previously prepared large-scale models, which are extremely accurate and added valuable generalization figurines, but which a practitioner can change in regard to a task in hand as well. This not only becomes time saving and requires less computational resources but also retains the core linguistic richness and unstructured nature of the language model that were introduced by pre-training over a massive corpus.

What is Fine tuning?

Fine-tuning is the process of taking a pre-trained model and further training it on a domain specific dataset. Most LLM models today have a very good global performance but fail in specific task-oriented problems.

How does it work?

1. Pre-trained Model Selection

Choose a pre-trained model that is suitable for your task and domain. Pre-trained models are often trained on large-scale datasets, such as ImageNet for image classification or Wikipedia for natural language understanding.

Tips to find the right pre-trained model
Here are the tips that can help you to find the right pre-trained models:
  • Model Size: To specify enough parameters, use counted parameters. Likewise, they are more involved, but that is due to the fact that they represent more intricate patterns more easily.

  • Available Checkpoints: Look for well-known sources that will provide outlines that can be used for retraining models. The recommendation is authenticity control points or community-published official versions.

  • Domain and Language: See to it that the text language you want to translate with is the same as the pre-trained model’s language. An important way of getting better performance is through the use of similar language or domain; for jobs with domain-specific vocabulary, this becomes very useful.

  • Pre-training Datasets: See to it that the text language you want to translate is the same as the pre-trained model’s language. An important way of getting better performance is through the use of similar language or domain; for jobs with domain-specific vocabulary, this becomes very useful.

  • Transfer Learning Capability: Assess the model for its ability to perform in this transfer learning. Some of the models have the ability to adjust for a series of assignments, while others work perfectly on a certain type of problem.

  • Resource Constraints: The computational power required depends on the task which has to be performed. Higher models rely on an increased amount of memory and computational capability; the fine-tuning and the inference perform including its processing part.

  • Documentation on Fine-Tuning: Apart from that, you need to pick up more special models that are comprehensive in this genre of instructions or tutorials ready for your kind of work. Features with comprehensive instructions are more useful for additional modification.

  • Bias Awareness: It is essential to take a look at existing pre-trained models for any possible bias inherent in them. Select models that went through extensive tests and check for fairness and bias when you have scientific predictions, if your task requires unbiased observations.

  • Evaluation Metrics: The selection of which system among ROUGE and RIU may be useful in cases when APIs (application programming interfaces) are required for example language creation and accuracy are very important when the task is characterization.

2. Model Adaptation
Modify the pre-trained model to suit your specific task or domain. This may involve changing the output layer of the model to match the number of classes in your dataset or adjusting other components of the model architecture.
3. Dataset Preparation
Prepare your dataset for fine-tuning. This involves splitting your data into training, validation, and test sets, as well as pre-processing the data (e.g., normalization, data augmentation) as needed.
4. Fine-tuning Process:
  • Initialize the model parameters with the pre-trained weights.

  • Optionally freeze some layers of the model to prevent them from being updated during training, especially if the pre-trained model is already well-suited to your task.

  • Train the model on your domain-specific dataset using techniques such as gradient descent optimization.

  • Monitor the model’s performance on the validation set and adjust hyperparameters (e.g., learning rate) as needed to prevent overfitting.

  • Evaluate the fine-tuned model on the test set to assess its performance.

5. Evaluation
Evaluate the fine-tuned model’s performance on unseen data to determine its effectiveness for your task. This may involve metrics such as accuracy, precision, recall, or F1 score, depending on the specific task.
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Types of LLM models based on specific tasks:

Computer Vision
  • Image classification: Fine-Tuned Vision Transformer(ViT), Vision Transformer (base-sized model), ResNet-50 v1.5, SegFormer (b5-sized)
  • Text-to-Image: SDXL-Lightning, SD-XL 1.0-base Model Card, Stable Diffusion v1-5, IP-Adapter-FaceI
  • Text Classification: FinBERT, RoBERTa, Reranker

  • Text Summarization: BART (large-sized model), medical_summarization, Fine-Tuned T5 Small, mT5-multilingual-XLSum

  • Text Generation: Llama-2, alpindale/Mistral-7B, bitnet_b1, dbrx-bas

Different approach to fine tuning model

PEFT(Prompt-based Encoder Fine-Tuning):
The PEFT (Prompt-based Encoder Fine-Tuning) approach is a method used for fine-tuning large language models (LLMs) such as GPT (Generative Pre-trained Transformer) for specific downstream tasks or domains. PEFT involves fine-tuning the LLM by providing prompts or examples relevant to the target task, which allows the model to adapt its parameters to perform well on that task.
Here’s an overview of the PEFT approach for fine-tuning LLMs:
  1. Select Pre-trained LLM: Choose a pre-trained LLM that serves as the starting point for fine-tuning. This could be a model like GPT-3, GPT-2, or BERT, depending on the specific requirements of your task.
  2. Define Task-Specific Prompts: Design prompts or examples that are relevant to your downstream task. These prompts should provide the LLM with the necessary context to understand the task and generate appropriate responses.
  3. Fine-tuning Process:
    • Prompt-based Fine-tuning: Fine-tune the LLM using prompt-based training examples. During fine-tuning, the LLM is presented with prompt-text pairs, where the prompt provides context for the task, and the corresponding text represents the desired output or response.
    • Optimization: Use standard optimization techniques such as gradient descent to update the parameters of the LLM based on the prompt-text pairs. The objective is to minimize a task-specific loss function, which measures the discrepancy between the model’s predictions and the desired outputs.
    • Iterative Training: Fine-tune the LLM iteratively using batches of prompt-text pairs. Monitor the model’s performance on a validation set and adjust hyperparameters as needed to improve performance.
PEFT has 2 different approaches to fine-tuning as follows:
LoRA (Large-scale Pre-training through Randomized Attention) and QLoRA (Quantized Large-scale Pre-training through Randomized Attention) are approaches proposed for efficiently training large-scale language models (LLMs) such as GPT (Generative Pre-trained Transformer) on massive datasets.
  1. LoRA:
    • Randomized Attention: LoRA introduces a randomized attention mechanism that allows for more efficient training of large-scale LLMs. Instead of computing attention scores for all pairs of tokens in the input sequence, LoRA uses a randomized sampling strategy to select a subset of tokens for attention computation. This reduces the computational complexity of the attention mechanism and speeds up training.
    • Training Efficiency: By leveraging randomized attention, LoRA enables more efficient training of LLMs on large datasets. This can lead to faster convergence and lower computational costs compared to traditional attention mechanisms.
  2. QLoRA:
    • Quantized Randomized Attention: QLoRA extends the idea of randomized attention by introducing quantization techniques to further reduce the computational complexity of attention computations.
    • Quantization: QLoRA quantizes the attention scores into a smaller number of discrete values, which reduces the precision of attention computations. This allows for even faster training of LLMs while maintaining competitive performance.
    • Efficient Inference: The quantization of attention scores in QLoRA also leads to more efficient inference, as the reduced precision of attention computations requires fewer computational resources during model deployment.
Both LoRA and QLoRA aim to address the scalability challenges associated with training large-scale LLMs on massive datasets. By introducing randomized attention mechanisms and quantization techniques, these approaches enable more efficient training and inference, making it possible to train state-of-the-art LLMs on even larger datasets with reduced computational resources.
The following table summarizes our recommendations for tuning LLMs by PEFT or QLoRA:
Specification Recommended Details
GPU memory efficiency QLoRA QLoRA has about 75% smaller peak GPU memory usage compared to LoRA.
Speed LoRA LoRA is about 66% faster than QLoRA in terms of tuning speed.
Cost efficiency LoRA While both methods are relatively inexpensive, LoRA is up to 40% less expensive than QLoRA.
Higher max sequence length QLoRA Higher max sequence length increases GPU memory consumption. QLoRA uses less GPU memory so it can support higher max sequence lengths.
Accuracy improvemen Same Both methods offer similar accuracy improvements.
Higher batch size QLoRA QLoRA supports much higher batch sizes. For example, the following are batch size recommendations for tuning openLLaMA-7B on the following GPUs:
  • 1 x A100 40G:
    • LoRA: A Batch size of 2 is recommended.
    • QLoRA: A Batch size of 24 is recommended.
  • 1 x L4:
    • LoRA: A Batch size of 1 fails with an out-of-memory error (OOM).
    • QLoRA: A Batch size of 12 is recommended.
  • 1 x V100:
    • LoRA: A Batch size of 1 fails with an out-of-memory error (OOM).
    • QLoRA: A Batch size of 8 is recommended.

The “auto-train” approach for fine-tuning large language models (LLMs) refers to the process of automatically selecting hyperparameters and training configurations to optimize the performance of the model on a specific task or dataset. This approach often involves techniques such as hyperparameter optimization (HPO) and automated machine learning (AutoML) to search through a large space of possible configurations and identify the best settings for fine-tuning the LLM.

Here’s an overview of the auto-train approach for fine-tuning LLMs:
  1. Define Objective: Specify the task or objective for fine-tuning the LLM. This could include tasks such as text generation, sentiment analysis, question answering, or any other natural language processing (NLP) task.
  2. Hyperparameter Optimization (HPO):
    • Search Space: Define the hyperparameters and training configurations to be optimized during fine-tuning. This may include parameters such as learning rate, batch size, number of training epochs, dropout rate, and model architecture settings.
    • Optimization Algorithm: Choose an optimization algorithm to search through the hyperparameter space. Common approaches include random search, grid search, Bayesian optimization, and evolutionary algorithms.
    • Objective Function: Define an objective function that quantifies the performance of the fine-tuned LLM on a validation set. This could be a metric such as accuracy, perplexity, or any other task-specific evaluation measure.
  3. Automated Machine Learning (AutoML):
    • Automated Pipeline Construction: Use AutoML techniques to automatically construct and evaluate different fine-tuning pipelines. These pipelines may involve data preprocessing, feature engineering, model selection, hyperparameter tuning, and evaluation.
    • Search Strategy: Define a search strategy to explore the space of possible fine-tuning pipelines efficiently. This could involve strategies such as sequential model-based optimization (SMBO), genetic algorithms, or neural architecture search (NAS).
    • Scalability and Resource Management: Consider the scalability and resource requirements of the AutoML process, especially when fine-tuning LLMs on large datasets or with complex search spaces.
  4. Training and Evaluation:
    • Training: Train the LLM using the selected hyperparameters and training configurations. This may involve multiple iterations of training with different settings, depending on the optimization algorithm used.
    • Evaluation: Evaluate the performance of the fine-tuned LLM on a held-out validation set using the objective function defined earlier. This allows for the comparison of different fine-tuning configurations and the selection of the best-performing model.

Overview of Fine tuning LLM:

As the power of large language models (LLMs) explodes, so does the need for fine-tuning methods that are both efficient and accessible. Two crucial approaches have emerged: Parameter-Efficient Fine-Tuning (PEFT) and Hugging Face AutoTrain Advanced. While both address fine-tuning challenges, they cater to distinct needs and preferences. This in-depth document delves into their differences, empowering data scientists to make informed choices when adapting LLMs to specific tasks.
    1. Understanding the Bottleneck: Traditional fine-tuning of LLMs involves updating all of their parameters. This approach suffers from several drawbacks:
      • High computational cost: Training demands extensive resources to manipulate numerous parameters.
      • Overfitting susceptibility: Large LLMs easily overfit on limited downstream data, hindering generalization.
      • Large model footprint: The fine-tuned model retains its hefty size, posing deployment challenges.
    2. PEFT: Efficiency Through Parameter ReductionPEFT tackles these concerns head-on by modifying only a subset of the LLM’s parameters. It achieves this through:
      • Low-rank matrices: Instead of directly updating all parameters, PEFT introduces low-rank matrices that modulate the outputs of hidden layers. These matrices have significantly fewer parameters, dramatically reducing computational expenses.
      • Targeted knowledge update: PEFT focuses on updating the parts of the LLM most relevant to the target task, leading to better generalization and less overfitting.
      • Compact model footprint: The final model incorporates only the updated low-rank matrices, resulting in a much smaller and more deployable size.

      Benefits of PEFT:

      • Faster and cheaper training: Reduced parameter count significantly slashes training time and resource requirements.
      • Improved generalization: Focus on task-specific information can lead to better performance on unseen data.
      • Smaller model size: More efficient deployment and sharing due to a reduced footprint.
    3. AutoTrain Advanced: Simplifying the JourneyWhile PEFT focuses on parameter efficiency, AutoTrain Advanced takes a different approach, aiming to simplify the entire fine-tuning process:
          • Automated workflow: AutoTrain Advanced automates tedious tasks like hyperparameter tuning, data preprocessing, and training setup, allowing you to focus on your task.
          • Flexibility: It offers both PEFT and standard fine-tuning, catering to your specific needs and preferences.
          • Advanced features: Beyond automation, AutoTrain Advanced provides.
          • Experiment tracking: Monitor and compare different fine-tuning runs for insightful analysis.
          • Advanced hyperparameter tuning: Fine-tune your training regime further through powerful optimization tools.
          • Integration with platforms: Leverage cloud platforms like Weights & Biases for seamless collaboration and model management.

      Benefits of AutoTrain Advanced:

        • Simple and accessible: Suitable for users of all levels wanting a streamlined and less technical fine-tuning experience.
        • Control and experimentation: Offers PEFT alongside standard fine-tuning, catering to advanced users seeking nuanced control.
        • Rich tooling: Experiment tracking and advanced hyperparameter tuning functionalities empower deeper analysis and optimization.
    4. Choosing the Champion:A Matter of PrioritiesThe optimal choice between PEFT and AutoTrain Advanced depends on your specific goals and skillset:
      • For faster training, improved generalization, and smaller models, PEFT is the champion, especially when computational resources are limited.
      • If ease of use, flexibility, and a streamlined workflow are your priorities, AutoTrain Advanced shines, allowing you to fine-tune with minimal technical overhead.
      • For advanced users seeking control, experimentation, and rich tooling, AutoTrain Advanced reigns supreme, offering both PEFT and standard fine-tuning alongside powerful analysis and optimization features.


In conclusion, fine-tuning the Large Language Models (LLMs) is a powerful approach that leads to lesser error rates while challenging the models with task-oriented or domain-focused contexts. Here, there is a concerted selection of the pre-trained models, and then the model is adapted to the specific requirements by iterative training using domain-specific datasets. Technologies such as PEFT and AutoTrain High-performance are two of the most appropriate methods for addressing challenges like computational costs and model overfitting. Through this fashion, professionals will accelerate the interactable algorithm, compelling the NLP generalization in NLP, at the same time reducing the model footprint, consequently magnifying the NLP capabilities in a number of fields.
The contrast between PEFT Advance and AutoTrain Advance depends on the prioritization of personal goals. PEFT is one of the more resource-efficiency models, thereby reducing the computational demands and the need for large model sizes, which perfectly suits resource-constrained settings. In contrast to this, AutoTrain Advanced approaches the fine-tuning with some degree of automation and offers options to allow the less skilled user to easily set the parameters. These techniques let the data scientists customize LLMs so that they attain the required output which enables them to harness the potential of natural language processing into innovative and accessible learning technologies.
Vikas Agarwal is the Founder of GrowExx, a Digital Product Development Company specializing in Product Engineering, Data Engineering, Business Intelligence, Web and Mobile Applications. His expertise lies in Technology Innovation, Product Management, Building & nurturing strong and self-managed high-performing Agile teams.

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