---
title: "Fine-tuning (deep learning)"
source: "https://systems-analysis.info/eng/Fine-tuning_(deep_learning)"
wiki: "systems-analysis.info/eng"
article: "Fine-tuning_(deep_learning)"
language: "en"
categories:
  - "Category:English"
  - "Category:Large language models"
  - "Category:Machine learning"
  - "Category:Technology"
revision_id: 147
wiki_created_at: 2026-09-06T22:18:00Z
wiki_modified_at: 2026-09-06T22:18:00Z
downloaded_at: 2026-09-07T22:21:20Z
---

# Fine-tuning (deep learning)

**Fine-tuning** is a transfer learning technique in machine learning where the parameters of a pre-trained model are adapted to a new, specific task. Instead of training a model from scratch, which requires vast amounts of data and computational resources, fine-tuning leverages the knowledge already encoded in the model's weights and "tunes" them for a specific purpose.

This approach has become the de-facto standard in deep learning, especially when working with large language models (LLMs) and computer vision models.

## Concept

The fine-tuning process can be divided into two main stages:

1\. **Pre-training:** A model (e.g., BERT or GPT) is trained on a very large and general dataset (e.g., the entire internet) using a self-supervised task (e.g., predicting the next word). During this stage, the model learns general patterns, syntax, semantics, and world knowledge.

2\. **Fine-tuning:** The pre-trained model is taken as a starting point, and its weights are adjusted on a small, labeled dataset specific to the target task.

The key idea is that the knowledge acquired during pre-training is general-purpose and can be successfully transferred to solve many other, more specialized tasks.

## The Fine-Tuning Process

A typical fine-tuning process includes the following steps:

1\. **Select a Pre-trained Model:** A model is chosen whose foundational capabilities are suitable for the target task (e.g., BERT for text understanding tasks, GPT for text generation).

2\. **Adapt the Architecture:** A new "head" layer, specific to the target task, is added to the pre-trained model. For example:

- For **text classification**, a simple fully-connected layer with a softmax function is added.
- For **named entity recognition (NER)**, a classifier is added to the output of each token.

3\. **Train on the Target Dataset:** The entire model (or part of it) is trained on the new, labeled dataset. During this step, the model's weights, including those of the pre-trained layers, are updated via gradient descent to minimize the loss function on the new task. 4. **Use a Lower Learning Rate:** Fine-tuning typically uses a significantly lower learning rate than pre-training. This is necessary to avoid drastically altering the useful knowledge already encoded in the model's weights, and instead to adjust them carefully.

## Types of Fine-Tuning

### Full Fine-Tuning

- **Principle:** All parameters of the pre-trained model are updated along with the new "head" layer.
- **Advantages:** Potentially offers the best performance, as the entire model adapts to the new task.
- **Disadvantages:** Requires significant computational resources and memory, as gradients must be stored and updated for all parameters. There is a risk of **catastrophic forgetting**, where the model "forgets" the general knowledge acquired during pre-training.

### Parameter-Efficient Fine-Tuning (PEFT)

This is a family of methods aimed at reducing the computational costs of fine-tuning. The main idea is to freeze most of the pre-trained model's parameters and train only a small number of new or selected existing parameters.

- **Examples of PEFT methods:**
  - **Adapters:** Small, additional "adapter" layers are inserted into the Transformer architecture, and only these layers are trained.
  - **LoRA (Low-Rank Adaptation):** Instead of updating the full weight matrices, LoRA trains their low-rank updates. This can reduce the number of trainable parameters by thousands of times.
  - **Prompt Tuning:** Trainable "prompt" vectors are added to the input data and are tuned to solve the task, while the model itself remains frozen.

<!-- -->

- **Advantages of PEFT:**
  - **Efficiency:** Significantly reduces memory and computational requirements.
  - **Modularity:** Allows a single pre-trained model to be easily adapted for multiple tasks by storing only a small set of adapted weights for each.

## Instruction Tuning

This is a specific type of fine-tuning aimed at improving an LLM's ability to follow natural language instructions.

- **How it works:** The model is fine-tuned on a dataset consisting of "instruction-desired output" pairs.
- **Goal:** To improve the model's ability to generalize to new, unseen tasks that can be described as instructions. Models like InstructGPT and FLAN-T5 are prominent examples of this approach.

## External links

- <a href="https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)" class="external text" rel="nofollow">Fine-tuning (deep learning) — Wikipedia</a>

## See also

- [Large Language Models](https://systems-analysis.info/eng/Large_language_model "Large language model")
- [BERT](https://systems-analysis.info/eng/BERT_(language_model) "BERT (language model)")
- [GPT](https://systems-analysis.info/eng/GPT_(OpenAI) "GPT (OpenAI)")

## Bibliography

- Howard, J.; Ruder, S. (2018). *Universal Language Model Fine-tuning for Text Classification*. <a href="https://arxiv.org/abs/1801.06146" class="external text" rel="nofollow">arXiv:1801.06146</a>.
- Houlsby, N. et al. (2019). *Parameter-Efficient Transfer Learning for NLP*. <a href="https://arxiv.org/abs/1902.00751" class="external text" rel="nofollow">arXiv:1902.00751</a>.
- Pfeiffer, J. et al. (2020). *AdapterFusion: Non-Destructive Task Composition for Transfer Learning*. <a href="https://arxiv.org/abs/2005.00247" class="external text" rel="nofollow">arXiv:2005.00247</a>.
- Hu, E. J. et al. (2021). *LoRA: Low-Rank Adaptation of Large Language Models*. <a href="https://arxiv.org/abs/2106.09685" class="external text" rel="nofollow">arXiv:2106.09685</a>.
- Lester, B.; Al-Rfou, R.; Constant, N. (2021). *The Power of Scale for Parameter-Efficient Prompt Tuning*. <a href="https://arxiv.org/abs/2104.08691" class="external text" rel="nofollow">arXiv:2104.08691</a>.
- Ben Zaken, A.; Goldberg, Y.; Ravfogel, S. (2022). *BitFit: Simple Parameter-Efficient Fine-Tuning for Transformer-based Masked Language-Models*. <a href="https://aclanthology.org/2022.acl-short.1/" class="external text" rel="nofollow">ACL 2022</a>.
- Ouyang, L. et al. (2022). *Training Language Models to Follow Instructions with Human Feedback*. <a href="https://arxiv.org/abs/2203.02155" class="external text" rel="nofollow">arXiv:2203.02155</a>.
- Han, Z. et al. (2024). *Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey*. <a href="https://arxiv.org/abs/2403.14608" class="external text" rel="nofollow">arXiv:2403.14608</a>.
- Bian, J. et al. (2025). *A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning*. <a href="https://arxiv.org/abs/2504.21099" class="external text" rel="nofollow">arXiv:2504.21099</a>.
- Li, X. et al. (2025). *Revisiting Fine-Tuning: A Survey of Parameter-Efficient Techniques and Future Directions*. <a href="https://www.preprints.org/manuscript/202504.0743/v1" class="external text" rel="nofollow">Preprints.org</a>.