LLM-finetuning

🚀 Parameter Efficient Fine-Tuning (PEFT) Repository

This repository provides code and resources for Parameter Efficient Fine-Tuning (PEFT), a technique for improving fine-tuning efficiency in natural language processing tasks.

📖 Introduction
🎯 Traditional Fine-Tuning
🔧 Parameter Efficient Fine-Tuning Techniques
- 3.1. 💡 Knowledge Distillation
- 3.2. ✂️ Pruning
- 3.3. ⚖️ Quantization
- 3.4. 🧩 Low-Rank Factorization
- 3.5. 🧠 Knowledge Injection
- 3.6. 🛠️ Adapter Modules
🌟 Advantages of Parameter Efficient Fine-Tuning
💻 Code Sample: Efficient Fine-Tuning with PEFT
🔗 Further References
📝 Conclusion

📖 Introduction

This repository presents Parameter Efficient Fine-Tuning (PEFT), a technique designed to enhance the efficiency of fine-tuning in natural language processing (NLP) tasks. By leveraging various techniques, PEFT aims to reduce the computational requirements and memory footprint associated with traditional fine-tuning.

🎯 Traditional Fine-Tuning

Traditional fine-tuning in NLP involves training a pre-trained model on a task-specific dataset. While effective, it can be computationally expensive and resource-intensive. This section explores the concept of traditional fine-tuning and discusses its limitations.

🔧 Parameter Efficient Fine-Tuning Techniques

This section introduces several techniques used in Parameter Efficient Fine-Tuning:

3.1. 💡 Knowledge Distillation

Knowledge distillation involves transferring knowledge from a large pre-trained model (teacher) to a smaller model (student), making fine-tuning more efficient.

3.2. ✂️ Pruning

Pruning techniques focus on removing unnecessary weights or connections from a pre-trained model, reducing its size and improving fine-tuning efficiency.

3.3. ⚖️ Quantization

Quantization reduces the precision of numerical values in a model, typically from floating-point to fixed-point representation. It helps reduce the model size and memory requirements, improving fine-tuning efficiency.

3.4. 🧩 Low-Rank Factorization

Low-rank factorization approximates a high-dimensional weight matrix with low-rank matrices, reducing the number of parameters and computations needed during fine-tuning.

3.5. 🧠 Knowledge Injection

Knowledge injection involves incorporating additional information, such as linguistic or domain-specific knowledge, into the fine-tuning process. It guides the learning process, improving fine-tuning performance and efficiency.

3.6. 🛠️ Adapter Modules

Adapter modules are lightweight, task-specific modules that can be inserted into a pre-trained model without modifying its architecture. They enhance fine-tuning efficiency by enabling the reusability of pre-trained models across multiple tasks.

🌟 Advantages of Parameter Efficient Fine-Tuning

This section highlights the advantages of Parameter Efficient Fine-Tuning techniques. By leveraging these techniques, fine-tuning processes become more efficient, reducing computational requirements, memory footprint, and training time.

💻 Code Sample: Efficient Fine-Tuning with PEFT

This repository provides a code sample that demonstrates how to implement efficient fine-tuning using the PEFT techniques described above. The code sample serves as a starting point for incorporating PEFT into your NLP projects.

🔗 Further References

For additional resources and references on Parameter Efficient Fine-Tuning, refer to the following sources provided in this repository.

LinkedIn - Connect with me on LinkedIn
Medium Article - Read my Medium article on Parameter Efficient Fine-Tuning

📝 Conclusion

In conclusion, Parameter Efficient Fine-Tuning (PEFT) offers techniques to enhance the efficiency of fine-tuning in NLP tasks. By employing knowledge distillation, pruning, quantization, low-rank factorization, knowledge injection, and adapter modules, PEFT enables more efficient fine-tuning processes and improves overall performance in NLP applications.

Note: As update is in progress stay tune.

Abonia1/LLM-finetuning