As part of our ongoing efforts to advance software solutions using AI, we recently undertook a proof of concept to fine-tune a Large Language Model (LLM) for improving customer support responses. Through our experience providing LLM fine-tuning services to our customers, we’ve come to appreciate the significant benefits that fine-tuning offers. Utilizing the Falcon LLM and Quantized Low-Rank Adapter (QLoRA) techniques, our objective was to evaluate the extent of improvement achievable in response accuracy and efficiency, all within the limitations of a single GPU. This project was an exploratory exercise aimed at pushing the boundaries of AI and LLM fine-tuning, rather than deploying a production-ready system.
Our Approach
Selecting the Falcon LLM
We chose the Falcon LLM for its flexibility and strong performance in handling a variety of queries. Its architecture provided a suitable platform for experimentation, allowing us to investigate its capabilities in generating enhanced customer support responses.
Data Curation
Recognizing the importance of high-quality data, we compiled and refined over 100,000 query-response pairs from previous customer interactions. This carefully curated dataset ensured relevance and accuracy, serving as a solid foundation for the fine-tuning process.
Implementing QLoRA
A key aspect of our proof of concept was the application of QLoRA. This technique enabled efficient fine-tuning of the large language model on a single GPU by combining low-rank approximation with quantization. QLoRA allowed us to retain essential model features while reducing the number of parameters, making the process feasible despite hardware constraints.
Optimizing Training with Hugging Face Transformers
We employed the Hugging Face Transformers library for the fine-tuning process. Through extensive testing and parameter adjustments, we optimized the training configuration. The 8-bit Paged Adam optimizer was instrumental in managing the model size and achieving optimal performance within our hardware limitations.
Challenges and Insights
Hardware Limitations
Operating within the memory constraints of a single GPU posed a significant challenge. By leveraging QLoRA’s quantization methods, we effectively reduced the model size without compromising performance, offering valuable insights into overcoming hardware limitations with advanced techniques.
Maintaining Contextual Accuracy
Ensuring the model’s responses remained contextually accurate was critical. We implemented continuous validation throughout the training to maintain the relevance and usefulness of the generated customer support responses.
Results
Although this was a proof of concept and not intended for production deployment, the improvements observed were noteworthy:
• Response Time: Decreased from an average of 6 seconds to 2.6 seconds.
• Accuracy: Improved from 85% to 96% in generating appropriate responses.
• Projected User Satisfaction: Indications suggest a substantial increase due to faster and more accurate responses.
This proof of concept has provided valuable insights into the potential of fine-tuning LLMs for customer support applications. By successfully adapting the Falcon LLM using QLoRA techniques on a single GPU, we demonstrated significant improvements in response time and accuracy. These findings will inform our future endeavors in AI and contribute to our goal of enhancing software solutions through innovative approaches.
For those interested in our work, the trained model from this project is available here: https://huggingface.co/azumo/falcon-7b-gsm8k
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