Industry
Artificial Intelligence, Research & Development
Improve training data to boost LLM performance
By fine-tuning this Mistral 7 Billion on the VIGO dataset from Hugging Face, we tailor it to excel in understanding and generating dialogue. This process, however, is not without its challenges. The sheer computational demand of fine-tuning such a substantial model typically necessitates powerful and often expensive hardware. Our approach circumvents these barriers by employing innovative techniques like bits and bytes quantization and parameter-efficient fine-tuning. These strategies not only make it feasible to refine Mistral 7B on a Google Colab T4 GPU with just 16 GB of memory but also preserve the model's efficacy.
usecases
Optimizing the Finetuning Process
Call Quality and Agent Performance Evaluation
The Call Analysis accelerator provides a systematic approach to evaluating customer support interactions. By analyzing calls for positive responses, major issues, and sentiment, businesses can gain insights into individual agent performance, identifying strengths and areas for improvement.
FEATURES
Leverage Call Analysis in your Business:
Automated Call Summaries
Instantly generate clear and concise summaries for every customer support call, saving time for both agents and supervisors while ensuring that important details are captured.
Sentiment and Issue Detection
The accelerator's ability to detect sentiment and major issues within calls enables businesses to quickly address customer concerns and improve overall service.
Customizable for Industry-Specific Insights
Fine-tune the model to meet the specific needs of your industry, ensuring it provides highly accurate insights for your unique customer interactions.
Performance Tracking and Reporting
Track key performance metrics across calls, including agent response quality and customer sentiment, helping to continuously refine support strategies and agent training programs.
