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
Bits and Bytes Quantization
This technique allows for the reduction of model sizes by applying 4-bit quantization, enabling large language models to fit in smaller hardware without significantly impacting performance. It's characterized by its ability to automatically quantize weights upon loading, making it simpler to use as it does not require a calibration dataset or any post-processing. However, it's noted that inference speeds might be slower compared to other quantization methods like GPTQ or FP16 precision.
Parameter-Efficient Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) optimizes large language models by adjusting a minimal subset of parameters, maintaining performance while reducing computational demands. LoRA and QLoRA, key PEFT methods, employ low-rank adaptations and quantization to decrease memory usage and enhance processing efficiency, facilitating model fine-tuning on limited-resource setups.
GPTQ Quantization
GPTQ is a post-training quantization method designed to compress model weights, thereby reducing the model's size while aiming to minimize the loss in performance (specifically, aiming to minimize mean squared error). Unlike bits and bytes quantization, GPTQ requires a calibration dataset to fine-tune the quantization process, making it a bit more complex to implement. However, it allows for the effective reduction of model sizes, facilitating the deployment of large models on constrained hardware.
FEATURES
Unleashing the Power of Open Source Large Language Models
Healthcare
Fine-tuned LLMs can analyze medical literature, assist in clinical decision-making, and support drug discovery by adapting to the specific language and knowledge domain of healthcare.
Finance
LLMs fine-tuned on financial data can analyze market trends, detect fraud, and provide personalized financial advice by understanding the nuances of the financial industry.
E-commerce
Fine-tuning LLMs on product data and customer reviews enables personalized recommendations, sentiment analysis, and improved customer support tailored to the e-commerce domain.
Education
Fine-tuned LLMs can adapt to educational content, enabling intelligent tutoring systems, personalized learning, automated essay scoring, and domain-specific language learning support.
