Tailoring Small Language Models for Enterprise Use Cases
Editor’s note: Julien Simon is a speaker for ODSC Europe this September 5th-6th! Be sure to check out his talk, “Tailoring Small Language Models for Enterprise Use Cases,” there!
Artificial Intelligence has become integral across various industries in recent years — a testament to its growing importance within modern business landscapes and research realms.
However, the initial excitement surrounding large language models (LLMs), particularly closed-source models, was met with a sobering reality. Despite their impressive feats in language generation tasks such as text completion and conversation, many organizations grappled with turning these technologies into viable production solutions.
The ROI and quality targets were out of reach for the majority mainly because closed-source models can be prohibitively expensive, both financially and operationally. They also often come with stringent limitations in terms of customization, which is critical when adapting AI to specific industry needs — a common requirement.
Small Language Models are the future
Fortunately, the open-source community has been at the forefront of driving innovation through collaboration, and it has proven to be capable of matching and surpassing LLM capabilities using small language models (SLMs).
Arcee AI has taken it upon itself to help AI practitioners tailor SLMs with cost-effective and nimble techniques. My session at ODSC Europe will guide you through creating optimized SLMs that are well-suited for real-life business applications.
Preparing Training Datasets for SLMs
Before we delve into fine-tuning and adaptations, it is crucial to prepare your training datasets. The process involves gathering domain-specific texts that are representative of the type and quality you expect from these AI systems once they’re deployed into production. These could range from customer service interactions to technical documentation to industry-relevant content. The possibilities here can be as varied as your business needs require.
End-to-End Model Adaptation
Once we’ve prepared our datasets and have a model ready for training, we can adapt it through a series of steps, typically continuous pre-training, model merging models, and last but not least, instruction fine-tuning — a process that involves refining an existing language understanding capability by exposing it to specific question-answer pairs.
Following this, we move on to quantization and inference — steps crucial for optimizing the model’s performance to run efficiently in production environments. Quantization is essentially about reducing the precision of numbers used during calculations — this helps speed up processing times significantly while maintaining satisfactory accuracy levels.
State-of-the-art tools
The demos in this session will rely on open-source tools such as Hugging Face transformers, Mergekit, Spectrum, and llama.cpp. We’ll also show you open-source datasets and models built by Arcee that are available on the Hugging Face hub. Nodding to enterprise practitioners looking for an off-the-shelf platform to adapt their models, we’ll close the session with a few words on Arcee’s products.
Conclusion
The journey towards harnessing AI’s full potential is not one without its challenges. Yet, with tools like those mentioned above and a well-structured approach to model adaptation, we can tailor even small language models into highly effective production-grade solutions that meet quality benchmarks and ROI targets.
By joining this session, you will gain practical insights into preparing SLMs for real-world applications, what continuous pre-training looks like in practice, and the importance of efficient inference processes. Arcee has demonstrated success with tailored models built specifically to meet business needs, and this session offers invaluable knowledge and inspiration on how your organization can take advantage of these innovations.
Don’t miss out — register now for an in-depth discussion and demonstration of SLMs, datasets, and Arcee solutions. Your journey towards mastering Small Language Models begins here!
About the Author/ODSC Europe 2024 Speaker:
Julien Simon, the Chief Evangelist at Arcee.ai, is dedicated to helping enterprise clients develop top-notch and cost-efficient AI solutions using open-source small language models. With over 30 years of tech experience, including more than a decade in cloud computing and machine learning, Julien is committed to daily learning and is passionate about sharing his expertise through code demos, blogs, and YouTube videos. Before joining Arcee.ai, he was Chief Evangelist at Hugging Face and Global Technical Evangelist at Amazon Web Services. He also served as a CTO at prominent startups.
Originally posted on OpenDataScience.com
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