At Mosaic Factor, we focus on the creation of domain specific LLMs (or light Large Language Models) for our client organisations.
Lightweight LLMs are of huge importance considering factors such as:
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- Sustainability
- Privacy
- Ethics
Light LLMs are more accountable AI models since they are tailored to the needs of a specific industry or domain, making them more secure, effective, and lighter in every aspect (considering energy consumption, size and adaptation to its intended use).
We focus on building LLMs that work with the intrinsic security and privacy requirements of industries which need to track down and have legal proof of what happen within their systems -and even when training AI models- such as Healthcare, Corporate Services, Manufacturing, to name some.
Clearly, also companies who aim to become socially responsible when managing large amounts of data or documentation and wish to approach AI model possibilities, responsibly.
Why using this solution?
- Data Analysis and customisation: LLMs can process large volumes of textual data to identify patterns and trends, facilitating informed decision-making and strategic planning as well as personalizing services to specific users based on their behavioural data.
- Automation of repetitive tasks: LLMs can take care of repetitive and low-value tasks, such as email classification, report generation, and data entry. This frees up time for employees to focus on more strategic tasks.
- Improved Customer Service: LLMs can be integrated into chatbots and virtual assistants to provide fast and accurate responses to customer queries, improving satisfaction and reducing wait times.
Integrating LLMs into your company
Integrating light LLMs into existing systems can be a strategic move which need to be managed properly. This is how we do it:
1. Assessment and Planning:
a. Identify Use Cases: we determine where an LLM can add value. Common use cases include chatbots, sentiment analysis, content generation, and translation.
b. Evaluate Data: we assess the quality and quantity of available data. LLMs require substantial training data for optimal performance.
2. Model Selection:
a. Choose a Light LLM: we opt for smaller models (e.g., DistilBERT, TinyGPT) that offer efficiency without sacrificing quality
b. Fine-Tuning: when using a pre-trained model, we fine-tune it on domain-specific data to improve relevance and accuracy.
3. Infrastructure and Deployment:
a. Computational Resources: we allocate sufficient computational resources (CPU/GPU) for training and inference.
b. API Integration: we set up APIs to interact with the LLM. Popular frameworks include Hugging Face Transformers and OpenAI’s API.
c. Scalability: we ensure the system can handle increased load as LLM usage grows.
4. Data Preprocessing:
a. Tokenization: we convert text into tokens suitable for LLM input.
b. Input Formatting: we prepare input data (e.g., prompts, questions) for LLMs.
5. Inference and Output:
a. Batch Processing: we optimise inference by batching requests.
b. Post-Processing: we clean and format LLM-generated output for user consumption.
6. Monitoring and Maintenance:
a. Performance Metrics: we monitor LLM performance (e.g. accuracy, response time).
b. Regular Updates: we keep LLMs up to date with new data and retrain them periodically.
c. Error Handling: we implement robust error handling for unexpected scenarios.
LLM integration is an iterative process. We usually recommend starting with a small-scale pilot, gather feedback, and refine the system based on real usage data.
Building robust and scalable AI models
We focus on building highly performant optimised AI models that minimise the use of computational resources, resulting in lower costs and environmental impact.
We always evaluate AI use cases within the specific company industry to make sure LLMs empower businesses to make data-driven decisions, optimise processes, and stay ahead in a dynamic market landscape.
