Model Solutions
Unlocking the Power of Words
Don’t wait for the future to happen – be a part of shaping it with Invenci.
The Revolution of Large Language Models
Large language models are transforming industries by enabling more natural and intuitive interactions between humans and machines
Mistral
A state-of-the-art text generation model, Mistral excels in complex multilingual reasoning tasks such as text understanding, transformation, and code generation.
Invenci has deployed Mistral with great success both in hosted and private retrieval augmented generation models.
Llama2
Llama 2, a remarkable language model from Meta, offers a family of transformer-based autoregressive causal language models, enabling individuals, creators, researchers, and businesses to experiment, innovate, and scale their ideas responsibly.
Another model implemented by Invenci in retrieval augmented generation use cases.
Starcoder
StarCoder has emerged as a powerful tool in the field of programming. Trained on licensed data from GitHub, including over 80 programming languages, Git commits, GitHub issues, and Jupyter notebooks, StarCoder enables a wide range of applications, from code autocompletion to natural language explanations of code snippets.
Invenci has employed StarCoder for our customers in text to SQL Code generation use cases.
Phi
Phi-2 is a compact model developed by Microsoft Research. Despite its relatively small size, Phi-2 achieves outstanding reasoning and language understanding capabilities and is available in the Azure AI Studio model catalog.
Phi-2 is yet another model deployed by Invenci for our customers in narrow use case retrieval augmented generation.
Discover more you can do with Models.
Selecting the Right Large Language Model for Your Business Needs
Steps to Choose an Optimal Large Language Model:
1. Define Specific Business Objectives: Clearly articulate the goals and outcomes you expect from deploying a large language model, such as enhancing customer service, automating content creation, or improving decision-making processes.
2. Evaluate Model Performance: Assess various models based on their performance metrics like accuracy, speed, and scalability, ensuring they meet the specific demands of your business tasks.
3. Consider Data Compatibility: Ensure the model can effectively handle the type and volume of data your business generates, including the ability to train on your proprietary datasets if customization is required.
4. Analyze Integration Capabilities: Check how easily the model can be integrated with your existing IT infrastructure and whether it supports the APIs and interfaces you currently use.
5. Review Compliance and Ethics: Evaluate the model for compliance with relevant data privacy laws (such as GDPR) and ethical considerations, ensuring it aligns with your company’s values and legal obligations.
6. Cost-Benefit Analysis: Consider the total cost of implementation, including training, integration, and ongoing maintenance, against the expected ROI to determine if the investment aligns with your budget and financial forecasts.
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When to use Open Source?
Cost Efficiency: Open-source models typically come without the hefty licensing fees associated with closed-source solutions, reducing initial and ongoing financial commitments.
Customizability and Control: Businesses have the flexibility to modify and tailor open-source models to fit specific requirements, providing greater control over the deployment and maintenance of the technology.
Transparency and Trust: With access to the source code, businesses can fully understand and trust how the model operates, ensuring no undisclosed functionalities that could impact performance or compliance.
Community Support and Innovation: Leveraging a community-driven development approach, open-source models benefit from the collective expertise and continuous improvements made by developers worldwide, enhancing the model’s robustness and capabilities.
Avoidance of Vendor Lock-In: Using open-source models avoids dependency on a specific vendor’s technology and pricing changes, granting businesses greater independence and flexibility in their AI strategy.
Pioneers in building the AI community.
At Invenci, we are deeply committed to the open-source community, recognizing its vital role in fostering innovation and collaboration in the technology landscape. Our engagement with open-source initiatives not only enhances our offerings but also ensures we contribute to and benefit from the latest advancements and collective expertise in AI development.
