Large Language Model (LLM) Development and Consulting

FAQ

• What is a large language model?

  A large language model is a type of artificial intelligence that relies on a wide range of NLP, deep learning, and ML algorithms to understand the structure of the language. It is trained on a very large dataset to generate accurate responses and catch up with conversations.

  Large language models have been shown to outperform traditional models on a variety of tasks, including machine translation, question answering, and sentiment analysis. Also, unlike traditional chatbots and virtual assistants, LLMs can come in handy for a variety of tasks, including text generation, image captioning, summarization, and other large language models use cases.

• What are the major large language models?

  Large language models examples include the GPT model which is trained on a dataset of 570 GB and fine-tuned for a variety of language tasks, such as translation, summarization, and question-answering. The model is 175 billion parameters in size, which makes it the largest language model ever trained.

  Megatron is another example of a large, powerful transformer with 11 billion parameters. Our team also works with OpenLLaMA, StableLM, PaL, and other major conversational AI solutions. We select the right LLM that suits your business needs and workloads.

• How is a large language model created?

  A large language model is created by training a neural network on a large corpus of text. The neural network learns to predict the next word in a sequence, based on the previous words in the sequence. The more parameters the mode has, the more capable it is, and the more training data it needs to score a high accuracy rate.

  Unlike traditional AI software, LLMs are general purpose and can be fine-tuned to match the specific needs of a given business. From sentiment analysis to content generation to granular recommendations, language models can support business operations across multiple areas.

• How much does a large language model cost?

  The cost of developing, training, and deploying a large language model can vary significantly depending on several factors, including the model’s size, complexity, usage, and whether you’re building it in-house or using a cloud-based API. Here’s an overview of the potential costs involved:

  • Training сosts
  • Hardware and compute сosts
  • Data сosts
  • Operational costs (Maintenance and fine-tuning).
  • Development team costs.
• What are the stages of LLM development?

  The development of a Large Language Model typically involves several key stages, each of which is crucial to building a robust, effective, and scalable model. Below are the primary stages in LLM development:

  1. Problem definition and requirement gathering
  2. Data collection and preprocessing
  3. Model architecture design
  4. Model training
  5. Fine-tuning (Optional)
  6. Evaluation and testing
  7. Model optimization and compression
  8. Deployment and integration
  9. Monitoring and maintenance
  10. Ethical considerations and bias mitigation.
• Is GPT an LLM?

  GPT is one of the most popular language models that is based on the combination of NLP, reinforcement learning, neural networks, and other innovative technologies. This ready-made model can be integrated into applications or customized on proprietary datasets through fine-tuning.