Unlock the potential of AI foundation models with our comprehensive LLM consulting services. From designing and training sophisticated language models to formulating tailored strategies, we empower you to leverage LLMs and NLP solutions for your specific use cases. Maximize your language processing capabilities and drive innovation with our expertise.
We specialize in fine-tuning LLMs through prompt engineering, leveraging transfer learning and data optimization. Our team carefully selects pre-trained models, trains them on relevant datasets and enhances their performance. With meticulous curation and precise instructions, we ensure maximum efficacy in optimizing LLMs for specific language-related tasks and domains.
LLMOps offers comprehensive management of language model operations. Our skilled team ensures efficient model deployment, monitoring and optimization using prompt engineering, fine-tuning, and model distillation techniques. We leverage tools like HoneyHive and HumanLoop to deliver high-performing, safe and compliant LLMOps solutions.
We enable the effortless incorporation of powerful pre-trained languages models like BERT or GPT-3 into your applications. With expertise in architecture understanding, domain customization, continuous training and retraining, we ensure accurate and adaptive integration that enhances your application’s language capabilities.
Experience the power of LLM-powered applications, revolutionizing various domains. From conversational chatbots like ChatGPT and personal assistants such as Notion AI to specialized tools like Jasper and Codium AI, our comprehensive service brings the capabilities of LLMs to enhance productivity, creativity, and efficiency in your applications.
We provide expert prompt engineering services. With a focus on maximizing autoregressive language model performance. We specialize in designing tailored prompts and selecting effective examples. Our expertise in zero-shot learning, few-shot learning, and chain-of-thought prompting ensures optimal model steerability and desired outcomes.
Foundation model selection is critical in fine-tuning large language models (LLMs). Our AI experts consider your use cases, model size, performance, language task capabilities, training data, updates and available resources. Evaluating these aspects ensures the selection of the right foundation model. Following are a few points we consider while selecting the foundation models from proprietary models and open-source models:
After selecting the foundation model, we access the LLM through APIs. Working with LLM APIs is more challenging than working with traditional APIs because the input-output relationship is only sometimes clear beforehand. We need to adapt the foundation model to downstream tasks or your specific goals to get the desired output. For that, we use the following techniques:
In classic MLOps, ML models are validated with performance metrics that indicate the model’s performance. We know that GLUE, SuperGLUE and SQuAD are some of the benchmarks for LLMs. Companies also use CommonsenseQA and StrategyQA benchmarks for reasoning evaluations. However, evaluating LLMs presents unique challenges. A/B testing is efficient in comparing models.
Deploying and monitoring LLM-powered applications require diligent attention to ensure optimal performance and mitigate risks. The ever-evolving nature of LLMs necessitates staying vigilant with the following considerations:
We take a comprehensive approach towards LLM consulting. We address the technical aspects of LLM and its impact on your business operations, people and processes. By gaining insights into your business challenges and goals, we fine-tune LLMs or develop customized LLM solutions that drive value and maximize ROI.