Our Capabilities for AI Large Language Models Development
Professionals at Cloudsmate ensure real-world applicability and accelerated performance with large language model development technical expertise.
Natural Language Processing
With our custom NLP model development, we build models with NLU and NLG capabilities using tools and frameworks such as NLTK, TensorFlow, and spaCy. These NLP models efficiently analyze, decode, and generate human language.
Machine Learning
We use advanced techniques like supervised, unsupervised, and reinforcement learning to build machine learning language models utilizing scikit-learn, PyTorch, and Keras. These ML models ensure efficacious and efficient business outcomes.
Transfer Learning
Our experts fine-tune pre-trained large language models like ChatGPT, LaMDA, BLOOM, Llama, and PaLM to build custom LLM models. They precisely meet domain-specific needs for businesses with progressive transfer learning techniques.
Deep Learning
Cloudsmate utilizes deep-learning algorithms to probe complex data for building ML-based DL technologies, further building business intelligence technology frameworks. The data unveils imaginative opportunities to achieve precise perfection.
Sentiment Analysis
Our experts utilize tools like VADER and NLTK to preprocess and then analyze the text data to train the LLM models. Using machine learning techniques like Naive Bayes, we establish businesses with accurate and precise sentiment analysis-based LLM-based systems.
In-Context Learning
We capitalize on tools like PyText, FastText, and Flair to prepare LLM models with revivified data, guaranteeing ongoing adaption to contemporary domains. This enduring advancement improves the subsequent model performance.
Our Large Language Model Development Process
Cloudsmate follows a systematic approach and streamlined process for LLM-powered solutions.
1. Data Set Preparation
Aggregating datasets for data-driven preprocessing prior to building a combination of structured and unstructured data.
2. Data Set Pipeline
Edificing neural networks to upturn functioning and fine-tuning hyperparameters to ascertain predictive preciseness.
3. Experimentation
Training the LLM model on high-powered GPUs and fine-tuning with transfer learning for specific tasks and domains.
4. Data Evaluation
Evaluating the performance with test data to review and validate the large language model to meet the target metrics.
5. Deployment
Initiating the deployment process on the environment as soon as the LLM model reaches the target performance and output.
6. Prompt Engineering
Improving the output quality constantly with prompt engineering and implementing the user feedback of the LLM model.
