Natural Language Processing with PyTorch

Natural Language Processing and LLMs with PyTorch

Customized NLP Solutions

At IntelliSensei, we provide tailored Natural Language Processing (NLP) solutions utilizing the powerful PyTorch framework. Our team designs, implements, and optimizes NLP models to meet specific business needs, allowing you to leverage the extensive potential of your textual data. Through customized tokenization, embedding techniques, and domain-specific adjustments, we ensure that your NLP models are consistently delivering actionable insights.

State-of-the-Art Model Development

We build and fine-tune transformer and LLM-based systems in native PyTorch with the Hugging Face ecosystem. That ranges from lightweight domain-specific encoders for classification, NER and retrieval, to parameter-efficient fine-tuning (LoRA/QLoRA with PEFT) of open-weight models such as Llama, Mistral, Qwen and Gemma on your own data and infrastructure. Where a full LLM is overkill we still deliver fast, compact task-specific models that cost a fraction to run. For the deeper engineering work, see our dedicated LLM fine-tuning and customization service.

Retrieval-Augmented Generation

Most enterprise language applications need to answer from your documents, not from a model's general knowledge. We design RAG pipelines end to end: embedding-model selection and fine-tuning, chunking and indexing strategy, hybrid retrieval with rerankers, vector-store integration (pgvector, OpenSearch, dedicated stores), and evaluation of retrieval hit-rate and answer groundedness. Read more on our RAG systems page.

Model Training and Optimization

Maximizing the performance of your NLP models requires precise training and fine-tuning. We offer comprehensive training regimes that include data preparation and decontamination, hyperparameter tuning, and the implementation of custom loss functions. We use mixed precision (BF16), gradient checkpointing and FSDP2 to train larger models on the hardware you already have, and preference tuning (DPO) where tone and safety matter. To ensure robustness, our team applies best practices for avoiding overfitting and for evaluating on genuinely held-out data.

LLM Inference and Deployment

We understand the importance of deploying language models into production environments seamlessly. For LLMs that means high-throughput serving with vLLM or SGLang (continuous batching, paged attention), quantized inference (INT8/FP8/INT4) to cut GPU cost, and torch.compile for latency-critical encoder models. We package everything with Docker and Kubernetes and wire it into your CI/CD so that models are always up to date and capable of handling large-scale operations. See LLM inference and serving optimization for the details.

Real-Time Text Processing

For applications requiring real-time text processing, such as chat assistants, agents, or interactive support tools, we offer optimized solutions that deliver rapid responses. We engineer for time-to-first-token and tokens-per-second budgets, stream responses, and add guardrails and PII handling so that real-time systems are safe as well as fast.

Sentiment Analysis and Text Classification

Our sentiment analysis and text classification services help you understand and categorize customer feedback, reviews, support tickets and social media interactions. A fine-tuned encoder model is still the right tool for most high-volume classification work: it is faster, cheaper and more predictable than prompting an LLM, and we can train it on a few thousand labelled examples.

Named Entity Recognition (NER) and Information Extraction

Enhance your text data comprehension with our Named Entity Recognition and structured-extraction solutions. We use fine-tuned PyTorch models to identify and classify entities in your text, such as names, places, dates, amounts and domain-specific identifiers, and LLMs with constrained decoding where the output must be valid JSON. This data extraction process is crucial for applications in finance, healthcare, and legal sectors.

Translation, Summarization and Generation

We offer translation, summarization and controlled text-generation services that enable you to break language barriers and condense lengthy documents into digestible formats. Our models facilitate accurate translations between multiple languages and provide concise, faithful summaries while preserving key information, with evaluation harnesses that catch hallucination before your users do.

Continuous Support and Maintenance

Our commitment does not end at deployment. We provide continuous support and maintenance for your NLP and LLM systems to ensure their ongoing performance and relevance. This includes regular model and dependency updates, performance and drift monitoring, and troubleshooting, helping you adapt to evolving requirements and technological advancements.

Utilizing our expertise in PyTorch and NLP, we empower your organization to transform textual data into valuable insights and actionable outcomes.

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