Large Language Models (LLMs)
Words shape the universe.
Large Language Models (LLMs) are neural networks trained to understand and generate human language at scale.
At Dhee, we work exclusively with transformer-based LLMs, as they currently provide the most reliable foundation for high-quality, multilingual, and conversational systems.
Transformer architectures enable models to process entire sequences in context, making them especially suitable for complex language understanding and generation tasks.
Transformer-Based LLMs
Transformer-based LLMs model language by learning relationships between all tokens in a sequence simultaneously.
This allows them to:
Maintain long-range context
Capture nuanced grammatical and semantic relationships
Scale efficiently with data and model size
These properties make transformers the dominant architecture behind modern LLMs used in production systems today.
What Transformer-Based LLMs Are Good At
Transformer-based LLMs excel at:
Natural language understanding
Conversational response generation
Translation across languages
Summarization and rewriting
Intent recognition
Semantic similarity and entailment
They perform best when:
Sufficient context is provided
The task is language-centric
High linguistic fidelity is required
How Transformer-Based LLMs Work (Conceptually)
At a high level, transformer-based LLMs operate as follows:
Tokenization Text is converted into tokens suitable for the model.
Contextual Processing The transformer processes all tokens together, allowing each token to attend to every other token in the context.
Token Prediction The model predicts the most likely next token, repeatedly, to generate an output.
This process enables coherent, context-aware language generation.
Stateless by Design
Transformer-based LLMs are stateless.
Each interaction:
Is processed independently
Has no inherent memory of previous turns
Relies entirely on provided context
Any persistent behavior are implemented outside the model in Dhee GPT Platform.
Conversational LLMs in Indian Languages
A core focus at Dhee is building conversational transformer-based LLMs for Indian languages.
These models are designed for:
Native conversational flow
Spoken-language patterns
Code-mixed inputs
Multi-turn dialogue consistency
Rather than treating Indian languages as translation targets, these models are trained and adapted for direct conversational competence.
Our open model collection is available here for you to try and use in your projects: 👉 https://huggingface.co/collections/dheeyantra/dhee-nxtgen-qwen3-v2
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