LLM Training

Train Smarter AI, One Step at a Time

Training a Large Language Model (LLM) is a complex, multi-phase process that transforms raw data into intelligent, high-performing AI. From foundational pretraining to advanced techniques like Reinforcement Learning from Human Feedback (RLHF) and multimodal learning, each stage plays a critical role in the model’s success.

Whether you're developing a chatbot, virtual assistant, or domain-specific AI, our comprehensive training approach ensures your model is optimized, safe and ready for real-world use.

Basic LLM Training: Building a Strong Foundation for Language Models

The focus here is building a strong foundation. We prepare high-quality data and establish core training methods to set your model up for success.

Data Collection & Preprocessing

Sources: Wikipedia, research papers, books, and web data

Cleaning: Remove duplicates, fix syntax errors, and tokenize

Formatting: Convert raw data into structured training-ready formats

Tokenization

We convert text into numeric tokens for model processing.

Word-level (e.g., Word2Vec)

Subword-level (e.g., BPE, SentencePiece)

Character-level tokenization

Model Architecture Selection

Choose the architecture that fits your goals:

BERT – Great for understanding tasks

GPT – Excellent for text generation

T5 / BART – Ideal for translation and summarization

LLaMA, Falcon, Mistral – Open-source models with state-of-the-art capabilities

Training Objectives

Masked Language Modeling (MLM) – Used in BERT

Causal Language Modeling (CLM) – Used in GPT

Seq2Seq Modeling – Used in T5 and BART for generation and translation

Basic Hyperparameter Tuning

Fine-tune key settings like:

Batch size

Learning rate

Number of epochs

Optimizers (Adam, SGD)

Intermediate LLM Training (Fine-Tuning & Optimization)

At this stage, the goal is to improve performance and customize your model for specific domains or tasks.

Transfer Learning & Fine-Tuning

We adapt pretrained models to your specific industry:

Medical AI: Fine-tuned on clinical reports (e.g., MIMIC dataset)

Legal AI: Trained on legal documents

Finance AI: Specialized in financial language

Parameter Optimization

Techniques to improve training efficiency:

Layer-wise learning rate adjustment
Adaptive optimizers (AdamW, LAMB)
Mixed Precision Training (FP16, BF16)

Handling Large Datasets

Data Augmentation: Paraphrasing, back-translation
Curriculum Learning: Train with increasing difficulty
Content Filtering: Remove harmful or biased inputs

Reinforcement Learning from Human Feedback (RLHF)

We train models using human preferences:

Build a reward model from human-labeled responses
Fine-tune the model using the reward function
Iterate based on improved human feedback

Model Compression & Efficiency

Optimize for speed and scale:

Quantization (e.g., INT8, FP16)
Pruning – Trim unnecessary weights
Distillation – Train smaller models to mimic larger ones

Advanced LLM Training (State-of-the-Art Techniques) AI

This level applies the most advanced, scalable techniques to push your LLM's capabilities to the edge.

Large-Scale Distributed Training

Train models across multiple GPUs/TPUs:

Data Parallelism

Model Parallelism

Pipeline Parallelism

Prompt Engineering & In-Context Learning

Design structured prompts to guide LLM behavior without retraining.

Continual Learning & Memory Augmentation

Make your model smarter over time:

LoRA (Low-Rank Adaptation) for continual updates

Episodic memory for improved chatbot retention

Adversarial Training & Robustness

Protect your model from manipulation:

Use adversarial examples during training

Enhance output reliability and safety

Multimodal Training (Text + Images + Speech)

Train models to work across different inputs:

CLIP – Image and text

Whisper – Speech recognition and synthesis

Flamingo – Vision-language models

Summary Table of LLM Factuality Techniques

Level	Key Techniques	Examples/Models
Basic	Data Collection, Tokenization, Pretraining	BERT, GPT, T5
Basic	MLM, CLM, Seq2Seq Objectives	Masked token prediction
Intermediate	Transfer Learning, Domain Adaptation	Fine-tuned GPT for Medical AI
Intermediate	RLHF (Reinforcement Learning from Human Feedback)	ChatGPT, Claude
Intermediate	Model Compression (Quantization, Pruning, Distillation)	TinyBERT, DistilBERT
Advanced	Distributed Training (Data/Model Parallelism)	GPT-4, PaLM, LLaMA
Advanced	Continual Learning, Memory Augmentation	LoRA, Retrieval-Augmented Generation (RAG)
Advanced	Adversarial Training	Robust models against prompt attacks
Advanced	Multimodal Training	CLIP, Whisper, Flamingo