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LLM Training Building and Optimizing AI Models

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 Training

Core training fundamentals & data preparation

Intermediate Training

Fine-tuning & optimization techniques

Advanced Training

State-of-the-art training methods

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 (for specialized tasks)

Architecture Selection

Choose the right model architecture for your needs:

  • Transformer-based (GPT, BERT, T5)
  • Encoder-Decoder (for translation tasks)
  • Decoder-only (for generation tasks)

Intermediate LLM Training

Fine-Tuning & Optimization. This level focuses on refining your model's performance through advanced techniques and optimization strategies.

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

Advanced LLM Training

State-of-the-Art Techniques. This level applies the most advanced, scalable techniques to push your LLM's capabilities to the edge.

Large-Scale Distributed Training

Scale training across multiple GPUs and nodes:

  • Data Parallelism: Split data across devices
  • Model Parallelism: Split model across devices
  • Pipeline Parallelism: Layer-wise distribution

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

Multimodal Training

Train models to understand multiple data types:

  • Vision-Language Models: CLIP, DALL-E
  • Audio-Language Models: Whisper, SpeechT5
  • Code-Language Models: CodeT5, CodeBERT

Let's build your next-generation AI—together

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