Overview

Large‑Language Models (LLMs) are transformer‑based neural nets trained on terabytes of text to learn a contextual distribution over tokens.
The training pipeline is a blend of data engineering, deep‑learning research, and large‑scale distributed systems.
Below is a “full stack” walk‑through aimed at someone who’s built distributed software and written low‑level code.

1. Data Pipeline

Implementation hint: Use torch.utils.data.IterableDataset with a generator that reads the shards sequentially, then yields mini‑batches to a collate function that pads to the max length in the batch.

2. Model Architecture

Key equations

1. Scaled Dot‑Product AttentionAttention⁡(Q,K,V)=softmax⁡ ⁣(QK⊤dk)VAttention(Q,K,V)=softmax(dk​​QK⊤​)V
2. Multi‑Head AttentionMH⁡(Q,K,V)=Concat⁡(head⁡1,…,head⁡h)WOMH(Q,K,V)=Concat(head1​,…,headh​)WO
3. Position‑wise Feed‑ForwardFFN⁡(x)=max⁡(0,xW1+b1)W2+b2FFN(x)=max(0,xW1​+b1​)W2​+b2​

3. Training Objective & Loss

For GPT‑style models the loss is computed over every token in the sequence:

L=1N∑i=1NCE⁡(pθ(xi+1∣x1:i),one-hot(xi+1))L=N1​i=1∑N​CE(pθ​(xi+1​∣x1:i​),one-hot(xi+1​))

Regularisation tricks

4. Optimisation

Example hyper‑parameters for a 175B GPT‑3‑style run:

5. Distributed Training & Hardware

5.1 Parallelism Strategies

Popular libraries: DeepSpeed (ZeRO‑3), Megatron‑LLaMA (tensor + pipeline parallelism), NVIDIA Megatron‑LM, GLaM, etc.

5.2 Hardware Landscape

Memory budgeting:

total_mem ≈ 2×(emb + transformer + activations) + optimizer

A typical rule‑of‑thumb is to keep activations ~ 40 % of memory. Activation checkpointing (aka recomputation) trades compute for memory.

6. Training Workflow (Pseudo‑code)

# Simplified training loop using DeepSpeed ZeRO-3

import deepspeed
import torch
from transformers import GPT2Config, GPT2LMHeadModel

config = GPT2Config(
    vocab_size=50257,
    n_embd=12288,
    n_layer=96,
    n_head=96,
    bos_token_id=50256,
    eos_token_id=50256,
)

model = GPT2LMHeadModel(config).to('cuda')
optimizer, model, _, _ = deepspeed.initialize(
    args=deepspeed_args,
    model=model,
    model_parameters=model.parameters(),
    config=deepspeed_config,
)

data_loader = build_dataset(...)

for epoch in range(num_epochs):
    for step, batch in enumerate(data_loader):
        inputs = batch['input_ids'].to('cuda')
        labels = batch['labels'].to('cuda')

        outputs = model(inputs, labels=labels)
        loss = outputs.loss / deepspeed_args.gradient_accumulation_steps

        model.backward(loss)
        optimizer.step()
        optimizer.zero_grad()

        if step % 100 == 0:
            print(f'Epoch {epoch} Step {step} Loss {loss.item()}')

Key points:

1. DeepSpeed ZeRO‑3 automatically shards optimizer state & gradients.
2. Gradient accumulation lets you simulate a huge batch on a few GPUs.
3. Automatic mixed‑precision (AMP) is enabled by default.

7. Post‑Training

7.1 Fine‑tuning & Adaptation

7.2 RLHF (Reinforcement Learning from Human Feedback)

1. Reward model – supervised regression on human‑ranked responses.
2. Proximal Policy Optimization – fine‑tune language model to maximise reward.
3. Safety constraints – policy‑based or rejection sampling.

8. Evaluation & Metrics

Tip: For large models, perplexity alone is insufficient; use a suite of downstream tasks and human evaluation.

9. Common Pitfalls & Gotchas

10. Quick Reference: Tooling

Bottom line

Training an LLM is essentially training a gigantic, multi‑layer transformer on a massive, clean, tokenised corpus, using distributed, memory‑efficient parallelism, and a carefully tuned optimisation loop. Once you master the data pipeline and the distributed system, the rest is largely a matter of hyper‑parameter sweeps and scaling experiments.

Happy training! 🚀