Module 21 — Training Pipeline for Large Language Models

Introduction

Training a Large Language Model (LLM) involves much more than defining a Transformer architecture. A complete training pipeline includes:

• Preparing datasets
• Tokenizing text
• Batching samples efficiently
• Configuring training hyperparameters
• Distributed training
• Memory optimization
• Checkpointing
• Evaluation
• Model saving

Modern libraries such as Hugging Face Transformers, Datasets, TRL, Accelerate, DeepSpeed, and PyTorch FSDP simplify the process of training models ranging from millions to billions of parameters.

In this module, you'll learn:

• Dataset
• Data Collator
• Trainer
• SFTTrainer
• TrainingArguments
• Accelerate
• DeepSpeed
• Fully Sharded Data Parallel (FSDP)
• Mixed Precision
• Gradient Checkpointing
• Train a Custom LLM

Complete LLM Training Pipeline

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1                 Dataset
2                    │
3            Data Preprocessing
4                    │
5               Tokenization
6                    │
7              Data Collator
8                    │
9          TrainingArguments
10                    │
11        Trainer / SFTTrainer
12                    │
13Accelerate / DeepSpeed / FSDP
14                    │
15             Model Training
16                    │
17          Evaluation & Saving

1. Dataset

What is a Dataset?

A dataset contains the examples used to train or evaluate a model. For language models, each example is usually a text sequence or an instruction–response pair.

Common dataset formats:

• Plain text
• JSON / JSONL
• CSV
• Parquet
• Hugging Face Datasets

Example instruction dataset:

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1Instruction:
2Explain recursion.
3
4Response:
5Recursion is a programming technique...

Load a Dataset

pythonCopy
1from datasets import load_dataset
2
3dataset = load_dataset(
4    "Abirate/english_quotes"
5)
6
7print(dataset)

2. Data Collator

What is a Data Collator?

A Data Collator prepares batches before they are sent to the model.

Responsibilities:

• Dynamic padding
• Label creation
• Batch construction
• Mask generation

Pipeline

textCopy
1Individual Samples
2
3↓
4
5Data Collator
6
7↓
8
9Batch Tensor

Example

pythonCopy
1from transformers import (
2    DataCollatorForLanguageModeling,
3    AutoTokenizer
4)
5
6tokenizer = AutoTokenizer.from_pretrained(
7    "gpt2"
8)
9
10collator = DataCollatorForLanguageModeling(
11    tokenizer=tokenizer,
12    mlm=False
13)

3. Trainer

What is Trainer?

Trainer is Hugging Face's high-level training API.

Responsibilities

• Training loop
• Evaluation
• Logging
• Checkpoint saving
• Gradient accumulation

Architecture

textCopy
1Dataset
2
3↓
4
5Trainer
6
7↓
8
9Training Loop
10
11↓
12
13Checkpoint

Example

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1from transformers import Trainer
2
3trainer = Trainer(
4    model=model,
5    args=training_args,
6    train_dataset=train_dataset
7)

4. SFTTrainer

What is SFTTrainer?

SFTTrainer (Supervised Fine-Tuning Trainer) is provided by the TRL (Transformer Reinforcement Learning) library and is designed specifically for instruction tuning and chat model fine-tuning.

Advantages

• Instruction tuning
• Chat datasets
• Conversational formatting
• LLM optimization

Example

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1from trl import SFTTrainer
2
3trainer = SFTTrainer(
4    model=model,
5    train_dataset=dataset,
6    args=training_args
7)

5. TrainingArguments

What are TrainingArguments?

TrainingArguments define the training configuration.

Common parameters

• Learning rate
• Batch size
• Epochs
• Logging
• Evaluation strategy
• Save strategy
• Gradient accumulation
• Mixed precision

Example

pythonCopy
1from transformers import TrainingArguments
2
3training_args = TrainingArguments(
4    output_dir="./model",
5    num_train_epochs=3,
6    learning_rate=2e-5,
7    per_device_train_batch_size=4,
8    gradient_accumulation_steps=4,
9    logging_steps=20,
10    save_strategy="epoch"
11)

6. Accelerate

What is Accelerate?

Accelerate is a Hugging Face library that simplifies training across:

• CPU
• Single GPU
• Multiple GPUs
• TPUs

Pipeline

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1PyTorch Code
2
3↓
4
5Accelerate
6
7↓
8
9Single GPU / Multi GPU / TPU

Example

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1from accelerate import Accelerator
2
3accelerator = Accelerator()
4
5model, optimizer, train_loader = accelerator.prepare(
6    model,
7    optimizer,
8    train_loader
9)

7. DeepSpeed

What is DeepSpeed?

DeepSpeed is Microsoft's distributed training framework for large models.

Features

• ZeRO optimization
• Memory partitioning
• Gradient partitioning
• Optimizer partitioning
• CPU offloading
• NVMe offloading

Architecture

textCopy
1Model
2
3↓
4
5ZeRO
6
7↓
8
9Distributed GPUs
10
11↓
12
13Training

Benefits

• Train larger models
• Lower GPU memory
• Faster distributed training

8. Fully Sharded Data Parallel (FSDP)

What is FSDP?

FSDP is PyTorch's distributed training technique that shards:

• Model parameters
• Gradients
• Optimizer states

Pipeline

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1GPU 1
2
3↓
4
5Shard Parameters
6
7↓
8
9GPU 2
10
11↓
12
13Shard Parameters
14
15↓
16
17GPU 3

Advantages

• Reduced memory usage
• Excellent scalability
• Native PyTorch integration

9. Mixed Precision

What is Mixed Precision?

Mixed precision uses lower-precision data types such as FP16 or BF16 where appropriate to accelerate training while preserving model quality.

Pipeline

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1FP32
2
3↓
4
5FP16 / BF16
6
7↓
8
9Faster Training

Advantages

• Faster computation
• Lower memory usage
• Larger batch sizes
• Better GPU utilization

Example

pythonCopy
1training_args = TrainingArguments(
2    output_dir="./model",
3    fp16=True
4)

On hardware with native BF16 support, bf16=True is often preferred over fp16=True.

10. Gradient Checkpointing

What is Gradient Checkpointing?

Gradient checkpointing reduces memory usage by recomputing selected intermediate activations during the backward pass instead of storing them all.

Pipeline

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1Forward Pass
2
3↓
4
5Discard Activations
6
7↓
8
9Backward Pass
10
11↓
12
13Recompute
14
15↓
16
17Gradient

Advantages

• Lower GPU memory
• Train larger models
• Slight increase in computation time

Enable Gradient Checkpointing

pythonCopy
1model.gradient_checkpointing_enable()

Comparison of Training Technologies

Practice 1 — Train a Small Language Model

pythonCopy
1from datasets import load_dataset
2from transformers import (
3    AutoTokenizer,
4    AutoModelForCausalLM,
5    Trainer,
6    TrainingArguments,
7    DataCollatorForLanguageModeling
8)
9
10model_name = "gpt2"
11
12tokenizer = AutoTokenizer.from_pretrained(model_name)
13tokenizer.pad_token = tokenizer.eos_token
14
15dataset = load_dataset(
16    "Abirate/english_quotes"
17)
18
19def tokenize(example):
20    return tokenizer(
21        example["quote"],
22        truncation=True,
23        max_length=128
24    )
25
26dataset = dataset.map(tokenize)
27
28collator = DataCollatorForLanguageModeling(
29    tokenizer=tokenizer,
30    mlm=False
31)
32
33model = AutoModelForCausalLM.from_pretrained(model_name)
34
35training_args = TrainingArguments(
36    output_dir="./gpt2-training",
37    num_train_epochs=3,
38    per_device_train_batch_size=8,
39    learning_rate=5e-5,
40    logging_steps=20,
41    fp16=True
42)
43
44trainer = Trainer(
45    model=model,
46    args=training_args,
47    train_dataset=dataset["train"],
48    data_collator=collator
49)
50
51trainer.train()

What You'll Learn

• Load and tokenize a dataset.
• Create batches using a data collator.
• Configure training with TrainingArguments.
• Train a causal language model using Trainer.

Practice 2 — Train a Custom LLM with SFTTrainer

pythonCopy
1from datasets import load_dataset
2from transformers import (
3    AutoTokenizer,
4    AutoModelForCausalLM,
5    TrainingArguments
6)
7from trl import SFTTrainer
8
9model_name = "meta-llama/Llama-3.2-1B"
10
11dataset = load_dataset(
12    "tatsu-lab/alpaca"
13)
14
15tokenizer = AutoTokenizer.from_pretrained(model_name)
16tokenizer.pad_token = tokenizer.eos_token
17
18model = AutoModelForCausalLM.from_pretrained(model_name)
19
20training_args = TrainingArguments(
21    output_dir="./alpaca-llama",
22    num_train_epochs=1,
23    learning_rate=2e-5,
24    per_device_train_batch_size=2,
25    gradient_accumulation_steps=8,
26    bf16=True,
27    logging_steps=10,
28    save_strategy="epoch"
29)
30
31trainer = SFTTrainer(
32    model=model,
33    train_dataset=dataset["train"],
34    args=training_args
35)
36
37trainer.train()
38
39trainer.save_model("./alpaca-llama")

What You'll Learn

• Fine-tune a causal LLM on an instruction-following dataset.
• Use SFTTrainer for supervised fine-tuning.
• Configure mixed precision and gradient accumulation.
• Save the resulting model for inference or further training.

Best Practices for LLM Training

Module Summary

After completing this module, you will be able to:

• Explain the components of a complete LLM training pipeline.
• Load and preprocess datasets using the Hugging Face Datasets library.
• Create efficient batches with data collators.
• Configure and use Trainer for general Transformer training.
• Fine-tune instruction-following models using SFTTrainer.
• Customize training with TrainingArguments.
• Use Accelerate for hardware-agnostic training.
• Understand how DeepSpeed and FSDP enable large-scale distributed training.
• Apply mixed precision and gradient checkpointing to optimize memory and performance.
• Train and save a custom language model for downstream applications.

Next Module: Module 22 – Inference, Quantization & Deployment, where you'll learn model quantization (8-bit/4-bit), GGUF, ONNX, TensorRT, vLLM, Text Generation Inference (TGI), FastAPI serving, streaming generation, and production deployment of Transformer models.