PyTorch for LLMs: Pro Course — Build Language Models from Scratch

Learn PyTorch by building LLMs from scratch: tokenisers, attention, transformers, training loops, distributed training, fine-tuning, and deployment. 13 modules, 65+ hours.

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Course Snapshot

Course Code	AIM-608
Duration	65+ hours
Modules	13
Projects	5
Phase	5 — PyTorch & Multimodal AI
Skill Level	Intermediate to Advanced
Format	Self-paced + live cohorts
Price	₹7,999 (early bird ₹5,499)
Last Updated	July 2026

Course Overview

If you have ever loaded a Hugging Face model and wondered what actually happens inside the forward pass, this course is your answer. We build every component of a modern language model from scratch in PyTorch — the tokeniser, the embedding layer, positional encoding, multi-head attention, the full transformer block, and the training loop that turns random weights into a language model. You will not just call ''nn.TransformerEncoder'; you will write the attention math yourself, understand why gradient clipping matters, and debug a training run that diverges. From there we move to modern LLM architectures — RMSNorm, RoPE, SwiGLU, grouped-query attention, and mixture-of-experts — implementing each one in raw PyTorch before comparing with production implementations. The course then covers distributed training with DDP and FSDP, fine-tuning with LoRA written from scratch, inference optimisation with KV caching and continuous batching, and deployment with custom serving code. Thirteen modules, five projects, and sixty-five hours of deep, hands-on PyTorch engineering. By the end, you will not just use LLMs — you will understand them at the tensor level and be able to build, train, and deploy your own.

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Who This Course Is Built For

This course is designed for specific professional profiles. If you match any of these, you will get maximum value.

ML Engineers

Engineers who want to go beyond calling Hugging Face APIs and understand the internals of transformer models at the PyTorch tensor level.

AI Researchers

Researchers who need to implement and modify novel architectures, custom attention mechanisms, or training algorithms in raw PyTorch.

Backend & Systems Engineers

Engineers building inference infrastructure who need to understand KV caching, attention computation, and memory management at the code level.

CS Students & Self-Learners

Learners who want a rigorous, from-scratch understanding of how language models work rather than a black-box API course.

This Course Is NOT For You If:

Those who have never written Python or used NumPy — start with a Python programming course first.
Anyone looking for a prompt engineering or no-code course — this is deep PyTorch implementation work.
Learners without access to a GPU — labs require at least a Colab T4 for training exercises.
Those who only want to use pretrained models via API — this course is about building and training, not calling.

What You Will Learn

After completing this course, you will be able to:

1 You will be able to implement a byte-pair encoding tokeniser from scratch in Python
2 You will be able to write multi-head self-attention and cross-attention in raw PyTorch
3 You will be able to build a complete transformer decoder model from individual layers
4 You will be able to write a training loop with loss computation, backprop, and gradient clipping
5 You will be able to implement modern LLM components: RMSNorm, RoPE, SwiGLU, and GQA
6 You will be able to train a small language model from random initialization on a custom corpus
7 You will be able to run distributed training with PyTorch DDP and FSDP across multiple GPUs
8 You will be able to implement LoRA fine-tuning from scratch without the PEFT library
9 You will be able to optimise inference with KV caching, continuous batching, and speculative decoding
10 You will be able to deploy a custom-trained model with a hand-written inference server

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Complete Course Curriculum

13 modules with detailed lessons. Every lesson includes specific learning points.

MODULE 1 — PyTorch Foundations

5 hours

Tensors, Gradients, and Autograd

Understand PyTorch tensors as the fundamental data structure for all LLM operations
Master autograd: how PyTorch builds a computation graph and computes gradients
Use requires_grad, retain_grad, and torch.no_grad() to control gradient computation
Debug gradient flow with hooks and gradient norm tracking

nn.Module and Custom Layers

Build custom layers by subclassing nn.Module with __init__ and forward methods
Register parameters, buffers, and sub-modules correctly
Use nn.Parameter to create trainable weights inside custom layers
Chain modules with nn.Sequential and inspect model architecture with named_parameters

Training Utilities: Optimizers and Schedulers

Configure SGD, Adam, and AdamW optimizers with appropriate learning rates
Use cosine annealing, linear warmup, and polynomial learning rate schedulers
Implement gradient clipping with torch.nn.utils.clip_grad_norm_
Save and load model checkpoints with state_dict and torch.save

MODULE 2 — Tokeniser from Scratch

5 hours

Byte-Pair Encoding (BPE) Implementation

Implement the BPE algorithm: merge rules, vocabulary building, and encoding
Train a BPE tokeniser on a text corpus with configurable vocabulary size
Handle special tokens: BOS, EOS, PAD, and UNK
Compare your BPE implementation with Hugging Face tokenizers and tiktoken

Tokeniser Engineering Details

Implement pre-tokenization with regex patterns for words, spaces, and punctuation
Handle Unicode and multilingual text in the tokeniser pipeline
Build encode and decode functions that are inverse-compatible
Profile tokeniser throughput and optimize for batch encoding

Embedding Layers and Vocabulary

Create nn.Embedding layers with custom vocabulary sizes and dimensions
Understand the relationship between token IDs and embedding vectors
Implement tied embeddings vs separate input/output embeddings
Visualise embedding space with t-SNE and PCA for trained models

MODULE 3 — Attention Implementation

6 hours

Scaled Dot-Product Attention

Implement the core attention formula: softmax(QK^T / sqrt(d_k))V in PyTorch
Understand why we scale by sqrt(d_k) to prevent gradient vanishing in softmax
Build causal masks for autoregressive (decoder-only) models
Debug attention weight distributions and identify common issues

Multi-Head Attention

Split the embedding dimension into multiple heads for parallel attention
Implement head projection with linear layers for Q, K, and V
Concatenate and project multi-head outputs back to the model dimension
Profile memory usage of multi-head attention and identify bottlenecks

Optimised Attention: Flash Attention Concepts

Understand the tiling strategy that makes Flash Attention memory-efficient
Implement a simplified online-softmax attention in PyTorch
Use PyTorch's F.scaled_dot_product_attention with Flash Attention backend
Benchmark standard attention vs Flash Attention in speed and memory

MODULE 4 — Transformer from Scratch

6 hours

Positional Encoding

Implement sinusoidal positional encoding from the original Attention is All You Need paper
Build learned positional embeddings as an alternative
Understand why decoder-only models use RoPE (Rotary Position Embedding)
Implement RoPE from scratch and compare with sinusoidal encoding

Feed-Forward Networks and Layer Normalisation

Build the standard two-layer MLP with GELU activation
Implement LayerNorm and RMSNorm from scratch with learnable parameters
Understand pre-norm vs post-norm architecture and their training stability
Compare GELU, SwiGLU, and ReLU feed-forward variants

Assembling the Full Transformer Block

Combine attention, feed-forward, residual connections, and normalisation into a block
Stack N transformer blocks into a complete decoder-only model
Implement the language modeling head for next-token prediction
Initialize weights with Xavier/Glorot and understand initialization impact on training

MODULE 5 — Training a Language Model

6 hours

The Training Loop

Write a complete training loop: forward pass, loss, backward, optimizer step
Implement cross-entropy loss for next-token prediction with label shifting
Add gradient accumulation to simulate larger batch sizes on limited GPU memory
Log training metrics: loss, perplexity, learning rate, and gradient norm

Data Loading for Language Models

Build a custom Dataset and DataLoader for text corpora with proper batching
Implement variable-length sequence handling with padding and attention masks
Use packing to concatenate documents and maximize GPU utilization
Create efficient data pipelines with prefetching and num_workers

Training a Small GPT from Scratch

Train a 10M parameter GPT model on a small text corpus (Shakespeare or Wikipedia)
Monitor training loss curves and identify convergence vs divergence
Generate text samples during training to qualitatively assess model improvement
Save checkpoints and resume training from a saved state

MODULE 6 — Modern LLM Architecture

5 hours

RMSNorm and SwiGLU

Implement RMSNorm as the modern replacement for LayerNorm in Llama and Mistral
Build the SwiGLU feed-forward block: SiLU activation with gating mechanism
Compare parameter counts and throughput of SwiGLU vs standard MLP
Understand why RMSNorm + SwiGLU improves training stability and inference speed

Rotary Position Embedding (RoPE)

Implement RoPE with complex number rotation of query and key vectors
Understand how RoPE encodes relative position without learned parameters
Extend RoPE to long contexts with NTK-aware scaling and YaRN
Compare RoPE with ALiBi and learned absolute position embeddings

Grouped-Query Attention and Mixture of Experts

Implement Grouped-Query Attention (GQA) to share K and V across query heads
Understand the memory and speed trade-offs of GQA vs MHA vs MQA
Build a Mixture-of-Experts (MoE) layer with top-k routing and expert networks
Implement load balancing loss for MoE training stability

MODULE 7 — Distributed Training

5 hours

DataParallel and DistributedDataParallel

Understand the difference between DataParallel (DP) and DistributedDataParallel (DDP)
Set up DDP with torch.distributed and multiple process groups
Launch multi-GPU training with torchrun and environment variables
Profile DDP communication overhead and gradient synchronization

Fully Sharded Data Parallel (FSDP)

Understand FSDP as the PyTorch equivalent of DeepSpeed ZeRO-3
Shard model parameters, gradients, and optimizer states across GPUs
Configure FSDP with mixed precision and activation checkpointing
Benchmark FSDP vs DDP on memory usage and training throughput

Tensor Parallelism and Pipeline Parallelism

Implement tensor parallelism by splitting attention heads across GPUs
Understand pipeline parallelism with micro-batching for very large models
Combine FSDP with tensor parallelism for 3D parallelism
Use torch.distributed.device_mesh for flexible parallelism strategies

MODULE 8 — Fine-Tuning Implementations

5 hours

LoRA from Scratch

Implement LoRA without the PEFT library: low-rank matrices A and B
Inject LoRA adapters into specific linear layers of a transformer
Train only LoRA parameters while freezing the base model
Merge LoRA weights back into the base model for deployment

Quantization-Aware Fine-Tuning

Implement 8-bit and 4-bit quantization wrappers for linear layers
Understand straight-through estimators for gradient flow through quantization
Train with quantized weights using bitsandbytes integration
Compare quality of quantized fine-tuning vs full-precision training

Preference Optimization in PyTorch

Implement the DPO loss function from scratch with a reference model
Build a simple PPO training loop with reward model and KL penalty
Implement GRPO with group-relative advantage estimation
Compare convergence and stability of DPO, PPO, and GRPO implementations

MODULE 9 — Inference Optimisation

5 hours

KV Caching

Implement KV cache for autoregressive generation to avoid recomputing attention
Understand cache shape, cache updating, and cache management across batches
Benchmark generation speed with and without KV caching
Implement cache compression with H2O (Heavy-Hitter Oracle) attention

Continuous Batching and PagedAttention

Understand continuous batching for variable-length request handling
Implement a simplified PagedAttention scheme with block-based KV cache
Compare static batching vs continuous batching in throughput and latency
Integrate continuous batching with a custom request queue

Speculative Decoding and Quantized Inference

Implement speculative decoding with a draft model and acceptance sampling
Use dynamic quantization with torch.quantization for CPU inference
Benchmark INT8 and INT4 inference with FP16 baseline
Combine speculative decoding with KV caching for maximum speedup

MODULE 10 — Custom Model Components

4 hours

Custom Attention Variants

Implement sliding window attention for long-context models
Build linear attention with kernel approximations for O(n) complexity
Implement multi-query attention and compare with grouped-query attention
Experiment with sparse attention patterns for document-level tasks

Custom Positional and Activation Functions

Implement ALiBi (Attention with Linear Biases) as an alternative to RoPE
Build custom activation functions: GeGLU, ReGLU, and gated variants
Experiment with different normalisation schemes: DeepNorm and ScaleNorm
Profile the impact of each component on training and inference speed

Extending Existing Architectures

Modify a Hugging Face model to add custom layers or attention variants
Implement mixture-of-depths: dynamic per-token computation allocation
Build a custom loss function for multi-task training
Test architectural changes on a small scale before scaling up

MODULE 11 — Evaluation & Benchmarking

4 hours

Perplexity and Loss Metrics

Compute perplexity correctly for language models with proper masking
Track training and validation perplexity curves for convergence analysis
Implement bits-per-character (BPC) for cross-tokeniser comparison
Detect overfitting and underfitting from perplexity curves

Benchmarking Against Standard Tasks

Implement evaluation harness for MMLU, GSM8K, and HumanEval in PyTorch
Build a few-shot evaluation pipeline with prompt formatting
Run zero-shot and few-shot evaluation on custom-trained models
Compare your model against published benchmarks for similar sizes

Profiling and Debugging

Use torch.profiler to identify CPU and GPU bottlenecks in training
Debug NaN losses with anomaly detection and gradient hooks
Profile memory usage with torch.cuda.memory_allocated and memory_reserved
Optimize data loading and forward pass with torch.compile

MODULE 12 — Deployment

4 hours

Building a Custom Inference Server

Write a FastAPI server that loads a PyTorch model and serves completions
Implement streaming responses with Server-Sent Events for token-by-token output
Add request batching and queueing for concurrent request handling
Configure GPU memory management with torch.cuda.empty_cache and context managers

Model Export and Optimisation

Export PyTorch models to ONNX for cross-platform inference
Use torch.compile and inductor for graph-level optimisations
Convert models to TensorRT for NVIDIA GPU optimisation
Benchmark exported models vs native PyTorch inference

Production Monitoring

Implement latency and throughput monitoring with Prometheus metrics
Track token-level latency and identify generation bottlenecks
Set up health checks and graceful degradation for inference servers
Log model outputs for quality monitoring and drift detection

MODULE 13 — Capstone Projects

5 hours

Project 1: GPT from Scratch

Build a complete GPT-style transformer in raw PyTorch
Train on a text corpus and generate coherent text samples
Evaluate perplexity and compare with a baseline
Document the architecture and training configuration

Project 2: Custom Attention Mechanism

Design and implement a novel attention variant (sliding window, linear, or sparse)
Integrate it into a transformer model and train
Benchmark against standard multi-head attention
Write a technical report on the trade-offs

Project 3–5: Distributed, Inference & Deployment

Scale training to multiple GPUs with DDP or FSDP
Implement KV caching and speculative decoding for fast inference
Deploy the model behind a custom FastAPI server with streaming
Present all projects with code, benchmarks, and documentation

Real-World Projects & Portfolio Outcomes

5 portfolio-worthy projects that prove your skills to employers.

Project 1

GPT from Scratch

Build a complete GPT-style transformer model entirely in raw PyTorch — tokeniser, embeddings, multi-head attention, transformer blocks, and training loop. Train on a text corpus and generate coherent samples.

Deliverable: A trained model checkpoint, generation samples, perplexity evaluation, and a documented architecture report.

Project 2

Custom Attention Mechanism

Design, implement, and benchmark a novel attention variant — sliding window, linear, or sparse attention — integrated into a transformer model. Compare against standard multi-head attention in quality and speed.

Deliverable: A custom attention implementation, benchmark results, and a technical write-up on trade-offs.

Project 3

Distributed Training at Scale

Scale a transformer training run across multiple GPUs using PyTorch DDP or FSDP. Configure distributed process groups, profile communication overhead, and benchmark against single-GPU training.

Deliverable: A distributed training script, multi-GPU benchmark report, and scaling efficiency analysis.

Project 4

Optimised Inference Engine

Implement KV caching, continuous batching, and speculative decoding for a trained model. Benchmark generation throughput and latency against a naive autoregressive baseline.

Deliverable: An optimised inference module, benchmark comparisons, and a streaming generation demo.

Project 5

Production Inference Server

Deploy a custom-trained model behind a FastAPI server with streaming responses, request batching, GPU memory management, and Prometheus monitoring. Include health checks and graceful degradation.

Deliverable: A production-ready inference server, Docker deployment config, and monitoring dashboard.

Tools, Technologies & Models Covered

LLM Models Covered

GPT-2 Small	OpenAI (repro)	Training from scratch
Llama 2 7B	Meta	Fine-tuning, DDP, FSDP
Mistral 7B	Mistral AI	Architecture study, LoRA
TinyLlama 1.1B	TinyLlama Project	Fast iteration training
Qwen 2.5 0.5B	Alibaba	Small-scale experiments
Custom GPT	Built in course	From-scratch implementation

Tools & Frameworks Covered

PyTorch	Core tensor and autograd framework	Deep
torch.distributed	DDP and FSDP distributed training	Deep
torch.compile	Graph-level optimisation	Intermediate
torch.profiler	Performance profiling	Intermediate
FastAPI	Inference server deployment	Intermediate
ONNX	Cross-platform model export	Intermediate
TensorRT	NVIDIA GPU optimisation	Intermediate
bitsandbytes	Quantization for fine-tuning	Intermediate
Weights & Biases	Experiment tracking	Intermediate

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Your Instructor

Dr. Priya Sharma

Principal ML Infrastructure Engineer

10+ years in deep learning systems and PyTorch

Built custom training frameworks used by 3 AI startups

2,800+ engineers trained

You do not understand a transformer until you have written the attention computation yourself, watched the loss curve, and debugged a NaN. This course makes you do exactly that.

Course Team & Curriculum Design

Our teaching team includes PyTorch contributors and ML infrastructure engineers who have built custom training frameworks from scratch. TAs provide code review and debugging support during live cohorts.

Learning Path & Prerequisites

Prerequisites

Intermediate to advanced Python programming
Basic PyTorch: tensors, nn.Module, training loops (or willingness to learn fast)
Linear algebra: matrix multiplication, dot products, and softmax
Understanding of neural network training: loss, gradients, and backpropagation
Access to a GPU (Colab T4 minimum, local GPU recommended for later modules)

Recommended Learning Paths

ML Engineer wanting model internals

Complete Modules 1–5 to build a transformer from scratch
Take Module 6 for modern architecture components
Choose Modules 8–9 for fine-tuning and inference skills
Build the GPT-from-scratch and inference server projects

Researcher implementing novel architectures

Focus on Modules 3–4 and Module 6 for attention and architecture
Deep-dive into Module 10 for custom components
Complete the custom attention mechanism project
Use Module 7 for scaling experiments to multiple GPUs

Infrastructure engineer building inference systems

Take Modules 1–3 for foundational PyTorch and attention
Focus on Module 9 for inference optimisation techniques
Complete Module 12 for production deployment
Build the optimised inference engine and server projects

What Comes After This Course

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Pricing & Enrollment

Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.

Self-Paced	₹7,999	All 13 modules, 5 projects, community Discord, lifetime access
Early Bird	₹5,499	Same as Self-Paced — limited time before cohort launch
Cohort Live	₹14,999	Live sessions, TA support, code review, and certificate

What Is Included

13 modules with 50+ hands-on lab notebooks
5 capstone projects with code review
Pre-configured Colab and multi-GPU training scripts
Private Discord community with instructors and TAs
Lifetime access including future updates
Certificate of completion with project portfolio

30-day money-back guarantee. Complete the first 4 modules and if the course is not the right fit, get a full refund — no questions asked.

Enroll Now — Early Bird ₹5,499

Frequently Asked Questions

Do I need prior PyTorch experience?

You need basic PyTorch familiarity — tensors, nn.Module, and a simple training loop. Module 1 provides a refresher, but this is not a beginner PyTorch course. If you are completely new, we recommend completing a basic PyTorch tutorial first.

Will I really build a transformer from scratch?

Yes. By Module 4 you will have written multi-head attention, positional encoding, feed-forward networks, layer normalisation, and a complete transformer block — all in raw PyTorch without using nn.TransformerEncoder. Module 5 has you train it on a real text corpus.

Do I need multiple GPUs for the distributed training module?

No. Module 7 includes Colab notebooks that simulate multi-GPU training on a single GPU. For true multi-GPU training, you can use Kaggle (2x T4) or cloud GPU rentals (RunPod, Lambda). We provide scripts for both single-GPU simulation and real multi-GPU setups.

How is this different from the Fine-Tuning LLMs course (AIM-607)?

AIM-607 teaches you to fine-tune existing models using libraries like PEFT, TRL, and Unsloth. This course (AIM-608) teaches you to build, train, and deploy models from scratch in raw PyTorch. You will understand the internals that those libraries abstract away.

What GPU do I need for the training exercises?

A Google Colab free T4 (16GB) is sufficient for Modules 1–6 and most projects. For distributed training (Module 7) and larger models, a local GPU with 24GB+ VRAM or a cloud rental is recommended. We provide size-appropriate models for every GPU tier.

Is this course useful if I only use Hugging Face models?

Absolutely. Understanding the internals helps you debug training issues, choose the right architecture, write custom loss functions, and optimise inference. Many students report that this course transformed how they use Hugging Face libraries.

Will I get a certificate?

Yes. Complete all 5 capstone projects and submit them for code review. Upon passing, you receive a certificate with links to your project portfolio, suitable for sharing on LinkedIn and with employers.