# train-llm-from-scratch | Fareed Khan | Knowledge Base | Instagit

A straightforward method for training your LLM, from downloading data to generating text.

GitHub Stars: 2.6k

Repository: https://github.com/FareedKhan-dev/train-llm-from-scratch

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## Articles

### [Value Head Architecture in RLHF Training: A Deep Dive into the Critic Network](/FareedKhan-dev/train-llm-from-scratch/what-architecture-value-head-rlhf-training)

Explore the RLHF value head architecture: a two-layer MLP projecting transformer states to scalar values. Learn how zero-initialization ensures stable training. Understand the critic network in LLM training from scratch.

- Tags: deep-dive
- Published: 2026-06-11

### [How Configuration Is Managed for LLM Training Jobs in train-llm-from-scratch](/FareedKhan-dev/train-llm-from-scratch/how-configuration-managed-llm-training-jobs)

Discover how LLM training jobs use a layered JSON config system with dataclass defaults, file merging, and CLI overrides. Learn configuration management in train-llm-from-scratch.

- Tags: internals
- Published: 2026-06-11

### [How to Use Streamlit for Controlling LLM Training: A Complete Guide to train-llm-from-scratch](/FareedKhan-dev/train-llm-from-scratch/how-use-streamlit-controlling-llm-training)

Control LLM training from scratch with an intuitive Streamlit interface. Manage data prep, pretraining, fine-tuning, and RLHF from your browser. Perfect for train-llm-from-scratch.

- Tags: how-to-guide
- Published: 2026-06-11

### [How to Evaluate an LLM's Performance on the GSM8K Benchmark: A Complete Implementation Guide](/FareedKhan-dev/train-llm-from-scratch/how-evaluate-llm-performance-gsm8k-benchmark)

Learn how to evaluate an LLM's performance on the GSM8K benchmark with this complete implementation guide. Discover the steps to accurately measure your model's math problem-solving capabilities.

- Tags: how-to-guide
- Published: 2026-06-11

### [How to Perform Inference and Generate Text with a Trained LLM: A Complete Guide](/FareedKhan-dev/train-llm-from-scratch/how-perform-inference-generate-text-trained-llm)

Easily perform inference and generate text with a trained LLM. Learn how to load your model and use generate_reply for seamless text generation with custom controls.

- Tags: tutorial
- Published: 2026-06-11

### [How to Format Instructions and Use Chat Templates for LLM Input](/FareedKhan-dev/train-llm-from-scratch/how-format-instructions-use-chat-templates-llm-input)

Learn to format instructions and use chat templates for LLM input with our tokenizer-agnostic implementation. Convert messages to token sequences and masks efficiently.

- Tags: how-to-guide
- Published: 2026-06-11

### [How to Process and Tokenize Data for LLM Training: A Complete Pipeline Guide](/FareedKhan-dev/train-llm-from-scratch/how-process-tokenize-data-llm-training)

Learn to process and tokenize data for LLM training with a complete pipeline guide. Convert raw text to efficient HDF5 token files using tiktoken and specialized loss masks.

- Tags: tutorial
- Published: 2026-06-11

### [How to Implement Proximal Policy Optimization (PPO) for LLM Fine-Tuning](/FareedKhan-dev/train-llm-from-scratch/how-implement-proximal-policy-optimization-ppo-llm-fine-tuning)

Learn how to implement Proximal Policy Optimization PPO for LLM fine-tuning. Stabilize RLHF training with trust regions and advantage estimation.

- Tags: tutorial
- Published: 2026-06-11

### [Direct Preference Optimization (DPO) Implementation Guide: From Theory to Code in train-llm-from-scratch](/FareedKhan-dev/train-llm-from-scratch/what-is-direct-preference-optimization-dpo-implemented)

Learn how to implement Direct Preference Optimization DPO in train-llm-from-scratch. Understand DPO theory and see code examples for aligning language models with human preferences easily.

- Tags: tutorial
- Published: 2026-06-11

### [How to Train a Reward Model for LLM Alignment: A Complete Implementation Guide](/FareedKhan-dev/train-llm-from-scratch/how-train-reward-model-llm-alignment)

Learn to train a reward model for LLM alignment. This guide details implementation using a scalar reward head and Bradley-Terry loss on human preference data.

- Tags: how-to-guide
- Published: 2026-06-11

### [How to Perform Supervised Fine-Tuning (SFT) on a Pretrained LLM: A Complete Implementation Guide](/FareedKhan-dev/train-llm-from-scratch/how-perform-supervised-fine-tuning-sft-pretrained-llm)

Learn to perform Supervised Fine-Tuning SFT on a pretrained LLM with this implementation guide. Discover prompt masking and masked cross-entropy loss for efficient training.

- Tags: how-to-guide
- Published: 2026-06-11

### [Understanding the Post-Training Pipeline for LLMs: From SFT to GRPO](/FareedKhan-dev/train-llm-from-scratch/what-involved-post-training-pipeline-llms)

Explore the LLM post-training pipeline from SFT to GRPO. Learn about reward modeling, preference alignment, and reinforcement learning in the FareedKhan-dev/train-llm-from-scratch repository.

- Tags: deep-dive
- Published: 2026-06-11

### [How to Configure Different LLM Sizes Using Hyperparameters: n_embed, n_head, and n_blocks](/FareedKhan-dev/train-llm-from-scratch/configure-different-llm-sizes-hyperparameters)

Learn to configure LLM sizes using n_embed, n_head, and n_blocks hyperparameters. Adjust model dimensions and layers via Python, JSON, or command line in train LLM from scratch.

- Tags: how-to-guide
- Published: 2026-06-11

### [How Multi-Head Attention Works in PyTorch: A Deep Dive into the Transformer Implementation](/FareedKhan-dev/train-llm-from-scratch/how-multi-head-attention-works-pytorch-implementation)

Understand multi-head attention in PyTorch. This guide explains how Transformers use parallel attention to create context-aware embeddings, offering a deep dive into the implementation.

- Tags: deep-dive
- Published: 2026-06-11

### [What Is the Purpose of the `forward_hidden` Method in the Transformer Model?](/FareedKhan-dev/train-llm-from-scratch/purpose-forward-hidden-method-transformer-model)

Discover the purpose of the forward_hidden method in Transformer models. Learn how it efficiently computes intermediate token representations for various downstream tasks.

- Tags: internals
- Published: 2026-06-11

### [Transformer Architecture for LLM Pretraining: Core Components Explained](/FareedKhan-dev/train-llm-from-scratch/key-components-transformer-architecture-llm-pretraining)

Explore the core components of Transformer architecture for LLM pretraining including self-attention and feed-forward networks. Learn how they power large language models.

- Tags: deep-dive
- Published: 2026-06-11

### [How to Save and Load PyTorch Transformer Model Checkpoints: Complete Implementation Guide](/FareedKhan-dev/train-llm-from-scratch/save-load-pytorch-transformer-checkpoints)

Learn to save and load PyTorch transformer model checkpoints including weights and optimizer state. Implement complete training snapshots using torch.save and torch.load for seamless restoration.

- Tags: how-to-guide
- Published: 2026-06-01

### [How to Implement Multi-Head Attention from Scratch Using PyTorch](/FareedKhan-dev/train-llm-from-scratch/how-to-implement-multi-head-attention-pytorch-scratch)

Learn to implement multi-head attention from scratch using PyTorch. Understand how parallel heads and scaled dot-product attention work in this detailed guide. Perfect for LLM development.

- Tags: how-to-guide
- Published: 2026-06-01

### [How to Debug and Diagnose Transformer Training Loss Divergence](/FareedKhan-dev/train-llm-from-scratch/how-to-debug-diagnose-transformer-training-loss-divergence)

Debug and diagnose transformer training loss divergence. Discover common causes like context length misalignment, missing gradient clipping, or invalid tokens. Utilize debugging tools from train-llm-from-scratch to fix issues.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Choose Vocabulary Size for Transformer Models: A Complete Guide](/FareedKhan-dev/train-llm-from-scratch/how-to-choose-vocabulary-size-transformer-models)

Learn how to choose vocabulary size for transformer models with this guide. Discover optimal ranges for monolingual and multilingual data using BPE for better NLP performance.

- Tags: tutorial
- Published: 2026-05-31

### [How to Implement Learning Rate Warmup for Transformer Training in PyTorch](/FareedKhan-dev/train-llm-from-scratch/how-to-implement-learning-rate-warmup-transformer-training)

Implement learning rate warmup for your transformer models in PyTorch. Learn how this technique stabilizes early training and prevents loss spikes, leading to better results. Fast and effective.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Save and Load Trained Transformer Models in PyTorch](/FareedKhan-dev/train-llm-from-scratch/how-to-save-load-trained-transformer-models-pytorch)

Easily save and load trained transformer models in PyTorch. Learn to store model and optimizer state_dicts using torch.save and torch.load for seamless training resumption and inference.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Optimize Transformer Training Performance on a Single GPU: 8 Proven Techniques](/FareedKhan-dev/train-llm-from-scratch/how-to-optimize-transformer-training-performance-single-gpu)

Optimize transformer training on a single GPU with 8 proven techniques. Learn to boost performance with mixed-precision, efficient attention, and optimized dataloaders for faster LLM training in the train llm from scratch repos...

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Implement Residual Connections in Transformer Blocks](/FareedKhan-dev/train-llm-from-scratch/how-to-implement-residual-connections-transformer-blocks)

Learn how to implement residual connections in Transformer blocks using the pattern x = x + sublayer(norm(x)) for stable gradient flow In deep networks

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Preprocess The Pile Dataset for Language Model Training](/FareedKhan-dev/train-llm-from-scratch/how-to-preprocess-the-pile-dataset-language-model-training)

Learn how to preprocess The Pile dataset for language model training with FareedKhan-devs efficient pipeline. Convert raw data to token-encoded HDF5 datasets for faster training.

- Tags: how-to-guide
- Published: 2026-05-31

### [Implementing Scaled Dot-Product Attention with Key, Query, and Value Projections](/FareedKhan-dev/train-llm-from-scratch/how-to-implement-attention-key-query-value-projections)

Implement key, query, and value projections for transformer attention. Learn to compute scaled dot-product attention scores and aggregate outputs efficiently.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Implement Gradient Checkpointing for Memory-Efficient Large Model Training](/FareedKhan-dev/train-llm-from-scratch/how-to-implement-gradient-checkpointing-memory-efficient-large-model-training)

Implement gradient checkpointing to reduce GPU memory for large model training. Train bigger transformer models with less hardware by recomputing activations during backpropagation.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Handle Out-of-Memory Errors When Training Large Models on Limited GPU Memory](/FareedKhan-dev/train-llm-from-scratch/handle-out-of-memory-errors-training-large-models-limited-gpu)

Learn to handle out-of-memory errors when training large models on limited GPU memory. Discover techniques like gradient accumulation, mixed-precision, and activation checkpointing for efficient training.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Implement Token Embedding Layers for Language Models in PyTorch](/FareedKhan-dev/train-llm-from-scratch/how-to-implement-token-embedding-layers-language-models)

Learn to implement token embedding layers in PyTorch for language models. Convert token IDs to continuous vectors and combine them with positional embeddings for transformer input. Train LLMs effectively.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Calculate Total Transformer Model Parameters: A Complete Guide for PyTorch](/FareedKhan-dev/train-llm-from-scratch/how-to-calculate-total-transformer-model-parameters)

Learn to calculate total transformer model parameters in PyTorch. This guide details summing token embeddings, attention, MLP layers, and layer norms. Achieve clarity on your model's size.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Configure Learning Rate Decay for Transformer Training in train-llm-from-scratch](/FareedKhan-dev/train-llm-from-scratch/how-to-configure-learning-rate-decay-transformer-training)

Learn to configure learning rate decay for Transformer training in train-llm-from-scratch by setting T_LR_DECAY_STEP and T_LR_DECAYED in config python. Optimize your model training.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Generate Text Using a Trained Transformer Model: Complete Implementation Guide](/FareedKhan-dev/train-llm-from-scratch/how-to-generate-text-using-trained-transformer-model)

Learn to generate text with a trained transformer model. Implement this guide to tokenize prompts, load checkpoints, and use model.generate() for autoregressive sampling.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Implement Layer Normalization in Transformer Blocks: Pre-Norm Architecture Explained](/FareedKhan-dev/train-llm-from-scratch/how-to-implement-layer-normalization-transformer-blocks)

Learn how to implement layer normalization in transformer blocks for stable LLM training. Understand the pre-norm architecture and residual connections.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Load and Preprocess HDF5 Data for LLM Training with train-llm-from-scratch](/FareedKhan-dev/train-llm-from-scratch/how-to-load-preprocess-hdf5-data-llm-training)

Learn to load and preprocess HDF5 data for LLM training. Convert JSONL.ZST to HDF5 using scripts and stream batched tensors efficiently with train-llm-from-scratch for PyTorch.

- Tags: how-to-guide
- Published: 2026-05-31

### [How to Implement Multi-Head Attention from Scratch in PyTorch](/FareedKhan-dev/train-llm-from-scratch/how-to-implement-multi-head-attention-from-scratch-pytorch)

Implement multi-head attention from scratch in PyTorch. Learn to build scaled dot-product attention with causal masking and combine multiple heads for richer contextual representation.

- Tags: how-to-guide
- Published: 2026-05-31

### [How the MLP Expands and Compresses Embedding Dimensions in Transformers](/FareedKhan-dev/train-llm-from-scratch/how-does-mlp-expand-compress-embedding-dimensions-transformers)

Understand how the MLP expands and compresses embedding dimensions in transformers. Learn its role in feature transformation and maintaining the residual pathway for LLM training.

- Tags: deep-dive
- Published: 2026-05-31

### [How to Configure Transformer Hyperparameters for Different Model Sizes in train-llm-from-scratch](/FareedKhan-dev/train-llm-from-scratch/how-to-configure-transformer-hyperparameters-for-different-model-sizes)

Learn to configure transformer hyperparameters for various model sizes in train-llm-from-scratch. Adjust embedding dimensions, attention heads, and layer counts for optimal scaling from small to large transformers.

- Tags: how-to-guide
- Published: 2026-05-31