pylmt 0.6.0

A PyTorch implementation of transformer-based language models including GPT architecture for pretraining and fine-tuning

Language Modeling using Transformers (LMT)

An educational PyTorch library for understanding how modern transformer architectures work -- from attention mechanisms to full language models. Every component is written to be understood, with clear code, detailed docstrings, and mathematical notation that maps directly to the papers.

Features

Attention Mechanisms

Component	Description	Paper
Multi-Head Attention	Standard scaled dot-product attention	Vaswani et al., 2017
Grouped Query Attention	Shared KV heads for efficiency	Ainslie et al., 2023
Sliding Window Attention	Local attention with fixed window	Beltagy et al., 2020
Multi-Head Latent Attention	KV compression + decoupled RoPE	DeepSeek-AI, 2024

Feed-Forward Networks

Component	Description
SwiGLU	Gated FFN with Swish activation (LLaMA, Mixtral)
Mixture of Experts	Top-k sparse routing with load balancing loss

Other Components: RMSNorm, Rotary Position Embedding (RoPE)

Model Architectures

Model	Key Components
GPT	Multi-head attention + GELU FFN + learned position embeddings
LLaMA	RMSNorm + RoPE + SwiGLU + GQA
Mixtral	LLaMA + MoE FFN + sliding window attention

Installation

pip install pylmt

Or install from source for development:

git clone https://github.com/michaelellis003/LMT.git
cd LMT
pip install uv
uv sync

Quick Start

import torch
from lmt.models.config import ModelConfig
from lmt.models.llama import LLaMA

config = ModelConfig(
    vocab_size=32000,
    embed_dim=512,
    num_heads=8,
    num_kv_heads=4,     # GQA: 4 KV heads shared across 8 query heads
    num_layers=6,
    context_length=1024,
    dropout=0.0,
)

model = LLaMA(config)
x = torch.randint(0, config.vocab_size, (1, 128))
logits = model(x)  # [1, 128, 32000]

Using Individual Layers

from lmt.layers.attention import GroupedQueryAttention
from lmt.layers.ffn import SwiGLU
from lmt.layers.normalization import RMSNorm

norm = RMSNorm(d_model=512)
attn = GroupedQueryAttention(config)
ffn = SwiGLU(d_model=512)

x = torch.randn(1, 64, 512)
x = x + attn(norm(x))  # Pre-norm attention
x = x + ffn(norm(x))   # Pre-norm FFN

Mixture of Experts

from lmt.models.mixtral import Mixtral

config = ModelConfig(
    vocab_size=32000, embed_dim=512, num_heads=8,
    num_kv_heads=4, num_layers=8,
    context_length=2048, window_size=256, dropout=0.0,
)

model = Mixtral(config, num_experts=8, top_k=2)
logits = model(x)
aux_loss = model.aux_loss  # load balancing loss for training

Project Structure

src/lmt/
  layers/
    attention/     # MHA, GQA, Sliding Window, MLA
    ffn/           # SwiGLU, MoE (Router + Experts)
    normalization/ # RMSNorm
    positional/    # RoPE
  models/
    gpt/           # GPT (original decoder-only transformer)
    llama/         # LLaMA (RMSNorm + RoPE + SwiGLU + GQA)
    mixtral/       # Mixtral (LLaMA + MoE + sliding window)
  training/        # Trainer, configs, dataloaders
  tokenizer/       # BPE, naive tokenizers

Development

uv run pytest tests/ -v       # Run tests (171 passing)
uv run ruff check src/ tests/ # Lint
uv run ruff format src/ tests/ # Format
uv run pyright src/            # Type check
uv run mkdocs serve            # Local docs server

License

Apache License 2.0. See LICENSE for details.