Large language models (LLMs) are revolutionizing the way we interact with technology. These powerful AI systems can generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way. But how are these models built?
This guide delves into the process of building an LLM from scratch, focusing on the often-overlooked aspects of training and data preparation. We’ll also touch on fine-tuning, inference, and the importance of sharing your work with the community.
The Secret Sauce: Data Preparation
While model architecture and hyperparameter tuning play a role, the most crucial factor in LLM performance is the data used for training. Experts from OpenAI and Entropic emphasize that the data set defines the model’s behavior and capabilities.
Therefore, meticulous data preparation is key. This involves several steps:
1. Data Collection:
- Common Crawl: This massive dataset contains petabytes of web data collected over the past decade. It offers vast diversity and coverage, but requires extensive filtering.
- Code repositories: GitHub and Software Heritage are excellent sources for code data, crucial for training LLMs to understand and generate code.
- Curated sources: Wikipedia and public domain books offer high-quality text, but may lack diversity.
- Synthetic data: LLMs can generate synthetic data tailored to specific topics and behaviors, offering greater control and scalability.
2. Filtering:
- Language filtering: Tools like FastText efficiently filter data by language.
- Heuristic filtering: This involves defining rules to remove low-quality data based on surface-level features like character repetition or punctuation ratios.
- ML-based filtering: Train a classifier or use perplexity measures to identify and remove undesirable data.
- Topic filtering: Ensure your data covers the desired topics and knowledge domains.
Hugging face library DataTrove is really helpful in data preparation.
3. Deduplication:
The web contains significant duplicate content. Deduplication improves training efficiency and accuracy, but requires careful consideration to avoid removing valuable data. Techniques like MinHash and BloomFilters offer different trade-offs between memory usage and accuracy.
4. Data Preparation for Training:
- Shuffling: Randomly shuffle your data to avoid biases and ensure all parts of the dataset contribute equally to the model’s vocabulary.
- Tokenization: Choose an appropriate tokenization strategy, paying particular attention to handling numbers, code elements, and special characters.
5. Data Quality Evaluation:
Evaluating data quality at scale is challenging. Training small models on the data and measuring their performance on high-signal benchmarks can provide valuable insights. Additionally, manually inspecting top domains and URLs helps ensure data quality.
from datatrove import DataTrove
# Initialize Data Trove with Common Crawl data source
dt = DataTrove(source="commoncrawl")
# Filter by language (e.g., English)
dt.filter_languages(["en"])
# Apply heuristic filters
dt.filter_heuristics(min_chars_per_line=5, max_duplicate_ngrams=3)
# Train and apply an ML-based filter
dt.train_ml_filter(good_data_path="good_data.txt", bad_data_path="bad_data.txt")
dt.filter_ml()
# Deduplicate using MinHash
dt.deduplicate(method="minhash", threshold=0.9)
# Filter by topic (e.g., physics)
dt.filter_topics(topic_list=["physics"])
# Prepare data for training
dt.shuffle()
dt.tokenize(tokenizer_type="dpe")
# Save the processed dataset
dt.save(output_path="processed_data.bin")Efficient Training Techniques
Training LLMs efficiently requires addressing two key challenges:
1. Parallelization:
LLMs often exceed the capacity of a single GPU, necessitating parallelization techniques:
- Data parallelism: Replicate the model on multiple GPUs and train on different data chunks, merging gradients during optimization.
- Tensor parallelism: Split the model’s weight matrices across multiple GPUs to handle models too large for data parallelism.
- Pipeline parallelism: Divide the model into layers and distribute them across GPUs, communicating intermediate results.
- Sequence parallelism: Split the input sequence across GPUs to parallelize computations performed independently for each token.
2. Synchronization:
Minimize communication overhead by overlapping computation and communication, both between GPUs and between CPU and GPU. Techniques like asynchronous computation and kernel fusion can significantly improve training speed.
Another huggingface library to help you train.
from nanotron import Nanotron, TransformerConfig
# Define model configuration
config = TransformerConfig(
vocab_size=50256,
hidden_size=1024,
num_layers=12,
attention_heads=16,
)
# Initialize Nanotron with the configuration and dataset path
nanotron = Nanotron(config, data_path="processed_data.bin")
# Train the model with specified parallelism and optimization settings
nanotron.train(
data_parallelism=4,
tensor_parallelism=2,
pipeline_parallelism=2,
optimizer="adamw",
learning_rate=1e-5,
)Beyond Transformers: Exploring New Architectures
While transformers have dominated the LLM landscape, recent innovations offer new possibilities:
- Mixture of Experts (MoE): This architecture uses specialized “expert” sub-networks to handle different aspects of the input data, potentially increasing capacity and efficiency. MegaBlox demonstrates the potential of MoE by efficiently handling sparsity through block-sparse matrices. Read about DBRX a leading open source MoE model
- Recurrent Models: Mamba, a state-space model, shows promising results by combining the strengths of convolutional and recurrent architectures.
Fine-Tuning and Alignment: Shaping LLM Behavior
After pre-training, LLMs need fine-tuning to align their behavior with specific tasks and desired outcomes.
- Alignment: Techniques like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) help shape the model’s behavior and responses.
- Fine-tuning: This involves further training the model on task-specific data to improve its performance on specific tasks.
import torch
from transformers import AutoModelForCausalLM
# Load pre-trained model
model = AutoModelForCausalLM.from_pretrained("pretrained_model")
# Define optimizer and loss function
optimizer = torch.optim.AdamW(model.parameters())
loss_fn = torch.nn.CrossEntropyLoss()
# Load fine-tuning data and prepare batches
# ...
# Fine-tuning loop
for epoch in range(num_epochs):
for batch in data_loader:
# Forward pass
outputs = model(batch["input_ids"])
# Calculate loss
loss = loss_fn(outputs.logits, batch["labels"])
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Save fine-tuned model
model.save_pretrained("fine_tuned_model")Inference Optimization: Bringing LLMs to the Real World
Efficient inference is crucial for deploying LLMs in real-world applications. Recent breakthroughs have made significant strides:
- Quantization: Converting model weights from floating-point values to integers significantly reduces memory footprint and improves inference speed.
- Speculative decoding: This technique uses a smaller model to generate candidate outputs, which are then validated by a larger model, leading to faster inference.
- Compilation and Graph Optimization: Compiling the model and optimizing the computation graph further enhances inference efficiency.