LLM or Large Language Model like BERT, gpt-o-mini, deepseek-r1 are new generation of artificial intelligence making the machine and human interact better in human language primarily English (as the dataset available in this language is enormous). LLM models have very broad range of use cases from chatbots, summarizer to solving day to day issue.
From this guide we will help you explore topics like LLM training, precision modes, training process, quantization techniques with help of practical code example with real life use cases.
Introduction to LLM
Large language models are simply a deep learning model trained with natural language process and are made to understand human languages LLMs are trained over a large dataset for understanding human language making it answer human questions in human understandable language. LLMs are generally design on transformer architecture making it easier to process sequential text in parallel. GPT-4 from OpenAI, BERT from Google and Llama from Meta are great example of LLM models.
Process and Importance for LLM Training
LLM training is very important for enabling it to perform specific tasks. Pre trained models are on their own enough to handle general language tasks. Fine tunning a pre trained model on the specific dataset make it perform better on particular problems just like in case of a human being learning and practicing single field makes you an expert.
Training modes from zero is a very resource hungry task but using a pre trained models provide a solid ground to start from.
Key component of LLM training includes:
- Data Collection: Large Dataset helps to generalize the model and avoid overfitting. Ensuring the data is factually correct and of high quality is very important. For example the models like deepseek and gpt collect data from wide range of source like books, websites and documentation from official sources.
- Model Architecture: LLM models are generally a multi-layer neural network, commonly transformer architecture. Transformer generally have a encoder and decoder for input and output layer. Self-attention mechanism allows helps models to generate relation between tokens or words in the sentence making the model understand human better.
- Training Goal: General goal of a LLM is to understand human and answer in human understandable language. But training process is required for setting the parameters and selecting dataset to make the model reduce the difference between predicted and actual required output.
Read more: How to Use Large Language Models (LLMs) To Interact with Your SQL Data
Steps of LLM Training
1. Environment Setup: Setting up a suitable environment is essential, including having access to powerful hardware like GPUs or TPUs. You can use frameworks like PyTorch or TensorFlow, along with libraries like Hugging Face to streamline model implementation.
#install necessary libraries
pip install tranformers torch datasets accelerate2. Preprocessing: The steps includes cleaning and formatting the raw data for training. Removing of unwanted data, lowercasing of dataset and filtering out sentences that does not fit the criteria.
import re
# Preprocessing function to clean and lower-case text
def preprocess_text(text):
text = text.lower() # Convert to lowercase
text = re.sub(r'[^a-zA-Z0-9\s]', '', text) # Remove special characters
return text
# Example
sample_text = "Hello, World! This is a test."
processed_text = preprocess_text(sample_text)
print(processed_text)
# Output: "hello world this is a test"
3. Tokenization: Breaking down the data (generally articles to para to sentence to single words or subwords). Example: I love dancing into [“I”, “love”, “dance”].
from transformers import LlamaTokenizer
# Initialize Llama tokenizer (assuming Llama model is available in Hugging Face)
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b")
# Tokenize text
input_text = "I love dancing"
tokens = tokenizer(input_text, return_tensors="pt")
print(tokens['input_ids']) # Tokenized output
from transformers import LlamaForCausalLM
# Initialize pre-trained Llama model
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b")
# Check model architecture
print(model.config)4. Model Initialization: While using pre trained models initialization of pre trained model is a crucial step.
from transformers import LlamaForCausalLM
# Initialize pre-trained Llama model
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b")
# Check model architecture
print(model.config)5. Hyperparameter Tuning: Hyperparameter like learning rate, number of epochs and batch size have a significant impact of models accuracy and performance. Optimization of Hyperparameter is trial and error process.
from transformers import TrainingArguments
# Example of setting up hyperparameters for fine-tuning
training_args = TrainingArguments(
output_dir="./results", # Directory for saving model checkpoints
per_device_train_batch_size=8, # Batch size per device (GPU/CPU)
num_train_epochs=3, # Number of epochs
logging_dir='./logs', # Directory for logging
learning_rate=5e-5, # Learning rate
evaluation_strategy="epoch", # Evaluate the model at the end of each epoch
logging_steps=10, # Log training every 10 steps
)
print(training_args)6. Training Step: Loss calculation and adjusting weights based on that with the objective to minimize that loss function over time in next iteration.
import torch
from torch.optim import AdamW
# Simple training loop (example)
optimizer = AdamW(model.parameters(), lr=5e-5)
for epoch in range(3): # Loop through epochs
model.train()
for step, batch in enumerate(dataset['train']):
# Prepare inputs
inputs = tokenizer(batch['text'], return_tensors="pt", padding=True, truncation=True)
# Forward pass
outputs = model(**inputs, labels=inputs["input_ids"])
loss = outputs.loss
# Backward pass
loss.backward()
optimizer.step()
optimizer.zero_grad()
if step % 10 == 0: # Log every 10 steps
print(f"Epoch {epoch}, Step {step}, Loss: {loss.item()}")Fine-tuning a Pre Trained Model
Developers and researcher generally prefers not to train a model from scratch, they rather use a pre trained models like BERT, GPT and DeepSeek and fine tune them for specific domain. Fine-tuning involves training the pre-trained model on a smaller, domain-specific dataset. This process adapts the model to your particular needs while maintaining the knowledge it learned from the larger dataset. Fine-tuning is more computationally efficient and requires less data than training a model from scratch.
from transformers import Trainer, TrainingArguments
from datasets import load_dataset
Load a dataset (use any dataset for your use case)
dataset = load_dataset("wikitext", "wikitext-103-raw-v1")
Define the trainer
trainer = Trainer(
model=model, # Pre-trained model
args=training_args, # Training arguments
train_dataset=dataset['train'], # Dataset for training
eval_dataset=dataset['validation'], # Dataset for evaluation
)
Start fine-tuning
trainer.train()Precision Modes and Quantization Models
LLM training is very compute intensive and expensive task. Reducing the precision of model help speed up the process and make it less compute intensive. Lower precision format like FP16 (16 bit floating point) in place of commonly used FP32(32 bit floating point) reduce memory usage with minimal loss of performance. Most of modern deep learning libraries also support mixed training also where parts of model can be trained at different precision modes.
Quantization is a optimization technique which help reduce bit width of the model’s weights from 32 bit to lower precision formats like INT8 INT4. This helps in reducing both memory usage and computational load which makes it possible to deploy models on devices with limited resources.
from transformers import LlamaForCausalLM
from torch import quantization
# Load pre-trained Llama model
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b")
# Convert the model to use FP16 precision (half precision)
model.half()
# Alternatively, apply dynamic quantization
quantized_model = quantization.quantize_dynamic(
model,
{torch.nn.Linear}, # Quantize only the Linear layers (common practice)
dtype=torch.qint8 # Use INT8 quantization
)
# Save quantized model
quantized_model.save_pretrained("./quantized_llama")Challenges in Training a LLM Model
- Data Quality: Quality and diversity of data sources is very important to train a unbiases model that produces unfair or accurate results ensuring high quality data output.
- High Computation Requirement: Training process requires high computation resources. High power and expensive GPUs or TPUs are very important to handle large amount of data for training.
- Ethical/factual consideration: LLM produce human like output which raises concerns of potential misinformation or ethically inaccurate content. It is important to monitor this and audit model’s output or sensitive data or misinformation.
- Generalization: While LLMs perform well on tasks they were trained on, they can sometimes struggle to generalize to new, unseen data. Ensuring that models can generalize well across various domains and tasks remains a challenge.
Virtual Router Solutions
- Text Generation: Generating human-like text for articles, stories, or dialogue.
- Sentiment Analysis: Understanding the sentiment behind a piece of text.
- Text Summarization: Condensing long texts into shorter summaries.
- Machine Translation: Translating text between languages.
Conclusion
Training and fine tuning of a LLM (Large language model) like Llama, BERT and gpt is rewarding but very compute intensive and challenging process. LLM model have a wide range of application across field eg. text generation and machine translation. These model produces human like communication which helps in automation of many tools in field of content creation. Data quality and training process plays a great role in model output where parameter and steps like quantization and data selection are very crucial.
