3 min read AI

Training a Local LLM: From Zero to (Almost) Hero - Using Your Own Documents

Training a Local LLM: From Zero to (Almost) Hero - Using Your Own Documents
Photo by Karsten Winegeart / Unsplash

So you've been captivated by the power of Large Language Models (LLMs) like ChatGPT, but you're concerned about privacy, cost, or simply want to understand how these things actually work? Training your own LLM locally is a challenging but incredibly rewarding endeavor. This article will guide you through the process, outlining the steps, tools, and considerations for bringing a language model to life on your own hardware.

Disclaimer: Training an LLM from scratch requires significant computational resources and time. This article focuses on fine-tuning an existing model, which is a more achievable goal for most enthusiasts. Training from scratch is generally reserved for organizations with substantial infrastructure.

What is Fine-Tuning?

Imagine a model already knowing a lot about language – grammar, common sense, etc. This is a pre-trained model. Fine-tuning takes that existing knowledge and adapts it to a specific task or dataset. Think of it like teaching a talented artist to specialize in portraiture. They already know how to paint, but you're giving them specific examples and guidance to improve their portrait skills.

Prerequisites:

  • Hardware: A powerful GPU is essential. At a minimum, you'll want an NVIDIA GPU with at least 12GB of VRAM. More VRAM (24GB+) is highly recommended, especially for larger models. CPU and RAM also matter, but the GPU is the bottleneck.
  • Software:
    • Python: The primary language for machine learning.
    • PyTorch or TensorFlow: Deep learning frameworks. PyTorch is often preferred for its flexibility.
    • Transformers Library (Hugging Face): A powerful library that simplifies working with pre-trained models. pip install transformers
    • Datasets Library (Hugging Face): To easily load and manage datasets. pip install datasets
    • PEFT (Parameter-Efficient Fine-Tuning): Crucial for making training feasible on limited hardware. pip install peft
  • Dataset: A folder containing your text documents. These could be technical manuals, research papers, stories, or anything else you want the model to learn from.

Step-by-Step Guide:

1. Choose a Pre-Trained Model:

The Hugging Face Model Hub (https://huggingface.co/models) is a fantastic resource. Popular choices for fine-tuning include:

  • Mistral 7B: A strong performer with a relatively small size.
  • Llama 2 7B/13B: Meta's open-source models.
  • TinyLlama: Extremely small and fast, good for experimentation.

Consider the model size: smaller models require less VRAM but may have lower performance.

2. Prepare Your Dataset:

  • Data Format: Your dataset is a folder of .txt files (or other text-based formats). Each file will be treated as a separate document.
  • Loading the Dataset: We'll use the datasets library to load the documents from your folder.
from datasets import load_dataset

dataset_path = "/path/to/your/document/folder" # Replace with your actual folder path
dataset = load_dataset("text", data_dir=dataset_path, split="train")

print(dataset) # See the structure of your dataset
  • Data Cleaning: While not always necessary, you might want to perform some basic cleaning, like removing unwanted characters or normalizing whitespace. This can be done using a function applied to each example in the dataset.

3. Implement Parameter-Efficient Fine-Tuning (PEFT):

Training all the parameters of a large model is computationally expensive. PEFT techniques like LoRA (Low-Rank Adaptation) allow you to train only a small subset of the parameters, significantly reducing VRAM requirements and training time.

from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from peft import LoraConfig, get_peft_model

model_name = "mistralai/Mistral-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Configure LoRA
lora_config = LoraConfig(
    r=8,  # Rank of the LoRA matrices
    lora_alpha=32,  # Scaling factor
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

model = get_peft_model(model, lora_config)
model.print_trainable_parameters() # Verify only a small number of params are trainable

4. Tokenization and Data Preparation:

def tokenize_function(examples):
    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)

tokenized_dataset = dataset.map(tokenize_function, batched=True)

# Remove the original text column, as we only need the input_ids and attention_mask
tokenized_dataset = tokenized_dataset.remove_columns(["text"])

# Set the format to PyTorch tensors
tokenized_dataset.set_format("torch")

5. Training:

training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    num_train_epochs=3,
    logging_steps=10,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
)

trainer.train()

6. Evaluation and Inference:

Evaluate the model on a held-out set of data (if available) to measure its performance. Once you're satisfied with the results, you can use the model for inference.

Troubleshooting & Considerations:

  • Out of Memory (OOM) Errors: Reduce the batch size, gradient accumulation steps, or use a smaller model.
  • Slow Training: Use a more powerful GPU, reduce the dataset size, or optimize the training code.
  • Overfitting: Use regularization techniques, such as dropout, or increase the dataset size.
  • Data Quality: The quality of your data is critical. Clean and normalize the data carefully.

Resources:

This revised version focuses on using a local folder of documents, providing a more practical example for many users. Remember to replace /path/to/your/document/folder with the actual path to your documents.

Aman Bhandal

Aman Bhandal