RTX 4070 Ti GPT-2 Fine-Tuning Guide

RTX 4070 Ti GPT-2 Fine-Tuning Guide | Generated by AI

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Setting Up Your Environment

Before training, ensure your NVIDIA RTX 4070 Ti is properly set up. This GPU has 12GB of VRAM, which is more than enough for GPT-2 Small (124M parameters) during fine-tuning. Training from scratch on a massive dataset like OpenWebText is impractical on a single consumer GPU due to time and compute requirements—it would take weeks or months. Instead, focus on fine-tuning the pre-trained model on your own dataset for specific tasks.

1. Install NVIDIA Drivers and CUDA

Download and install the latest NVIDIA drivers for your RTX 4070 Ti from the official NVIDIA website. Ensure it’s version 535 or higher for full compatibility.
Install the CUDA Toolkit. The RTX 4070 Ti (compute capability 8.9) supports CUDA 12.x. Recommend CUDA 12.4:
- Download from the NVIDIA CUDA Toolkit archive.
- Follow the installation guide for your OS (Windows/Linux).
Install cuDNN (CUDA Deep Neural Network library) matching your CUDA version (e.g., cuDNN 8.9 for CUDA 12.4).

Verify installation:

nvidia-smi  # Should show your GPU and CUDA version
nvcc --version  # Confirms CUDA installation

2. Set Up Python Environment

Use Python 3.10 or 3.11. Install via Anaconda or Miniconda for easier management.

Create a virtual environment:

conda create -n gpt2-train python=3.10
conda activate gpt2-train

3. Install Required Libraries

Install PyTorch with CUDA support. For CUDA 12.4:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

Verify:

python -c "import torch; print(torch.cuda.is_available())"  # Should return True

Install Hugging Face libraries and others:

pip install transformers datasets accelerate sentencepiece pandas tqdm

Preparing Your Dataset

Choose or prepare a text dataset (e.g., a .txt file with one sample per line or a CSV with a ‘text’ column).

For example, use a public dataset from Hugging Face Datasets:

from datasets import load_dataset
dataset = load_dataset("bookcorpus")  # Or your custom dataset: load_dataset("text", data_files="your_data.txt")

Split into train/test if needed:

dataset = dataset["train"].train_test_split(test_size=0.1)

Fine-Tuning GPT-2 Small

Use the Hugging Face Transformers library for simplicity. Here’s a complete script for causal language modeling (predicting the next token).

Script Example

Save this as train_gpt2.py and run with python train_gpt2.py.

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments, DataCollatorForLanguageModeling
from datasets import load_dataset

# Load tokenizer and model (GPT-2 Small)
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token  # Set padding token
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Load and preprocess dataset (replace with your dataset)
dataset = load_dataset("bookcorpus")
dataset = dataset["train"].train_test_split(test_size=0.1)

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

tokenized_dataset = dataset.map(preprocess, batched=True, remove_columns=["text"])

# Data collator for language modeling
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

# Training arguments (optimized for single GPU)
training_args = TrainingArguments(
    output_dir="./gpt2-finetuned",
    evaluation_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=4,  # Adjust based on VRAM; start low to avoid OOM
    per_device_eval_batch_size=4,
    num_train_epochs=3,  # Adjust as needed
    weight_decay=0.01,
    fp16=True,  # Mixed precision for faster training and less VRAM
    gradient_accumulation_steps=4,  # Effective batch size = batch_size * accumulation_steps
    save_steps=1000,
    logging_steps=500,
    report_to="none",  # Or "wandb" for tracking
)

# Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["test"],
    data_collator=data_collator,
)

# Train
trainer.train()

# Save the model
trainer.save_model("./gpt2-finetuned")

Running the Training

Monitor GPU usage with nvidia-smi in another terminal.
If you hit out-of-memory (OOM) errors:
- Reduce per_device_train_batch_size to 2 or 1.
- Increase gradient_accumulation_steps to maintain effective batch size.
- Use a smaller max_length (e.g., 256 instead of 512).
Training time: On a 4070 Ti, for a medium-sized dataset (e.g., 100k samples), expect 1-5 hours per epoch depending on batch size.

Evaluation and Inference

After training:

from transformers import pipeline

generator = pipeline("text-generation", model="./gpt2-finetuned", device=0)  # device=0 for GPU
output = generator("Your prompt here", max_length=50, num_return_sequences=1)
print(output)

Evaluate perplexity (lower is better):

import math
eval_results = trainer.evaluate()
perplexity = math.exp(eval_results["eval_loss"])
print(f"Perplexity: {perplexity}")

Tips and Troubleshooting

Use mixed precision (fp16=True) to save VRAM and speed up training.
If training from scratch (not recommended): Implement a custom transformer architecture (e.g., via NanoGPT or similar repos) and use a huge dataset, but expect very long runtimes.
For larger datasets, use distributed training if you have multiple GPUs, but that’s beyond a single 4070 Ti.
Common issues: If PyTorch doesn’t detect the GPU, reinstall drivers/CUDA. Ensure no other processes are using the GPU.

References

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