Practical Business Solutions for Fine-Tuning AI Models
Introduction
This guide outlines how to fine-tune NVIDIA’s NV-Embed-v1 model using the Amazon Polarity dataset. By employing LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning) from Hugging Face, we can adapt the model efficiently on low-VRAM GPUs without changing all its parameters.
Steps to Implement Fine-Tuning
- Authenticate with Hugging Face to access NV-Embed-v1
- Load and configure the model efficiently
- Apply LoRA fine-tuning using PEFT
- Preprocess the Amazon Polarity dataset for training
- Optimize GPU memory usage
- Train and evaluate the model on sentiment classification
Step-by-Step Implementation
By following these steps, you will successfully fine-tune the NV-Embed-v1 model for binary sentiment classification.
1. Authentication
from huggingface_hub import login
login() # Enter your Hugging Face token when prompted2. Load the Model
import os
HF_TOKEN = "...." # Replace with your actual token
os.environ["HF_TOKEN"] = HF_TOKEN
import torch
from transformers import AutoModel, AutoTokenizer
MODEL_NAME = "nvidia/NV-Embed-v1"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
model = AutoModel.from_pretrained(MODEL_NAME, device_map="auto", torch_dtype=torch.float16, token=HF_TOKEN)3. Configure LoRA
from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["self_attn.q_proj", "self_attn.v_proj"],
lora_dropout=0.1,
bias="none",
task_type="FEATURE_EXTRACTION",
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()4. Load and Tokenize Dataset
from datasets import load_dataset
dataset = load_dataset("amazon_polarity")
def tokenize_function(examples):
return tokenizer(examples["content"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)5. Set Up Training Parameters
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
num_train_epochs=1,
save_strategy="epoch",
save_total_limit=1,
logging_dir="./logs",
logging_steps=10,
fp16=True,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
)
trainer.train()6. Save the Fine-Tuned Model
model.save_pretrained("./fine_tuned_nv_embed")
tokenizer.save_pretrained("./fine_tuned_nv_embed")
print("Model and tokenizer saved successfully.")Conclusion
By the end of this tutorial, you will have a fine-tuned NV-Embed-v1 model that is optimized for sentiment analysis. This process demonstrates how efficient fine-tuning techniques can be applied to real-world NLP tasks, enabling affordable adaptation of large models for various applications like product review classification and AI-driven recommendation systems.
Next Steps
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