Supervised Fine Tuning of Llama v2 using TRL’s SFTTrainer with QLoRA

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It’s basically taking an already trained model (in this case, Llama v2) and fine-tuning it on a specific task or dataset using labeled data. This helps the model learn new information that wasn’t included in its original training set.

Now TRL’s SFTTrainer. It stands for “Supervised Fine Tuning Trainer” and is used to fine-tune pretrained language models like Llama v2 on specific tasks or datasets using labeled data. This helps the model learn new information that wasn’t included in its original training set, which can improve its performance on those tasks.

QLoRA comes into play here too. It stands for “Quantization-aware Low-Rank Adapter Tuning” and is a technique used to reduce the memory footprint of large language models during finetuning without sacrificing performance. Essentially, it quantizes (reduces the number of bits) the pretrained model to 4 bits and attaches small trainable adapter layers that are fine-tuned while using the frozen quantized model for context. This helps improve efficiency and reduce memory usage when working with large language models like Llama v2.

So, how does this all work in practice? Let’s say we want to fine-tune Llama v2 on a specific task or dataset using labeled data. We would first load the pretrained model (in this case, Llama v2) and set up our environment with TRL’s SFTTrainer. Then, we would define our training parameters such as batch size, learning rate, etc., and start fine-tuning on our chosen task or dataset using labeled data.

Here’s an example of what this might look like in code:

# Import necessary libraries
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

# Load pretrained model and set up environment with TRL's SFTTrainer
base_model = AutoModelForCausalLM.from_pretrained("llama-2-7b") # Load the Llama v2 model
config = base_model.config # Get the model's configuration
config.use_cache = False # Disable caching to save memory

# Define training parameters such as batch size, learning rate, etc.
batch_size = 16 # Set the batch size for training
learning_rate = 5e-4 # Set the learning rate for the optimizer
num_epochs = 3 # Set the number of training epochs

# Set up TRL's SFTTrainer with QLoRA for fine-tuning on our chosen task or dataset using labeled data
bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16) # Configure the quantization settings for the model
base_model = AutoModelForCausalLM.from_pretrained("llama-2-7b", quantization_config=bnb_config, device_map={"": 0}, trust_remote_code=True, use_auth_token=True) # Load the model with the specified quantization settings and device mapping
trainer = TRLTrainer(model=base_model, args=None, train_dataset=train_data, eval_dataset=eval_data, data_collator=DataCollatorForLanguageModeling(language_model=base_model.config.decoder_start_token_id), compute_metrics=compute_metrics) # Set up the TRLTrainer with the loaded model, training and evaluation datasets, and data collator
trainer.fit() # Start the fine-tuning process

And that’s it! With this setup, we can fine-tune Llama v2 on our chosen task or dataset using labeled data with QLoRA for improved efficiency and reduced memory usage.

If you want to learn more about this technique or any other computer science research topic, feel free to reach out and let us know.

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