So basically, we’re talking about a way to make large language models (LLMs) more efficient by using less memory and fewer calculations during training and inference.

First off, what an LLM is. It’s essentially a fancy algorithm that can understand natural language like humans do. But these algorithms are really big and take up a lot of space on your computer or server. That’s where LoRA comes in it stands for “Learned Optimizations with AdaRounding” and it allows us to compress the weights (the numbers that make up the algorithm) without losing too much accuracy.

Now, quantization. This is a technique used to reduce the number of bits needed to represent each weight in memory or during calculations. By using fewer bits, we can save space and speed things up. But there are trade-offs if we use too few bits, we might lose accuracy. That’s where AQLM (Adaptive Quantization with Linear Mapping) comes in it allows us to find the right balance between memory usage and accuracy by adjusting the number of bits used for each weight based on its importance.

So how does all this work together? Well, we start by loading our LLM into memory (which can be a pain because they’re so big). Then we apply LoRA to compress the weights without losing too much accuracy. Next, we quantize those compressed weights using AQLM to save even more space and speed things up during training and inference. And that’s it! We now have an efficient LLM that can understand natural language like a human would.

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

# Import necessary libraries
from transformers import AutoModelForCausalLM, Trainer, TrainingArguments
import bitsandbytes as bnb

# Load our pretrained LLM and apply LoRA compression to the weights
model = AutoModelForCausalLM.from_pretrained("llama-2-7b") # Load the pretrained LLM model
lora_config = {
    "inference_mode": "fp16",  # Use half precision floating point numbers for faster inference
    "ranks": [4, 8],          # Compress the weights using LoRA with a rank of 4 or 8 (lower is better)
}
lora = bnb.LoRA(model=model, lora_config=lora_config) # Apply LoRA compression to the model weights for efficient storage and faster training

# Load our training data and set up our Trainer object to fine-tune the model on it
train_dataset = ... # Load the training dataset
eval_dataset = ... # Load the evaluation dataset
training_args = TrainingArguments(output_dir="./outputs", num_train_epochs=3, per_device_train_batch_size=16) # Set up training arguments, including the output directory, number of training epochs, and batch size
trainer = Trainer(model=lora, train_dataset=train_dataset, eval_dataset=eval_dataset, args=training_args) # Set up the Trainer object with the compressed model, training and evaluation datasets, and training arguments

# Train the model on our data using the Trainer object and save it to disk for later use
trainer.train() # Train the model using the Trainer object
# The trained model will be saved to the specified output directory for later use

And that’s basically how AQLM quantization works with LoRA finetuning! It might sound complicated at first, but once you break it down into simpler terms, it makes a lot more sense.