Fine-Tune Llama 3 with LoRA on AWS EC2

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This example demonstrates how to fine-tune a Meta Llama 3 model with LoRA on AWS EC2 using Runhouse. See also our related post for Llama 2 fine-tuning.

Make sure to sign the waiver on the Hugging Face model page so that you can access it.

Set up credentials and dependencies

Install the required dependencies:

$ pip install "runhouse[aws]"

We'll be launching an AWS EC2 instance via SkyPilot, so we need to make sure our AWS credentials are set up:

$ aws configure $ sky check

To download the Llama 3 model on our EC2 instance, we need to set up a Hugging Face token:

$ export HF_TOKEN=<your huggingface token>

Create a model class

We import runhouse, the only required library we need locally:

import runhouse as rh

Next, we define a class that will hold the various methods needed to fine-tune the model. You'll notice this class inherits from rh.Module. This is a Runhouse class that allows you to run code in your class on a remote machine.

Learn more in the Runhouse docs on functions and modules.

DEFAULT_MAX_LENGTH = 200 class FineTuner(rh.Module): def __init__( self, dataset_name="Shekswess/medical_llama3_instruct_dataset_short", base_model_name="meta-llama/Meta-Llama-3-8B-Instruct", fine_tuned_model_name="llama-3-8b-medical", ): super().__init__() self.dataset_name = dataset_name self.base_model_name = base_model_name self.fine_tuned_model_name = fine_tuned_model_name self.tokenizer = None self.base_model = None self.fine_tuned_model = None self.pipeline = None def load_base_model(self): import torch from transformers import AutoModelForCausalLM, BitsAndBytesConfig # configure the model for efficient training quant_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=False, ) # load the base model with the quantization configuration self.base_model = AutoModelForCausalLM.from_pretrained( self.base_model_name, quantization_config=quant_config, device_map={"": 0} ) self.base_model.config.use_cache = False self.base_model.config.pretraining_tp = 1 def load_tokenizer(self): from transformers import AutoTokenizer self.tokenizer = AutoTokenizer.from_pretrained( self.base_model_name, trust_remote_code=True ) self.tokenizer.pad_token = self.tokenizer.eos_token self.tokenizer.padding_side = "right" def load_pipeline(self, max_length: int): from transformers import pipeline # Use the new fine-tuned model for generating text self.pipeline = pipeline( task="text-generation", model=self.fine_tuned_model, tokenizer=self.tokenizer, max_length=max_length, ) def load_dataset(self): from datasets import load_dataset return load_dataset(self.dataset_name, split="train") def load_fine_tuned_model(self): import torch from peft import AutoPeftModelForCausalLM if not self.new_model_exists(): raise FileNotFoundError( "No fine tuned model found on the cluster. " "Call the `tune` method to run the fine tuning." ) self.fine_tuned_model = AutoPeftModelForCausalLM.from_pretrained( self.fine_tuned_model_name, device_map={"": "cuda:0"}, # Loads model into GPU memory torch_dtype=torch.bfloat16, ) self.fine_tuned_model = self.fine_tuned_model.merge_and_unload() def new_model_exists(self): from pathlib import Path return Path(f"~/{self.fine_tuned_model_name}").expanduser().exists() def training_params(self): from transformers import TrainingArguments return TrainingArguments( output_dir="./results_modified", num_train_epochs=1, per_device_train_batch_size=4, gradient_accumulation_steps=1, optim="paged_adamw_32bit", save_steps=25, logging_steps=25, learning_rate=2e-4, weight_decay=0.001, fp16=False, bf16=False, max_grad_norm=0.3, max_steps=-1, warmup_ratio=0.03, group_by_length=True, lr_scheduler_type="constant", report_to="tensorboard", ) def sft_trainer(self, training_data, peft_parameters, train_params): from trl import SFTTrainer # Set up the SFTTrainer with the model, training data, and parameters to learn from the new dataset return SFTTrainer( model=self.base_model, train_dataset=training_data, peft_config=peft_parameters, dataset_text_field="prompt", # Dependent on your dataset tokenizer=self.tokenizer, args=train_params, ) def tune(self): import gc import torch from peft import LoraConfig if self.new_model_exists(): return # Load the training data, tokenizer and model to be used by the trainer training_data = self.load_dataset() if self.tokenizer is None: self.load_tokenizer() if self.base_model is None: self.load_base_model() # Use LoRA to update a small subset of the model's parameters peft_parameters = LoraConfig( lora_alpha=16, lora_dropout=0.1, r=8, bias="none", task_type="CAUSAL_LM" ) train_params = self.training_params() trainer = self.sft_trainer(training_data, peft_parameters, train_params) # Force clean the pytorch cache gc.collect() torch.cuda.empty_cache() trainer.train() # Save the fine-tuned model's weights and tokenizer files on the cluster trainer.model.save_pretrained(self.fine_tuned_model_name) trainer.tokenizer.save_pretrained(self.fine_tuned_model_name) # Clear VRAM from training del trainer del train_params del training_data self.base_model = None gc.collect() torch.cuda.empty_cache() print("Saved model weights and tokenizer on the cluster.") def generate(self, query: str, max_length: int = DEFAULT_MAX_LENGTH): if self.fine_tuned_model is None: # Load the fine-tuned model saved on the cluster self.load_fine_tuned_model() if self.tokenizer is None: self.load_tokenizer() if self.pipeline is None or max_length != DEFAULT_MAX_LENGTH: self.load_pipeline(max_length) # Format should reflect the format in the dataset_text_field in SFTTrainer output = self.pipeline( f"<|start_header_id|>system<|end_header_id|> Answer the question truthfully, you are a medical professional.<|eot_id|><|start_header_id|>user<|end_header_id|> This is the question: {query}<|eot_id|><|start_header_id|>assistant<|end_header_id|>" ) return output[0]["generated_text"]

Define Runhouse primitives

Now, we define code that will run locally when we run this script and set up our Runhouse module on a remote cluster. First, we create a cluster with the desired instance type and provider. Our instance_type here is defined as A10G:1, which is the accelerator type and count that we need. We could alternatively specify a specific AWS instance type, such as p3.2xlarge or g4dn.xlarge.

Learn more in the Runhouse docs on clusters.


Make sure that all the following code runs within a if __name__ == "__main__": block, as shown below. Otherwise, the script code will run when Runhouse attempts to run code remotely. We'll break up the block in this example to improve readability.

if __name__ == "__main__": cluster = rh.cluster( name="rh-a10x", instance_type="A10G:1", memory="32+", provider="aws", ).up_if_not()

Next, we define the environment for our module. This includes the required dependencies that need to be installed on the remote machine, as well as any secrets that need to be synced up from local to remote. Passing huggingface to the secrets parameter will load the Hugging Face token we set up earlier.

Learn more in the Runhouse docs on envs.

env = rh.env( name="ft_env", reqs=[ "torch", "tensorboard", "scipy", "peft==0.4.0", "bitsandbytes==0.40.2", "transformers==4.31.0", "trl==0.4.7", "accelerate", ], secrets=["huggingface"], # Needed to download Llama 3 from Hugging Face )

Finally, we define our module and run it on the remote cluster. We construct it normally and then call get_or_to to run it on the remote cluster. Using get_or_to allows us to load the exiting Module by the name llama3-medical-model if it was already put on the cluster. If we want to update the Module each time we run this script, we can use to instead of get_or_to.

Note that we also pass the env object to the get_or_to method, which will ensure that the environment is set up on the remote machine before the module is run.

fine_tuner_remote = FineTuner().get_or_to( cluster, env=env, name="llama3-medical-model" )

Fine-tune the model on the cluster

We can call the tune method on the model class instance as if it were running locally. This will run the function on the remote cluster and return the response to our local machine automatically. Further calls will also run on the remote machine, and maintain state that was updated between calls, like self.fine_tuned_model. Once the base model is fine-tuned, we save this new model on the cluster and use it to generate our text predictions.


For this example we are using a small subset of 1000 samples that are already compatible with the model's prompt format.


Generate Text

Now that we have fine-tuned our model, we can generate text by calling the generate method with our query:

query = "What's the best treatment for sunburn?" generated_text = fine_tuner_remote.generate(query) print(generated_text)