Finetuning Llama 3 with Odds Ratio Desire Optimization

Introduction

Massive Language Fashions are sometimes educated quite than constructed, requiring a number of steps to carry out nicely. These steps, together with Supervised High quality Tuning (SFT) and Desire Alignment, are essential for studying new issues and aligning with human responses. Nonetheless, every step takes a big period of time and computing assets. One answer is the Odd Ratio Desire Optimization (ORPO), which mixes SFT and Desire Tuning in a single step. This information will discover ORPO and its potential to scale back the time taken to coach Massive Language Fashions.

Studying Goals

• Perceive the standard movement of coaching a Massive Language Mannequin (LLM), together with pretraining, supervised fine-tuning, and choice alignment.
• Establish completely different coaching and fine-tuning strategies for LLMs, similar to supervised fine-tuning and choice optimization (e.g., PPO, DPO, ORPO).
• Clarify the idea of Odds Ratio Desire Optimization (ORPO) and its function in lowering coaching time and computational assets by combining supervised fine-tuning and choice optimization in a single step.
• Describe the important thing elements of ORPO, together with the percentages ratio time period within the coaching loss and its integration with supervised fine-tuning.
• Discover ways to put together knowledge for finetuning an LLM with ORPO, together with knowledge formatting and preprocessing steps.
• Perceive the method of loading and coaching an LLM with ORPO, together with mannequin loading, patching the DPOTrainer, and initiating the coaching course of.
• Consider the effectiveness of ORPO in enhancing the effectivity and coherence of LLMs by aligning them extra intently with human preferences.

This text was revealed as part of the Knowledge Science Blogathon.

Typical Circulate of LLM Coaching

• Pretraining:
  • Massive Language Fashions are pretrained on a big corpus of textual content knowledge like Wikipedia.
  • That is unsupervised coaching the place the mannequin learns about phrase sequences and their possibilities.
• Instruction Tuning:
  • The mannequin is educated to comply with directions offered within the knowledge.
  • Knowledge contains directions and their corresponding solutions.
  • This coaching allows the mannequin to reply appropriately to consumer prompts, performing like a chat mannequin.
• Supervised High quality-Tuning:
  • LLM is educated on domain-specific or task-specific knowledge.
  • Instance: fine-tuning to masks Personally Identifiable Data (PII) knowledge.
  • Knowledge accommodates each masked and unmasked variations of textual content, permitting the mannequin to be taught the duty.
• Alignment-Tuning or Desire Alignment:
  • Geared toward aligning mannequin responses to generate accountable and clear solutions.
  • Desire optimization strategies embody PPO (Coverage Desire Optimization), DPO (Direct Desire Optimization), and ORPO (Odds Ratio Desire Optimization).

So we see right here that there are completely different fine-tune phases of an LLM. Every fine-tuning step consumes a variety of time and the bigger the information, the extra the coaching time for the LLM. Primarily the Supervised High quality-Tuning and the Desire Alignment, being carried out as separate steps, devour a variety of coaching time.

Introduction to ORPO

ORPO aka Odds Ratio Desire Optimization goals to scale back each the coaching time and the assets required throughout the Desire Optimization. It does this by combining each the Supervised High quality-Tuning and the Desire Optimization in a single step. ORPO removes the necessity for using a reward mannequin, which is mostly utilized in different Desire Algorithms just like the DPO and the PPO. ORPO believes that the SFT is highly effective sufficient to converge to steer the mannequin to chosen responses from the rejected responses. The components for the brand new loss could be seen under:

The Odds Ratio time period in ORPO is used to calculate the probability of a mannequin producing an output sequence y given an enter sequence x. This worth signifies that the mannequin is n occasions extra more likely to generate the sequence y than not. The chances ratio of chosen responses over rejected responses measures the mannequin’s probability of producing chosen responses.

The log of this odds ratio is taken into account as a result of simply taking the ratio of uncooked possibilities of the chosen over the rejected will produce a really small worth. And eventually, an activation operate just like the sigmoid is utilized to this log of odds ratio. This ultimate equation known as the ORPO loss and this loss is added to the SFT loss. A tunable parameter lambda is launched for hyperparameter tuning.

The ORPOTrainer goals to scale back the mixed lack of Unfavorable Log Chance and ORPO loss by supervised fine-tuning the Massive Language Mannequin. This strategy focuses on the chosen response and strikes it away from rejected ones, eliminating the necessity for a further reward mannequin. This strategy considerably reduces computation assets for choice tuning and align tuning, thereby lowering coaching and tuning time for Massive Language Fashions.

Finetuning Llama 3 with ORPO – Knowledge Preparation

We’ll now proceed with steps of fine-tuning llama 3 with ORPO.

Step1: Putting in Libraries

On this part, we are going to finetune the newly launched Llama 3 with the ORPO. For this, we can be working with the Kaggle Pocket book and begin by putting in the next libraries.

!pip set up -U -q xformers --index-url https://obtain.pytorch.org/whl/cu121
!pip set up -q "unsloth[kaggle-new] @ git+https://github.com/unslothai/unsloth.git"
!pip set up -q datasets trl transformers speed up huggingface-cli wandb

• xformers: A library launched by Meta that permits us to work with versatile transformer components, thus permitting us to mix completely different components of LLMs.
• unsloth: This can be a library that we’ll be working with to coach the Llama 3. Unsloth is thought to hurry the coaching technique of Massive Language Fashions and scale back the GPU reminiscence consumption.
• datasets: A library from huggingface which we are going to work with to obtain a dataset to finetune on
• trl: A library from huggingface for coaching the Massive Language Fashions.
• transformers: We’ll work with this library to obtain the mannequin from huggingface.
• speed up: We want this to hurry up the GPU inference for the Massive Language Fashions.
• huggingface-cli: We want this library to login into huggingface to obtain the llama-3 mannequin as a result of llama-3 requires authentication to make use of it.

Step2: Check in HuggingFace Account

To work with the Meta Mannequin, first, we have to settle for their phrases and circumstances. Go to this hyperlink, check in together with your HuggingFace account, and settle for their settlement coverage. After this, we are going to log in to our HuggingFace account via the huggingface-cli command.

Step3: Dataset Loading and Knowledge Preprocessing

We’ll begin with dataset loading and knowledge preprocessing half. First, we have to log in with our huggingface account so we will entry and obtain Meta’s Llama 3 8B mannequin and the tokenizer. For this, the code can be:

!huggingface-cli login --token $you_api_key

Right here within the above command, present your HuggingFace token. This token could be obtained from the HuggingFace web site. Working this command will log us into our HuggingFace account and we see the next output:

Step4: Obtain the Mannequin

Subsequent, we are going to obtain the mannequin. The code for this can be:

from transformers import AutoTokenizer

base_model = "meta-llama/Meta-Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(base_model)

• We import the AutoTokenizer Class from the transformers library.
• Right here we first outline the mannequin title within the variable base_model.
• Then we name the AutoTokenizer.from_petrained() operate and cross it the base_model variable.

Working the code will obtain the Llama3 Tokenizer from the Meta HuggingFace repository. This tokenizer is critical to use the chat format of Llama 3 for the dataset that we’ll be working with and to tokenize them.

Step5: Finetune Llama 3

Now we are going to obtain the dataset that we’ll finetune our Llama 3 on. The code for this can be:

from datasets import load_dataset

dataset_name = "jondurbin/truthy-dpo-v0.1"
dataset = load_dataset(dataset_name)

• Right here we import the load_dataset class from the datasets library.
• Then we offer the trail for our dataset to the dataset_name variable.
• This dataset_name variable is given to the load_dataset() operate, which downloads the dataset from the HuggingFace hub.

Working this code will obtain the information “truthy-dpo-v0.1” from the huggingface and retailer it within the variable dataset. A couple of rows from the dataset could be seen under:

We can be working with the 4 columns within the dataset. These are the system, immediate, chosen, and rejected columns. The system and the immediate columns comprise the system message and the consumer immediate. The chosen column accommodates the chosen response and the rejected column accommodates the rejected response.

Step6: Creating Columns

We have to create new chosen and rejected columns the place every of those columns accommodates each the system message, the consumer immediate, and the chosen or the rejected response. The code for this may be seen under:

def format_chat_template(row):
    message_chosen = [{"role":"system","content":row['system']},
    {"function":"consumer","content material":row['prompt']},
    {"function":"assistant","content material":row['chosen']}]
    
    message_rejected = [{"role":"system","content":row['system']},
    {"function":"consumer","content material":row['prompt']},
    {"function":"assistant","content material":row['rejected']}]
    
    immediate = row['system'] + '/n' + row['prompt']
    
    row["chosen"] = tokenizer.apply_chat_template(message_chosen, tokenize=False)
    row["rejected"] = tokenizer.apply_chat_template(message_rejected, tokenize=False)
    row['prompt'] = immediate
    
    return row

The offered code defines a operate known as format_chat_template that takes a row of knowledge as enter and returns a modified model of that row.

Contained in the operate, two lists are created:

• message_chosen: This record represents a chat message with the assistant message because the “chosen” response. It accommodates three dictionaries, every representing a message from both the system, the consumer, or the assistant.
• message_rejected: This record represents a chat message with the assistant message because the “rejected” response. Just like – message_chosen, it even accommodates three dictionaries representing messages from the system, consumer, and assistant.
• The subsequent line creates a string known as immediate by concatenating the system and immediate columns from the enter row. This string represents the system’s message adopted by the consumer’s immediate.
• The operate then applies a technique known as apply_chat_template from a tokenizer object (tokenizer) to the message_chosen and message_rejected lists. This operate takes in these messages and applies formatting to them primarily based on the chat format that the Llama 3 takes.
• Right here we assign tokenizer=False as a result of we’d like again the textual content, not the tokens.
• Lastly, the modified row is returned as output.

Step7: Making use of Perform to Dataset

Now, we are going to apply this operate to the Dataset that we now have simply downloaded. For this, we work with the next code:

import os

dataset = dataset.map(
    format_chat_template,
    num_proc= os.cpu_count(),
)

Right here, we map the operate that we now have simply outlined, to the dataset that we now have simply downloaded from HuggingFace. To map it, we name the map operate of the dataset object and cross it the operate for formatting and the CPU rely, in order that execution could be finished in parallel. Working this code will modify the information throughout the dataset with the required formatting for the coaching course of.

Lastly, we’re finished with the information pre-processing half. Subsequent, we are going to obtain the Llama-3 8 Billion mannequin and prepare it with this dataset.

Mannequin Loading and Coaching

On this part, we are going to obtain the mannequin and begin the coaching course of.

Step1: Downloading the Mannequin

First, we are going to start with downloading the mannequin. The code for this can be:

from unsloth import FastLanguageModel
import torch

max_seq_length = 2048
dtype = None 
load_in_4bit = True 

mannequin, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/llama-3-8b-bnb-4bit", 
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
    token = secret_value_0, 
)

• We begin by importing FastLanguageModel from the unsloth library and PyTorch.
• Then we outline 3 variables, the max_seq_length, the utmost tokens which are to be generated by the mannequin, dtype, which we give None for auto-detection and load_in_4bit, the place the True implies that we want to quantize to 4-bit.
• Now, we name the .from_pretrained() from FastLanguageModel(), and to this, we cross.

Step2: Quantization

Working the above code will obtain the llama-3 8b mannequin and quantize it to a 4-bit format and it’ll additionally fetch the related tokenizer.

mannequin = FastLanguageModel.get_peft_model(
    mannequin,
    r = 16, 
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 16,
    lora_dropout = 0, 
    bias = "none",    
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = False,  
    loftq_config = None, 
)

Now, we attempt to get the PEFT model of our mannequin. For this, we name the .get_peft_model() operate of the FastLanguageModel class. To this, we cross the next parameters

• mannequin: That is the mannequin that we now have downloaded simply now.
• rank: It’s the rank of the LoRA matrix. We offer a price of 16 for it.
• target_modules: This can be a record of goal modules for which we want to create the LoRA on. We can be taking all the eye layers and the linear layers.
• alpha: That is the LoRA scaling issue. We set this scaling issue to 16, it’s often equal to or double the dimensions of rank.
• lora_dropout: Defines the share of dropping of neurons. Unsloth presently doesn’t assist dropout, therefore it’s set to 0.
• bias: Unsloth doesn’t assist bias phrases, therefore it’s set to none.
• use_rslora: Wether to allow Rank Stabilized Lora or Not? Set to False.
• loftq_config: That is set to none as a result of we shouldn’t have any LoftQ config.

Working this code will create the LoRA Adapters, which we can be coaching with the dataset that we now have downloaded.

Step3: Patching DPOTrainer

Let’s begin by patching the DPOTrainer.

from unsloth import PatchDPOTrainer

PatchDPOTrainer()

The unsloth library has not but launched an official implementation for ORPO Coach. To deal with this, the PatchDPOTrainer is imported, which can patch the present DPOTrainer and ORPOTrainer from the HuggingFace trl library, enhancing its pace and reminiscence effectivity.

from trl import ORPOConfig, ORPOTrainer


orpo_trainer = ORPOTrainer(
    mannequin = mannequin,
    args = ORPOConfig(
        output_dir="/kaggle/working/mannequin",
        max_prompt_length=512,
        max_length=1024,
        logging_steps=1,
        per_device_train_batch_size=2,
        remove_unused_columns=False,
        gradient_accumulation_steps=2,
        optim="paged_adamw_8bit",
        lr_scheduler_type="cosine",
        gradient_checkpointing=True,
        beta=0.1,
        num_train_epochs=1,
        fp16=True,
        do_eval=False,
    ),
    train_dataset = dataset["train"],
    tokenizer = tokenizer,
)

We begin by importing the ORPOTrainer and ORPOConfig from the trl library. Then we set the parameters contained in the ORPOTrainer.

These embody:

• output_dir: Right here we specify the output listing the place to retailer the LoRA adapters.
• max_prompt_length: Defines the utmost immediate size. That is set to 512
• max_length: This defines the utmost size of the sequence. It’s set to 1024
• logging_steps: We set this to 1, so we will see the logs, just like the coaching loss each single epoch
• per_device_train_batch_size: It’s the variety of batches that we’ll be coaching per GPU, and we set this to 2.
• gradient_accumulation_steps: We set this to 2, accumulating gradients each 2 steps earlier than updating them.
• remove_unused_columns: Will take away the null columns if current within the dataset if set to True
• optim: Right here we outline the optimizer we wish to work with whereas coaching. We’ll work with the paged_adamw_8bit optimizer.
• lr_scheduler_type: This tells the kind of studying fee scheduler to work with. We go together with cosine
• beta: It’s the hyperparameter for the ORPO loss. 0.1 is the beneficial worth.
• We set the gradient_checkpointing to True.
• We set fp16 to True, as a result of the GPU we’re engaged on will assist it, and since we shouldn’t have any analysis knowledge, we set the do_eval=False and we prepare for 1 full epoch.

So, we cross this ORPOConfig, which is the coaching argument to the ORPOTrainer together with the dataset and the tokenizer. Working this code will create the ORPOTrainer and is able to begin the coaching step.

Step4: Provoke Coaching

We’ll provoke the coaching with the next code.

orpo_trainer.prepare()

Calling the .prepare() on the orpo_trainer will begin the coaching course of. We are able to see within the pic that we get the coaching metrics just like the coaching loss, rewards/chosen, rewards/rejected, and so forth. There are a complete of 247 steps that have been taken to finish one epoch of coaching on all the dataset. Within the second pic, we will see that because the variety of steps elevated, the coaching loss has come down.

The odds_ratio within the third image fluctuates, however total will increase with the variety of steps. This means the next likelihood of producing chosen responses in comparison with rejected ones, permitting for alignment tuning on a Massive Language Mannequin utilizing ORPO or Odds Ratio Desire Optimization.

Conclusion

Odds Ratio Desire Optimization (ORPO) presents a promising strategy to effectively fine-tune massive language fashions like Llama 3 by combining Supervised High quality-Tuning and Desire Optimization in a single step. By introducing an odds ratio time period within the coaching loss, ORPO successfully balances the number of most well-liked outputs over rejected ones, all whereas eliminating the necessity for a separate reward mannequin. This streamlined strategy not solely reduces the coaching time and computational assets required but in addition results in a extra coherent and environment friendly mannequin. ORPO demonstrates its potential in aligning language fashions extra intently with human preferences, optimizing their skill to generate high-quality, related responses in varied purposes.

Key Takeaway

• ORPO combines Supervised High quality-Tuning and Desire Optimization right into a single coaching step, considerably lowering the time and assets required to coach massive language fashions.
• By incorporating an odds ratio time period within the coaching loss, ORPO guides the mannequin in the direction of most well-liked responses whereas avoiding rejected ones, thus enhancing the standard of generated textual content.
• ORPO has the potential to use to numerous massive language fashions, similar to Llama 3, showcasing its potential to boost the coaching course of for a variety of NLP duties and purposes.
• Integrating ORPO into current coaching workflows turns into simple utilizing libraries similar to unsloth and trl, thereby streamlining the coaching course of.
• The mixture of destructive log-likelihood and ORPO loss permits the mannequin to converge towards extra appropriate responses primarily based on the chosen and rejected sequences.

Incessantly Requested Questions

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