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Why fine-tuning is key to AI adoption

In the field of AI, pre-training of large models is like building the foundation of a skyscraper, while fine-tuning is like installing customized windows and doors on the building to make it more suitable for your needs. Whether it is ChatGPT, GitHub Copilot, or the recently popular DeepSeek, fine-tuning technology is indispensable behind them.

Fine-tuning is not something mysterious. It is a technique that uses existing large models to quickly adapt to specific tasks with a small amount of data and computing resources. In simple terms, it is to "stand on the shoulders of giants" and build your own AI tools with lower costs and higher efficiency.

This article will give you an in-depth understanding of the core principles of fine-tuning, and show you how to use LoRA for efficient fine-tuning through code examples. Whether you are a technical novice or an experienced developer, you can find inspiration in it.

1. What is fine-tuning? Why do we need fine-tuning?

1. Basic Concepts of Fine-tuning

Fine-tuning refers to the process of further training a large model that has already been trained for a specific task or scenario. Compared with training a model from scratch, fine-tuning can significantly reduce the time, computing resources, and data requirements.

For example, suppose you have a general language model that can answer a variety of questions, but is not familiar with the professional terms in the medical field. At this time, you can retrain the model with a small amount of medical-related data through fine-tuning to make it a "medical expert."

unsetunset2. What problems can fine-tuning solve?unsetunset

• Enhance capabilities in specific fields : such as sentiment classification, dialogue generation, API orchestration, etc.
• Reduce hallucinations : Make the content generated by the model more accurate and reliable.
• Improve consistency : Maintain high quality even when content is generated differently each time.
• Lower costs : Compared to training from scratch, fine-tuning requires much less computing resources and data.
• Avoid data breaches : Fine-tuning can be done on-premises or in a private cloud environment to protect sensitive data.

In short: fine-tuning the four major functions

1. Knowledge implantation: Let AI learn the professional terms in the Pharmacopoeia
2. Thinking correction: Avoid the illusion that Qin Shi Huang used an iPhone
3. Personalized customization: Clone Musk's Twitter style in 1 hour
4. Cost reduction: 7 billion parameter model outperforms trillion-dollar base

unsetunset3. Practical Application of Fine-tuningunsetunset

• Simulate the way a specific person speaks : For example, character.ai can fine-tune the model to imitate a historical figure or celebrity.
• Support multiple languages : Let the model learn to handle minority languages ​​or dialects.
• Industry-specific models : such as medical big models, legal big models, financial risk control models, etc.
• Artistic creation : For example, the "alchemy" tutorial in Stable Diffusion is based on fine-tuning technology.

2. Core steps and technical details of fine-tuning

unsetunset1. Data preparation: high quality + diversityunsetunset

The first step of fine-tuning is to prepare high-quality data. As the old saying goes, “Garbage in, garbage out”. If you feed your model garbage data, the output will also be bad.

• High quality : Ensure that data is accurately labeled and generated manually instead of relying on other models.
• Diversity : Cover as many scenarios as possible to avoid model overfitting.
• Quantity requirement : Usually 50-100 pieces of data are needed to see significant results, and around 1,000 pieces of data can achieve good results.

The data format is usually as follows

The dataset is usually in JSON format, and each data record consists of three main fields: instruction, input, and output.

{
"instruction": "Introduction to the disease",
"input": "Please explain what acute gastroenteritis is",
"output": "Acute gastroenteritis is an acute inflammation of the gastrointestinal mucosa caused by various reasons. It can be caused by various types of pathogenic microorganisms such as bacteria, viruses, and parasites. In addition, non-infectious factors such as absorbing irritating foods, drugs, alcohol, or food allergies may also cause acute gastroenteritis. Usually, the disease occurs more in summer and autumn, and children or people with poor resistance are more susceptible.
Common clinical symptoms of acute gastroenteritis include nausea, vomiting, abdominal pain and diarrhea. In severe cases, dehydration, electrolyte imbalance and life-threatening conditions may occur. The disease can be transmitted through contact with carriers or eating and drinking contaminated food and water, or through direct contact between people. ",
},
{
"instruction": "Introduction to the disease",
"input": "Please explain what hypertension is",
"output": "Hypertension (also known as high blood pressure) is a phenomenon in which the pressure on the blood vessel wall caused by blood flow is continuously higher than normal. According to the "Chinese Hypertension Clinical Practice Guidelines", the diagnostic criteria for hypertension in adults are systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 80 mmHg. The causes of hypertension are mostly genetic factors and unhealthy lifestyles, such as high-salt diet, excessive drinking, long-term mental stress and lack of physical activity.",
},
]

Each data is a JSON object containing the following fields:

• instruction(instruction)

• Describes the type of task or the task goal that the model needs to accomplish.
• This field defines the role or function of the model in the current conversation. For example, in the example,instructionThe value of"Introduction to Disease", indicating that the model’s task is to “introduce the definition and related information of a certain disease.”

• input(enter)

• Provide specific inputs or questions from which the model can generate outputs.
• In the example,inputThe values ​​of"Please tell me what acute gastroenteritis is."and"Please tell me what hypertension is.", this is a specific question asked by the user.

• output(Output)

• The target answer or response content that the model needs to generate.
• This field contains theinputThe corresponding correct answer. For example,"Please tell me what acute gastroenteritis is.",outputA detailed description of acute gastroenteritis is provided.

Characteristics of the dataset

• Clearly structured: Each piece of data follows a unified format, making it easier for the model to understand and learn.instructionThe task type is clarified.inputProvides specific context,outputThe correct answer was given.

• Task-oriented datasets are task-centric.instructionThe fields specify the type of tasks that the model needs to complete. This design makes the dataset suitable for a variety of application scenarios, such as question-answering systems, knowledge popularization, and medical consultation.

• High-quality annotationoutputThe content of a field is usually high-quality text that has been manually edited or professionally reviewed. This high-quality annotation can help the model better learn knowledge in a specific field.

• Although only the "introduce disease" task is shown in the example, the actual dataset can beinstructionvalue to support multiple task types. For example:

• instruction: "Translate sentence",input: "Translate 'hello' into English",output: "Hello".
• instruction: "Generate Code",input: "Write a Python function to calculate the Fibonacci sequence",output: Python code snippet.

unsetunset2. Parameter settings: learning rate, LoRA configuration, etc.unsetunset

During the fine-tuning process, parameter settings are crucial. Here are a few key points:

• Learning rate : controls the speed of model update. Too large a learning rate may lead to oscillation, while too small a learning rate may lead to slow convergence.
• LoRA Configuration :
• r: The rank of the low-rank matrix is ​​usually set to 1~8, and the empirical value is 4.
• lora_alpha: Scaling factor, used to control the influence of the LoRA matrix on the original weights. The recommended initial value is 32.
• lora_dropout: A parameter to prevent overfitting, usually set to 0.01.

unsetunset3. Model selection: Advantages of LoRAunsetunset

LoRA (Low-Rank Adaptation) is an efficient fine-tuning method. Its core idea is to reduce the number of parameters that need to be updated by introducing a low-rank matrix. (Source: LoRA paper: LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS)

LoRA principle is specific

• The weight matrix of the original model is a high-dimensional matrix (such as 768×768), and directly adjusting all parameters will consume a lot of computing resources.
• LoRA decomposes it into two low-dimensional matrices (such as 768×4 and 4×768), which significantly reduces the computational cost.

Let's take a specific example:

• If the original matrix dimension of the model is 1000×1000, 1 million parameters need to be adjusted.
• With LoRA, only 1000×4 + 4×1000 = 8000 parameters need to be adjusted.

To use an analogy, it's like you have a Swiss Army knife with many tools (like scissors, screwdrivers, etc.), but when solving a specific task, you usually only need to use a few of them to complete most of the work. In this example, the model matrix is ​​like a Swiss Army knife. Although it is complex (full rank), you actually only need to use some simple tools (low rank).

That is to say, when fine-tuning, only those parameters that affect the specific task need to be adjusted. The original matrix dimension is high, assumingDimensional Matrix, the simplest way to adjust the matrix and keep the matrix data (for reuse) is to use matrix addition, adding aMatrix of dimensionsBut if the data is fine-tuned, it is still aThe number of parameters is large for a matrix of dimensionality. LoRA reduces the order of magnitude of parameters by expressing the latter as a low-rank decomposition.

For example, suppose the original weight matrixThe dimension isIf you fine-tune all parameters directly, you need to adjustparameters.Decomposed into two low-rank matricesand, significantly reducing the number of parameters.

Assumptionsis the rank of the low-rank matrix,The dimension is,The dimension isAt this point, the number of parameters that need to be adjusted is.ifIf is smaller (such as 4), the number of parameters is greatly reduced.

in

LoRA’s reusability

An important feature of LoRA is its reusability. Since LoRA does not change the parameters of the original model, it can be flexibly applied in multiple tasks or scenarios. For example, the model running on the mobile terminal can dynamically load the corresponding LoRA parameters according to different tasks, thereby greatly reducing the storage and running space requirements.

This efficiency makes it possible for even ordinary people to complete fine-tuning on free GPU resources such as Google Colab.

unsetunset4. Code Example: Fine-tuning DistilBERT with LoRAunsetunset

Below is a simple code example showing how to fine-tune a DistilBERT model with LoRA for the sentiment classification task of movie reviews.

• Initial model: https://huggingface.co/distilbert/distilbert-base-uncased
• Fine-tuning data: https://huggingface.co/datasets/stanfordnlp/imdb
• Python code:

# Install necessary libraries
!pip install datasets transformers peft

# Load the IMDB dataset
from  datasets  import  load_dataset
dataset = load_dataset( "stanfordnlp/imdb" )

# Load the initial model
from  transformers  import  AutoModelForSequenceClassification, AutoTokenizer
model_name =  "distilbert-base-uncased"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Data preprocessing
def  preprocess_function (examples) :
    return  tokenizer(examples[ 'text' ], truncation= True , padding= 'max_length' , max_length= 128 )

encoded_dataset = dataset.map(preprocess_function, batched= True )

# Configure LoRA
from  peft  import  LoraConfig, get_peft_model
lora_config = LoraConfig(
    task_type = "SEQ_CLS" ,
    r = 4 ,
    lora_alpha= 32 ,
    lora_dropout = 0.01 ,
    target_modules=[ "q_lin" ]
)
model = get_peft_model(model, lora_config)

# Train the model
from  transformers  import  Trainer, TrainingArguments
training_args = TrainingArguments(
    output_dir = "./results" ,
    evaluation_strategy= "epoch" ,
    learning_rate = 5e-5 ,
    per_device_train_batch_size= 16 ,
    num_train_epochs = 3 ,
    weight_decay = 0.01
)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=encoded_dataset[ "train" ],
    eval_dataset=encoded_dataset[ "test" ]
)
trainer.train()

# Test results
predictions = trainer.predict(encoded_dataset[ "test" ])
print(predictions.metrics)

This code shows how to fine-tune a 67M parameter DistilBERT model with LoRA, ultimately increasing the classification accuracy from 50% to 87%.

3. The Future and Challenges of Fine-tuning

1. The computing power threshold is gradually lowered

As computing power costs drop and fine-tuning technology advances, more and more companies and individuals are able to participate in AI development. For example, the success of DeepSeek proves that even without top-level GPUs, top performance can be achieved through algorithm optimization.

2. The importance of data quality

Although fine-tuning reduces the demand for data volume, the requirements for data quality are getting higher and higher. In the future, how to obtain high-quality and diverse data will become the key to the success of fine-tuning.

3. The rise of industry-specific models

As fine-tuning technology becomes more popular, more specialized AI models will emerge in all walks of life. These models will not only improve work efficiency, but also bring huge commercial value to enterprises.

4. Best Practices and Precautions for Fine-tuning

1. The importance of data quality

High-quality data is the key to successful fine-tuning. Even a small amount of data can achieve significant results as long as the quality is high enough. Therefore, when collecting data, you should try to ensure its accuracy, diversity, and representativeness.

2. Prevent overfitting

During fine-tuning, the model may overfit due to the small amount of data. To prevent this, you can optimize it by:

• Add regularization : such as L2 regularization or Dropout.
• Use cross-validation : Evaluate the generalization ability of the model by dividing the training set and validation set multiple times.
• Adjust hyperparameters : such as learning rate, batch size, etc.

unsetunset3. Evaluation after fine-tuningunsetunset

After fine-tuning, the model should be fully evaluated to ensure that its performance on the target task meets expectations. Common evaluation indicators include accuracy, F1 score, BLEU score, etc.

5. Practical application scenarios of fine-tuning

1. Sentiment Analysis

Sentiment analysis is one of the classic application scenarios of fine-tuning. Through fine-tuning, the model can better understand the emotional tendency in the text, and thus be used in areas such as public opinion monitoring and user feedback analysis.

2. Dialogue System

Fine-tuning can help the model better adapt to the conversation task and make the responses it generates more natural and coherent. For example, through fine-tuning, the model can be made to imitate the speaking style of a specific person, so that it can be used for virtual assistants or game characters.

unsetunset3. Multi-language supportunsetunset

Fine-tuning can also be used to expand the language support range of the model. For example, through fine-tuning, the model can learn to handle small languages ​​or dialects to meet the needs of globalization.

6. Future development and trends of fine-tuning

1. Automated fine-tuning

With the development of automated machine learning (AutoML), the fine-tuning process may become more intelligent and automated in the future. For example, the technical threshold of fine-tuning can be further lowered by automatically searching for the best hyperparameters and automatically selecting fine-tuning strategies.

2. Federated Learning and Privacy Protection

Federated learning is a distributed machine learning method that allows multiple devices or institutions to jointly train a model without sharing data. In the future, fine-tuning techniques may be combined with federated learning to optimize the model while protecting data privacy.

3. More efficient fine-tuning method

In addition to LoRA, more efficient fine-tuning methods may emerge in the future, such as Adapters, Prefix Tuning, etc. These methods will further reduce the cost of fine-tuning and enable more companies and individuals to participate in AI development.

Seizing opportunities in the AI ​​era

Fine-tuning technology allows us to see the possibility of AI universalization. Whether it is individual developers or small and medium-sized enterprises, they can build their own AI products through fine-tuning. The success of DeepSeek is just the beginning, and there are countless opportunities waiting for us to explore in the future.