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Fine-Tuning DeepSeek LLM

Fine-tuning a large language model (LLM) like DeepSeek can significantly improve its performance for specific tasks. In this guide, we’ll walk through the process using PyTorch, Transformers, and PEFT while keeping things simple and easy to understand.

1. Setting Up the Environment

Before starting, we need to install the necessary libraries. The following command ensures that we have all the required packages:

import torch
torch.cuda.is_available()    
        

To install the latest versions of PyTorch and other dependencies, use:

!pip install -U torch transformers datasets accelerate peft bitsandbytes
!pip install --upgrade torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118    
        

Here, bitsandbytes helps optimize memory usage, and PEFT (Parameter Efficient Fine-Tuning) enables more efficient model adaptation.

2. Loading the DeepSeek Model

Now, let’s load the DeepSeek model along with the tokenizer.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "deepseek-ai/deepseek-llm"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)     
        

This code loads the DeepSeek model and its tokenizer. The tokenizer converts text into numerical format, which the model can process.

3. Preparing the Dataset

We need a dataset to fine-tune our model. Using the datasets library, we can easily load and preprocess data.

from datasets import load_dataset

dataset = load_dataset("path_to_your_dataset")
        

Make sure to replace path_to_your_dataset with the actual dataset path or Hugging Face dataset name.
To tokenize the dataset:

def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)
        
tokenized_datasets = dataset.map(tokenize_function, batched=True)
        

This function ensures all text data is tokenized correctly and ready for training.

4. Fine-Tuning the Model

Now, let’s fine-tune the DeepSeek model using PEFT and LoRA (Low-Rank Adaptation) to make training more efficient.

from peft import LoraConfig, get_peft_model

config = LoraConfig(
    r=8,
    lora_alpha=16,
    lora_dropout=0.1,
    bias="none",
    target_modules=["q_proj", "v_proj"]
)
            
model = get_peft_model(model, config)
        

This code applies LoRA to specific model layers, reducing the number of trainable parameters and optimizing efficiency.

5. Training the Model

To train the model, we use Trainer from the Transformers library:

from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    save_strategy="epoch",
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    num_train_epochs=3,
    logging_dir="./logs"
)
            
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["test"]
)
            
trainer.train()
        

This configuration:
-> Sets up logging and saving options
-> Defines batch sizes for training and evaluation
-> Specifies the number of training epochs
Once training completes, the model is fine-tuned and ready for use!

6. Evaluating the Model

After training, we evaluate the model to check its performance:

def generate_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=100)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)
        
print(generate_text("The future of AI is"))
        

This function generates text based on a given prompt, showcasing the fine-tuned model’s capabilities.

Conclusion

Fine-tuning DeepSeek LLM using PEFT and LoRA allows for efficient training with fewer resources. This step-by-step guide covered:
-> Setting up the environment
-> Loading the model and dataset
-> Applying efficient fine-tuning techniques
-> Training and evaluating the model
With these steps, you can fine-tune your own DeepSeek model for various NLP applications!

Our Fine-Tuning Services

We provide comprehensive fine-tuning services for any LLM model on your own data—whether it’s text-based, image-based, or audio-based. Our team specializes in customizing models to fit your specific needs, ensuring optimized performance and efficient deployment.

-> Looking for fine-tuning support? Contact Us !
-> Explore our github repository for demo notebooks showcasing fine-tuning of different LLMs on various datasets.
-> Fine-tune the DeepSeek Model using LoRA by following our dedicated guide.

Feel free to reach out if you need expert assistance in fine-tuning or building custom AI-powered chatbots tailored to your requirements!