A Comprehensive Guide to Fine-Tuning Large Language Models (LLMs)

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A Comprehensive Guide to Fine-Tuning Large Language Models (LLMs) · devs3

javascript

{
  "messages": [
    {"role": "system", "content": "You are a helpful legal assistant. Summarize the provided legal clause in plain English."},
    {"role": "user", "content": "Clause: 'The party of the first part hereby indemnifies and holds harmless the party of the second part from any and all claims, demands, and actions arising from the execution of the aforementioned agreement.'"},
    {"role": "assistant", "content": "This means that the first party will financially protect the second party from any lawsuits or costs related to this agreement."}
  ]
}

javascript

{
  "instruction": "Summarize this legal clause in plain English.",
  "input": "Clause: 'The party of the first part hereby indemnifies...'",
  "output": "This means that the first party will financially protect..."
}

python

from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from peft import LoraConfig, get_peft_model
from trl import SFTTrainer

# 1. Load Model and Tokenizer
model_name = "meta-llama/Llama-3-8b-hf"
model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True) # QLoRA 4-bit quantization
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

# 2. Configure LoRA
peft_config = LoraConfig(
    lora_alpha=16,
    lora_dropout=0.1,
    r=64,  # the "rank"
    target_modules=["q_proj", "v_proj"], # which parts of the model to attach to
    task_type="CAUSAL_LM",
)

# 3. Set Training Arguments
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-5,
    fp16=True
)

# 4. Initialize Trainer
trainer = SFTTrainer(
    model=model,
    train_dataset=your_prepared_dataset, # Your dataset from Step 3
    peft_config=peft_config,
    dataset_text_field="text",
    tokenizer=tokenizer,
    args=training_args,
)

# 5. Start Training!
trainer.train()

# 6. Save the Adapters (not the whole model)
trainer.model.save_pretrained("./my_fine_tuned_llama")

A Comprehensive Guide to Fine-Tuning Large Language Models (LLMs)

Why Fine-Tune an LLM?

Join the conversation

Comments

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Step-by-Step Guide to Fine-Tuning

Step 1: Define Your Objective

Step 2: Choose Your Base Model

Step 3: Prepare Your Dataset

Step 4: Select a Fine-Tuning Method

Step 5: Configure Your Training Parameters (Hyperparameters)

Step 6: Run the Fine-Tuning Job

Step 7: Evaluate and Iterate

A Simple Code Example (Using Hugging Face with QLoRA)

Important Considerations

Why Fine-Tune an LLM?

Join the conversation

Comments

Keep reading

Building Advanced MCP Agents: A Guide to Multi-Agent Coordination, Context Awareness, and Gemini Integration

RAG vs. CAG: Choosing the Right Context Architecture for Modern AI Applications

Step-by-Step Guide to Fine-Tuning

Step 1: Define Your Objective

Step 2: Choose Your Base Model

Step 3: Prepare Your Dataset

Step 4: Select a Fine-Tuning Method

Step 5: Configure Your Training Parameters (Hyperparameters)

Step 6: Run the Fine-Tuning Job

Step 7: Evaluate and Iterate

A Simple Code Example (Using Hugging Face with QLoRA)

Important Considerations