How-To Fine-Tune Qwen2 Model On My Own Data for Free

 This video shows a step-by-step process to fine-tune Qwen2 LLM locally on custom dataset for free using UnslothUnsloth easily on Google Colab.

Code:

%%capture

# Installs Unsloth, Xformers (Flash Attention) and all other packages!

!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

!pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes

from unsloth import FastLanguageModel

import torch

max_seq_length = 2048

dtype = None

load_in_4bit = True

fourbit_models = [

    "unsloth/Qwen2-0.5b-bnb-4bit",

]

model, tokenizer = FastLanguageModel.from_pretrained(

    model_name = "unsloth/Qwen2-0.5B",

    max_seq_length = max_seq_length,

    dtype = dtype,

    load_in_4bit = load_in_4bit,

)

model = FastLanguageModel.get_peft_model(

    model,

    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,

)

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:

{}

### Input:

{}

### Response:

{}"""

EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN

def formatting_prompts_func(examples):

    instructions = examples["instruction"]

    inputs       = examples["input"]

    outputs      = examples["output"]

    texts = []

    for instruction, input, output in zip(instructions, inputs, outputs):

        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN

        texts.append(text)

    return { "text" : texts, }

pass

from datasets import load_dataset

dataset = load_dataset("yahma/alpaca-cleaned", split = "train")

dataset = dataset.map(formatting_prompts_func, batched = True,)

from trl import SFTTrainer

from transformers import TrainingArguments

from unsloth import is_bfloat16_supported

trainer = SFTTrainer(

    model = model,

    tokenizer = tokenizer,

    train_dataset = dataset,

    dataset_text_field = "text",

    max_seq_length = max_seq_length,

    dataset_num_proc = 2,

    args = TrainingArguments(

        per_device_train_batch_size = 2,

        gradient_accumulation_steps = 8,

        # Use num_train_epochs = 1, warmup_ratio for full training runs!

        warmup_steps = 20,

        max_steps = 120,

        learning_rate = 5e-5,

        fp16 = not is_bfloat16_supported(),

        bf16 = is_bfloat16_supported(),

        logging_steps = 1,

        optim = "adamw_8bit",

        weight_decay = 0.01,

        lr_scheduler_type = "linear",

        seed = 3407,

        output_dir = "outputs",

    ),

)

trainer_stats = trainer.train()