Supervised Fine Tuning (SFT)

What is Supervised Fine Tuning?

Supervised Fine Tuning (SFT) is a training approach that uses labeled input-output pairs to teach the model specific behaviors and response patterns. The model learns by observing examples of correct inputs and their corresponding desired outputs, gradually adjusting its parameters to minimize the difference between its predictions and the target responses. This technique is particularly effective when you have clear examples of how the model should behave and provides predictable, measurable improvements in task-specific performance.

Prerequisites

Before installing, make sure you have:

1. Python 3.11+

2. Pip or Poetry

3. gcloud CLI - required because gllm-training is a private library hosted in a private Google Cloud repository

After installing, please run

gcloud auth login

to authorize gcloud to access the Cloud Platform with Google user credentials.

Our internal gllm-training package is hosted in a secure Google Cloud Artifact Registry. You need to authenticate via gcloud CLI to access and download the package during installation.

1. The minimum requirements:

   1. CUDA-compatible GPU

   2. Recommendation GPU:

      1. RTX A5000

      2. RTX 40/50 series.

   3. Windows/Linux, currently not support for macOS

Installation

Linux or Windows WSL

pip install --extra-index-url "https://oauth2accesstoken:$(gcloud auth print-access-token)@glsdk.gdplabs.id/gen-ai-internal/simple/" "gllm-training"

Windows Powershell

Windows Command Prompt

Quickstart

Let's jump into a basic example fine-tuned using SFTTrainer. SFT trainer cannot be empty, which means it must have at least the model name and csv datasets path.

from gllm_training import SFTTrainer

finetuner = SFTTrainer(
    model_name="Qwen/Qwen3-1.7b",
    datasets_path="examples/csv"
)
results = finetuner.train()

Fine tuning model using YAML file.

We can run experiments in a more structured way by using a YAML file. The current fine-tuning SDK supports both online data from Google Spreadsheets and local data in CSV format.

Example 1: Fine tuning using online data.

We can prepared our experiment using YAML file with the data trained and validation from google spreadsheet.

1

Configure environment variables (.env)

Fill in the GOOGLE_SHEETS_CLIENT_EMAIL and GOOGLE_SHEETS_PRIVATE_KEY fields. If you don’t have these keys, please contact the infrastructure team.

GOOGLE_SHEETS_CLIENT_EMAIL="[e-mail]"
GOOGLE_SHEETS_PRIVATE_KEY="-----BEGIN PRIVATE KEY-----\nYOUR_PRIVATE_KEY\n-----END PRIVATE KEY-----\n"

2

Share the spreadsheet

Share your Google Spreadsheet containing the training and validation data with the GOOGLE_SHEETS_CLIENT_EMAIL.

Share -> add your google sheet client email -> set as editor

3

Experiment configuration (sft_experiment_config.yml)

You can use a YAML file to plan your fine tuning experiments. To fine tuning with YAML, you need to define the required variables in the file.

fine_tune_hyperparam_1: &fine_tune_hyperparam_1_conf
  hyperparameters_id: "1"
  max_seq_length: 2048
  load_in_8bit: true
  r: 16
  lora_alpha: 16
  target_modules: ["q_proj", "k_proj", "v_proj"]
  per_device_train_batch_size: 1
  learning_rate: 2.0e-4
  num_train_epochs: 1
  eval_strategy: "steps"
  save_strategy: "steps"
  load_best_model_at_end: true

storage_config: &storage_config
  provider: "s3"
  bucket_name: "your-s3-bucket"
  upload_to_cloud: false

experiment_1:
  experiment_id: "1"
  hyperparameters: *fine_tune_hyperparam_1_conf
  storage_config: *storage_config
  model_name: "Qwen/Qwen3-1.7b"
  framework: "unsloth"
  spreadsheet_id: "<your-google-sheets-id>"
  train_sheet: "<train_data_sheet_name>"
  validation_sheet: "<validation_data_sheet_name>"
  prompt_sheet: "<prompt_sheet>"
  prompt_name: "<your_prompt_name>"
  column_mapping_config:
    input_columns:
      query: "query"
    label_columns:
      label: "target"

4

Fine tuning

To run your fine-tuning, you need to load the YAML data using the YamlConfigLoader function, and select the experiment ID when executing the load function.

from dotenv import load_dotenv
from gllm_training import YamlConfigLoader
from gllm_training import SFTTrainer
load_dotenv(override=True)

config_loader = YamlConfigLoader(base_dir="./")
config = config_loader.load("sft_experiment_config.yml", "experiment_1")
finetuner = SFTTrainer(**config)
finetuner.train()

5

(Notes) Output format

Our SDK supports dictionary or string output formats for fine-tuned models.

1. Output dictionary

   YAML format

   column_mapping_config:
     label_columns:
       label: "target"

   Expected output

   {
       "label": "Pentingnya Risiko Kepatuhan Terintegrasi di perbankan"
   }

2. Output string

   YAML format

   column_mapping_config:
     label_columns: "target"

   Expected output

   "Pentingnya Risiko Kepatuhan Terintegrasi di perbankan"

Example 2: Fine tuning using local data.

The remaining hyperparameter configurations for fine-tuning are the same as when using online data. Below is an example YAML configuration for using local data for training and validation.

experiment_1:
  experiment_id: "1"
  hyperparameters: *fine_tune_hyperparam_1_conf
  storage_config: *storage_config
  model_name: "Qwen/Qwen3-1.7b"
  framework: "unsloth"
  datasets_path: "examples/csv"
  train_filename: "training_data.csv"
  validation_filename: "validation_data.csv"
  prompt_filename: "prompt_data.csv"
  prompt_name: "prompt_default"
  column_mapping_config:
    input_columns:
      query: "query"
    label_columns:
      label: "target"

Upload model to cloud storage

When running experiments, we don’t always save the model directly to the cloud. Instead, we may first evaluate its performance before uploading it to cloud storage. To support this workflow, we provide a save_model function that allows you to upload the model as a separate step after fine tuning.

1

Configure environment variable (.env)

Fill in the AWS_ACCESS_KEY, AWS_SECRET_KEY and AWS_REGION fields. If you don’t have these keys, please contact the infrastructure team.

AWS_ACCESS_KEY="<AWS_ACCESS_KEY>"
AWS_SECRET_KEY="<AWS_SECRET_KEY>"
AWS_REGION="<AWS_REGION>"

2

Upload model

To upload the model, you need to configure the storage configuration and specify the model path on save_model function. The model path should point to the directory of your best adapter model.

from gllm_training import SFTTrainer
from gllm_training.schema import StorageConfig

finetuner = SFTTrainer(
    model_name="Qwen/Qwen3-0.6b",
    storage_config=StorageConfig(
        provider="s3",
        upload_to_cloud=True,
        object_prefix="fine-tuned-models",
        bucket_name="glair-gen-ai-llm-model",
    ),
)
finetuner.save_existing_model(
    model_path="data/fine_tuned/exp_1/Qwen3-1.7b/exp_id_1:sft_fine_tuned_hyperparam_1:prompt_1:Qwen3-1.7b:adapter"
)