OpenAI GPT-4o-mini / GPT-4o / GPT-4.1 Fine-Tuning API: JSONL Şema + Cost + Dashboard

// === OpenAI fine-tuning JSONL şeması ===
// Her satır: chat conversation
{"messages":[{"role":"system","content":"Sen TR yardımcı asistansın."},{"role":"user","content":"İstanbul nüfusu?"},{"role":"assistant","content":"Yaklaşık 15 milyon."}]}
{"messages":[{"role":"system","content":"Sen TR yardımcı asistansın."},{"role":"user","content":"2+2 kaç?"},{"role":"assistant","content":"4."}]}
 
// Function calling support
{"messages":[{"role":"user","content":"Hava nasıl?"},{"role":"assistant","content":null,"function_call":{"name":"get_weather","arguments":"{\"city\":\"Istanbul\"}"}}],"functions":[{"name":"get_weather","parameters":{"type":"object","properties":{"city":{"type":"string"}}}}]}
 
// Validation: 50-100 sample, training set'ten ayrı

# === OpenAI fine-tuning flow ===
from openai import OpenAI
client = OpenAI()
 
# 1. Upload training file
with open("train.jsonl", "rb") as f:
    train_file = client.files.create(file=f, purpose="fine-tune")
 
with open("val.jsonl", "rb") as f:
    val_file = client.files.create(file=f, purpose="fine-tune")
 
# 2. Create fine-tune job
job = client.fine_tuning.jobs.create(
    training_file=train_file.id,
    validation_file=val_file.id,
    model="gpt-4o-mini-2024-07-18",
    hyperparameters={
        "n_epochs": 3,
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
    },
    suffix="tr-cookbook-v1",
)
print(f"Job started: {job.id}")
 
# 3. Monitor
import time
while True:
    j = client.fine_tuning.jobs.retrieve(job.id)
    print(f"Status: {j.status}, trained_tokens: {j.trained_tokens}")
    if j.status in ["succeeded", "failed", "cancelled"]:
        break
    time.sleep(60)
 
# 4. Use fine-tuned model
response = client.chat.completions.create(
    model=j.fine_tuned_model,
    messages=[{"role": "user", "content": "Test query"}],
)
print(response.choices[0].message.content)