/microsoft-foundry

Quickstart: Fine-Tune Your First Model

6 steps from zero to a fine-tuned model using SFT with synthetic data.

Time: ~20 min active + 1-3 hours training.

Prerequisites

• Microsoft Foundry project with a deployed model (e.g., gpt-4.1-mini)
• Python 3.10+ with openai installed
• Project endpoint URL and API key (Foundry portal → Project Settings)

Step 1: Connect to Your Project

export OPENAI_BASE_URL="https://<your-resource>.services.ai.azure.com/api/projects/<your-project>/openai/v1/"
export AZURE_OPENAI_API_KEY="<your-key>"

from openai import OpenAI
import os

client = OpenAI(base_url=os.environ["OPENAI_BASE_URL"], api_key=os.environ["AZURE_OPENAI_API_KEY"])
resp = client.chat.completions.create(model="gpt-4.1-mini", messages=[{"role": "user", "content": "Hello"}], max_tokens=10)
print(resp.choices[0].message.content)

Step 2: Generate Training Data

import json, re

SYSTEM_PROMPT = "You are a concise technical support agent. Answer in 1-2 sentences."

generation_prompt = """Generate 50 diverse technical support conversations.
Each should have a customer question and an ideal agent response (1-2 sentences).
Cover: password resets, billing, product setup, account changes, shipping, troubleshooting.
Return a JSON array where each element has "question" and "answer" fields."""

resp = client.chat.completions.create(
    model="gpt-4.1-mini", messages=[{"role": "user", "content": generation_prompt}],
    max_tokens=8000, temperature=1.0,
)

content = resp.choices[0].message.content
match = re.search(r'```(?:json)?\s*\n(.*?)\n```', content, re.DOTALL)
json_str = match.group(1) if match else content.strip().strip("`").replace("json\n", "")
examples = json.loads(json_str)

for split, name, rng in [("train", "train.jsonl", examples[:40]), ("val", "val.jsonl", examples[40:])]:
    with open(name, "w") as f:
        for ex in rng:
            f.write(json.dumps({"messages": [
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": ex["question"]},
                {"role": "assistant", "content": ex["answer"]},
            ]}) + "\n")

Validate: python scripts/validate/validate_sft.py train.jsonl

Step 3: Baseline the Base Model

with open("val.jsonl") as f:
    test_examples = [json.loads(line) for line in f][:5]

for ex in test_examples:
    resp = client.chat.completions.create(
        model="gpt-4.1-mini", messages=ex["messages"][:2], max_tokens=200)
    print(f"Q: {ex['messages'][1]['content']}")
    print(f"Expected: {ex['messages'][2]['content']}")
    print(f"Base model: {resp.choices[0].message.content}\n")

Step 4: Upload Data and Submit Job

import time

with open("train.jsonl", "rb") as f:
    train = client.files.create(file=f, purpose="fine-tune")
with open("val.jsonl", "rb") as f:
    val = client.files.create(file=f, purpose="fine-tune")

for _ in range(30):
    if client.files.retrieve(train.id).status == "processed" and client.files.retrieve(val.id).status == "processed":
        break
    time.sleep(10)

job = client.fine_tuning.jobs.create(
    model="gpt-4.1-mini", training_file=train.id, validation_file=val.id,
    suffix="my-first-ft",
    method={"type": "supervised"},
    hyperparameters={"n_epochs": 2, "learning_rate_multiplier": 1.0},
)
print(f"Job submitted: {job.id}")

Or via script:

python scripts/submit_training.py --model gpt-4.1-mini --training-file train.jsonl --validation-file val.jsonl --type sft --suffix my-first-ft --epochs 2

Step 5: Monitor

python scripts/monitor_training.py --job-id <your-job-id>

Or check Microsoft Foundry portal → Fine-tuning → Jobs.

Step 6: Deploy, Test, and Compare

python scripts/deploy_model.py --model-id <fine-tuned-model-name> --name my-ft-deployment --capacity 50

for ex in test_examples:
    base = client.chat.completions.create(model="gpt-4.1-mini", messages=ex["messages"][:2], max_tokens=200)
    ft = client.chat.completions.create(model="my-ft-deployment", messages=ex["messages"][:2], max_tokens=200)
    print(f"Q: {ex['messages'][1]['content']}")
    print(f"Base:       {base.choices[0].message.content}")
    print(f"Fine-tuned: {ft.choices[0].message.content}\n")

What's Next

• Scale data: 200-500 examples → workflows/dataset-creation.md
• Try RFT: For verifiable answers → references/training-types.md
• Debug: workflows/diagnose-poor-results.md
• Full guide: workflows/full-pipeline.md