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import requests
import os
from openai import OpenAI
import time
from dotenv import load_dotenv
load_dotenv()
api_key=os.environ.get('NEBIUS_API_KEY')
api_url="https://api.studio.nebius.com"
client = OpenAI(
base_url=api_url+"/v1",
api_key=api_key,
)
def create_lora_from_job(name, ft_job, ft_checkpoint, base_model):
fine_tuning_result = f"{ft_job}:{ft_checkpoint}"
lora_creation_request = {
"source": fine_tuning_result,
"base_model": base_model,
"name": name,
"description": "Deploying fine-tuned LoRA model"
}
response = requests.post(
f"{api_url}/v0/models",
json=lora_creation_request,
headers={"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
)
print("Model creation response code:", response.status_code)
print("Model creation response body:", response.text)
response.raise_for_status()
return response.json()
# Get the custom model information, check its status and wait for the validation to complete
def wait_for_validation(name, delay=5):
while True:
time.sleep(delay)
print(f"Validating LORA: {name}")
lora_info=requests.get(
f"{api_url}/v0/models/{name}",
headers={"Content-Type": "application/json","Authorization": f"Bearer {api_key}"}
).json()
if lora_info.get("status") in {"active", "error"}:
return lora_info
# Create a multi-message request
def get_completion(model):
completion = client.chat.completions.create(
model=model,
messages=[{"role":"user","content":"Hello"}],
)
return completion.choices[0].message.content
# Deploy a LoRA adapter model by using IDs of a fine-tuning job and its checkpoint
lora_name=create_lora_from_job("nl-to-sql-adapter", "ftjob-b8246b132b234b78a73e4b508817868d", "ftckpt_db41d377-7e1c-43f8-838a-73801db71444", "meta-llama/Meta-Llama-3.1-8B-Instruct").get("name")
# Check the custom model status
lora_info = wait_for_validation(lora_name)
# If the custom model validation is successful, create a multi-message request with this model
if lora_info.get("status") == "active":
print(get_completion(lora_name))
# If there is an error, display the reason
elif lora_info.get("status") == "error":
print(f"An error occurred: {lora_info["status_reason"]}")
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