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This guide walks you through training your first model on Adaptive Engine. You’ll create a project, deploy a base model, upload training data, run fine-tuning, and test the results.
Train a customer service agent to respond like a poet.
Create a project
Projects organize your models, data, and training runs.
Deploy a model
Attach a model to deploy it for inference.
Run fine-tuning
SFT (supervised fine-tuning) trains your model on labeled examples.
Test your model
Deploy and chat with your fine-tuned model. Set ADAPTIVE_URL and ADAPTIVE_API_KEY environment variables before starting. See Authentication for details.
Create a project
Projects organize your models, data, and training runs. import os from adaptive_sdk import Adaptive adaptive = Adaptive( base_url = os.environ[ "ADAPTIVE_URL" ], api_key = os.environ[ "ADAPTIVE_API_KEY" ], ) adaptive.projects.create( key = "quickstart-fun-poet" ) adaptive.set_default_project( "quickstart-fun-poet" )
Deploy a model
Attach a model to deploy it for inference. adaptive.models.attach( model = "qwen-3-4b-instruct-2507" , wait = True , )
The model becomes available within a few minutes.
Upload a dataset
Datasets contain examples that teach your model how to respond. Download: fun-poet-completions.jsonl adaptive.datasets.upload( file_path = "fun-poet-completions.jsonl" , dataset_key = "fun-poet-completions" , )
Run fine-tuning
SFT (supervised fine-tuning) trains your model on labeled examples. job = adaptive.jobs.run( recipe_key = "sft" , num_gpus = 1 , args = { "model_to_train" : "qwen-3-4b-instruct-2507" , "output_model_key" : "fun-poet-v1" , "dataset" : "fun-poet-completions" , "epochs" : 3 , "samples_per_batch" : 16 , "tp" : 1 , }, ) print ( f "Job started: { job.id } " )
Training takes 15-30 minutes. Check progress in the UI under Runs.
Test your model
Deploy and chat with your fine-tuned model. adaptive.models.attach( model = "fun-poet-v1" , wait = True , ) response = adaptive.chat.create( model = "fun-poet-v1" , messages = [ { "role" : "user" , "content" : "I can't log into my account" } ], ) print (response.choices[ 0 ].message.content)
The response should rhyme. Next steps
Track metrics Rate responses to build training data
Training recipes SFT, RLHF, DPO, and other methods
