Documentation Index
Fetch the complete documentation index at: https://docs.adaptive-ml.com/llms.txt
Use this file to discover all available pages before exploring further.
Recipes are pre-built workflows for training and evaluating models. Run them on Adaptive’s compute infrastructure.
Run a recipe
job = adaptive.jobs.run(
recipe_key="sft",
num_gpus=1,
args={
"model_to_train": "llama-3.1-8b-instruct",
"output_model_key": "my-model-v1",
"dataset": "my-training-data",
"epochs": 3,
},
)
| Parameter | Type | Required | Description |
|---|
recipe_key | str | Yes | Recipe identifier (see below) |
num_gpus | int | Yes | Number of GPUs to use |
args | dict | Yes | Recipe-specific arguments |
Built-in recipes
Training:| Recipe | Key | Use when |
|---|
| Supervised fine-tuning | sft | You have high-quality completions |
| RL on preferences | preference_rlhf | You have preferred/rejected pairs |
| RL on metrics | metric_rlhf | You have completion-level scores |
| RL with grader | rl | Criteria can be expressed in natural language |
| Recipe | Key | Use when |
|---|
| Evaluate with grader | eval | Comparing model performance |
Run an evaluation
adaptive.jobs.run(
recipe_key="eval",
num_gpus=2,
args={
"dataset": "eval-prompts",
"models_to_evaluate": ["model-a", "model-b"],
"graders": ["quality-judge"],
},
)
Run a recipe
Navigate to your use case and open the Recipes tab. Select a recipe and configure its parameters.Click Run to launch. Monitor progress in the Runs tab.View evaluation results
After an evaluation completes, click the run to see the score table.Click any row to drill down into individual interactions and compare model outputs side-by-side.
