Hi @jayshan , you're right that it doesn't show up in the Experiments tab. It's accessed through the databricks_genai SDK directly from a notebook. The Experiments tab will only show your run after you launch one via the SDK.

I just ran it in my workspace and it works, here's a quick setup you can try:

%pip install databricks_genai
dbutils.library.restartPython()

from databricks.model_training import foundation_model as fm

# 1. First, check what models are actually available in your region/workspace
for m in fm.get_models():
    print(m.name)

In my workspace this returned: Llama 3.1 8B / 8B-Instruct / 70B / 70B-Instruct, Llama 3.2 1B / 1B-Instruct / 3B / 3B-Instruct, and Llama 3.3 70B-Instruct. So meta-llama/Llama-3.2-3B-Instruct from the docs example is still valid, if you got "model not available" earlier, it might be a region issue or the SDK version.

# 2. Launch a small training run (point train_data_path to a JSONL in a Unity Catalog Volume)
run = fm.create(
    model="meta-llama/Llama-3.2-3B-Instruct",
    train_data_path="/Volumes/<your_catalog>/<schema>/<volume>/train.jsonl",
    task_type="CHAT_COMPLETION",
    register_to="<your_catalog>.<schema>.<model_name>",
    training_duration="1ep",
    learning_rate="5e-7",
)

print(run.name, run.status)

The training data should be a JSONL file with chat-format rows like:

{"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}

Once you call fm.create(), the run will then show up in the Experiments tab as an MLflow run. If you still hit "model not available" after running get_models(), share the exact error and we can dig into it.