Train Sft

Step 2: Determine Backend

Backend:

1. ServerlessBackend (Recommended) — Train on remote managed GPUs. No local GPU needed, production-ready inference endpoint.
2. LocalBackend — Train on your local GPU. Full control, fast iteration.

Step 3: Select and Validate Dataset (JSONL scenario)

IMPORTANT: Do NOT assume a dataset. Do NOT make up or hallucinate file paths. Never pretend you searched for files if you didn't actually run a search tool.

If you have access to file system tools (Glob) and can actually execute them, search for .jsonl files using Glob (**/*.jsonl). Present real results as options. Always include "Provide my own file path" as the last option.

Otherwise, ask the user: "What is the path to your JSONL training file?" — nothing more.

Once the user has provided a file path, validate it if you can run code using the script below. If you cannot run code, skip validation and move on.

import json, sys
ROLES = {"system", "user", "assistant", "developer", "tool", "function"}
errors = []
for i, line in enumerate(open(sys.argv[1]), 1):
    try:
        r = json.loads(line)
        msgs = r.get("input", r).get("messages", [])
        assert isinstance(msgs, list) and msgs, "no messages"
        for j, m in enumerate(msgs):
            assert m.get("role") in ROLES, f"messages[{j}]: invalid role {m.get('role')!r}"
            assert m.get("content") or m.get("function_call") or m.get("tool_calls"), f"messages[{j}]: no content"
        if "input" not in r:
            assert msgs[-1]["role"] == "assistant", "last message must be from assistant"
        tools = r.get("tools")
        if tools is not None:
            assert isinstance(tools, list), "tools must be a list"
    except Exception as e:
        errors.append(f"  Line {i}: {e}")
print(f"{len(errors)} error(s):\n" + "\n".join(errors) if errors else f"Valid! {i} rows")
sys.exit(1 if errors else 0)

The JSONL format supports these fields per row:

• messages (required): List of chat messages
• tools (optional): List of tool/function definitions for tool-call training
• response_format (optional): Structured output schema (not used during training, but useful as metadata)

Report the row count and validation result to the user. Do NOT read the whole dataset file. Do NOT name the dataset. If the format is wrong, help them fix it or convert their data.

Step 4: Gather Base Parameters

Do NOT ask the user to review or confirm their answers after collecting them — just proceed to the next step.

• Base model: Recommend ONLY these models:
  • OpenPipe/Qwen3-14B-Instruct
  • Qwen/Qwen3-30B-A3B-Instruct-2507
  • meta-llama/Llama-3.1-8B-Instruct
• Project name: A name for this training project (default: sft-project)
• Run name: A static, descriptive name (e.g., agent-001, pii-redactor-001, math-tutor-001). Ask the user for a meaningful name. Do NOT generate random names.

For distillation also ask:

• Teacher model: The larger model to distill from (e.g., an OpenRouter model)
• Teacher API base URL and key: If using a third-party provider
• Prompts: What prompts to send to the teacher model

Step 5: Gather Hyperparameters

This step only applies if you can run code AND know the row count from validation. If you cannot run code, skip this step entirely — do NOT make up or guess hyperparameter values. The train_sft_from_file function has sensible built-in defaults.

Run this Python snippet via Bash to compute defaults (replace NUM_ROWS with the actual row count). Do NOT show any formulas or calculation steps to the user — only show the final values.

import math, sys
n = int(sys.argv[1])
epochs = max(1, min(10, round(10000 / n)))
batch_size = 2
total_steps = math.ceil(n * epochs / batch_size)
steps_per_epoch = math.ceil(n / batch_size)
warmup_steps = max(10, min(1000, round(steps_per_epoch * 0.05)))
warmup_ratio = round(warmup_steps / total_steps, 4)
print(f"epochs={epochs} batch_size={batch_size} lr=2e-4 schedule=linear warmup_ratio={warmup_ratio}")

Present the output values to the user, then ask:

• Use defaults (Recommended) — show all values in the description
• Customize — adjust individual hyperparameters

If they choose "Customize", ask which parameters to change.

For distillation:

Use the same defaults computation as JSONL (replace NUM_ROWS with the number of trajectories). create_sft_dataset_iterator handles the LR schedule automatically.

Step 6: Generate the Training Script

Write a complete, runnable Python script. Use the patterns below. Every script MUST:

• Call await backend.close() at the end so the process doesn't hang
• Print post-training info and usage examples (see shared block below)

Post-training block (append to ALL scripts before backend.close()):

    # --- Training complete ---
    step = await model.get_step()
    inference_name = model.get_inference_name()
    client = model.openai_client()

    print("\n" + "=" * 60)
    print("SFT TRAINING COMPLETE")
    print("=" * 60)
    print(f"  Model:          {inference_name}")
    print(f"  Base model:     <BASE_MODEL>")
    print(f"  Training step:  {step}")
    print(f"  Inference URL:  {client.base_url}")
    print(f"  W&B run:        https://wandb.ai/<YOUR_TEAM>/<PROJECT_NAME>/runs/<RUN_NAME>")
    print("=" * 60)

    print("\n--- Python usage (openai SDK) ---\n")
    print(f'''\
from openai import OpenAI

client = OpenAI(
    base_url="{client.base_url}",
    api_key="not-needed",
)

response = client.chat.completions.create(
    model="{inference_name}",
    messages=[
        {{"role": "user", "content": "Your prompt here"}},
    ],
)
print(response.choices[0].message.content)
''')

    print("--- curl usage ---\n")
    print(f'''\
curl {client.base_url}chat/completions \\
  -H "Content-Type: application/json" \\
  -d '{{
    "model": "{inference_name}",
    "messages": [
      {{"role": "user", "content": "Your prompt here"}}
    ]
  }}'
''')

    await backend.close()

Backend setup

Use the appropriate backend based on the user's choice:

LocalBackend:

from art.local import LocalBackend

backend = LocalBackend()
model = art.TrainableModel(
    name="<RUN_NAME>",
    project="<PROJECT_NAME>",
    base_model="<BASE_MODEL>",
    _internal_config=art.dev.InternalModelConfig(
        engine_args={"gpu_memory_utilization": 0.7},
    ),
)
await model.register(backend)

ServerlessBackend:

from art.serverless.backend import ServerlessBackend

backend = ServerlessBackend()  # uses WANDB_API_KEY env var
model = art.TrainableModel(
    name="<RUN_NAME>",
    project="<PROJECT_NAME>",
    base_model="<BASE_MODEL>",
)
await model.register(backend)

Note: _internal_config with gpu_memory_utilization is only used with LocalBackend. Do NOT include it for ServerlessBackend.

JSONL file training pattern:

If hyperparameters were computed in Step 5, pass them explicitly. If Step 5 was skipped, omit them — train_sft_from_file has sensible defaults.

"""SFT training script generated by /train-sft wizard."""
import asyncio
import art
<BACKEND_IMPORT>
from art.utils.sft import train_sft_from_file

async def main():
    <BACKEND_SETUP>

    await train_sft_from_file(
        model=model,
        file_path="<FILE_PATH>",
        # Only include these if hyperparameters were computed:
        # epochs=<EPOCHS>,
        # batch_size=<BATCH_SIZE>,
        # peak_lr=<PEAK_LR>,
        # schedule_type="<SCHEDULE_TYPE>",
        # warmup_ratio=<WARMUP_RATIO>,
        verbose=True,
    )

    # ... post-training block + backend.close() ...

if __name__ == "__main__":
    asyncio.run(main())

Distillation pattern:

"""Distillation SFT script generated by /train-sft wizard."""
import asyncio, os
from dotenv import load_dotenv
from openai import AsyncOpenAI
import art
<BACKEND_IMPORT>
from art.utils.sft import create_sft_dataset_iterator

load_dotenv()

async def main():
    teacher_client = AsyncOpenAI(
        api_key=os.environ["<API_KEY_ENV_VAR>"],
        base_url="<TEACHER_API_BASE>",
    )
    prompts = ["<PROMPT_1>", "<PROMPT_2>"]

    trajectories = []
    for prompt in prompts:
        completion = await teacher_client.chat.completions.create(
            model="<TEACHER_MODEL>",
            messages=[{"role": "user", "content": prompt}],
        )
        trajectories.append(
            art.Trajectory(
                messages_and_choices=[
                    {"role": "user", "content": prompt},
                    {"role": "assistant", "content": completion.choices[0].message.content},
                ],
                tools=<TOOLS_OR_NONE>,
                )
        )

    <BACKEND_SETUP>

    for chunk in create_sft_dataset_iterator(
        trajectories,
        epochs=<EPOCHS>,
        batch_size=<BATCH_SIZE>,
        peak_lr=<PEAK_LR>,
        schedule_type="<SCHEDULE_TYPE>",
        warmup_ratio=<WARMUP_RATIO>,
    ):
        await model.train_sft(chunk.trajectories, chunk.config, verbose=True)

    # ... post-training block + backend.close() ...

if __name__ == "__main__":
    asyncio.run(main())

Step 7: Write and Offer to Run

1. Write the script to a file (suggest sft_train.py)
2. Ask the user if they want to run it now with uv run python <script_path>
3. If yes, run it directly using the Bash tool (do NOT delegate to a Task subagent) so training logs stream live to the user. Use a 2-minute timeout. If it times out, check progress and decide whether to continue.
4. LocalBackend only — GPU memory errors: If training fails with OOM, lower gpu_memory_utilization in the existing _internal_config (e.g. from 0.7 to 0.5).
5. LocalBackend only — Stale GPU memory: If available GPU memory looks too small, previous training runs may still be occupying memory. Before retrying, run nvidia-smi to check, and if needed kill leftover processes with kill <pid> to free memory.

Important Notes

• LocalBackend requires a GPU.
• ServerlessBackend requires a WANDB_API_KEY environment variable.