| --- |
| license: mit |
| task_categories: |
| - text-generation |
| tags: |
| - sft |
| - agent |
| - tool-calling |
| - data-analysis |
| - trl |
| --- |
| |
| # 🛠️ Data Agent — SFT |
|
|
| **4,677 worked examples** of an agent doing data science *the right way*. Each row is a complete, |
| **verified-correct** trajectory: read the question, poke at the data with a shell tool, reason, |
| compute, and write the answer. Every one of these solved its task and passed a deterministic grader |
| — so you're fine-tuning on demonstrations that are **known to be correct**, not just plausible. |
|
|
| Drop-in ready for [TRL](https://github.com/huggingface/trl): conversational `messages` + `tools`. |
|
|
| ## Where it comes from |
| These are real agent rollouts on the [Data Agent](https://huggingface.co/HuggingEnvs) tasks, which |
| were themselves built from the [**jupyter-agent dataset**](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset) |
| (data-science notebooks over Kaggle datasets). We kept **only trajectories that reached the correct |
| answer** under deterministic grading (reward = 1.0) — one clean demonstration per task. |
|
|
| ## What's inside |
| - **4,677 correct trajectories** — one per task |
| - **Difficulty** — easy 1,402 · medium 2,640 · hard 635 |
| - One tool throughout: `bash` (shell command execution) |
|
|
| ## What's in a row |
| - **`messages`** — the full conversation in OpenAI/TRL chat format: `system` → `user` (the task) → |
| `assistant` (reasoning + `tool_calls`) → `tool` (command output) → … → final `assistant` answer. |
| Tool-call `arguments` are JSON objects; `tool` messages carry the tool `name`. |
| - **`tools`** — the `bash` tool's JSON schema (rendered by `apply_chat_template(..., tools=...)`) |
| - `task_id`, `difficulty` (1–5), `difficulty_tier`, `n_turns`, `source_agent` |
|
|
| ## Fine-tune with TRL |
| ```python |
| from datasets import load_dataset |
| from trl import SFTTrainer, SFTConfig |
| |
| ds = load_dataset("HuggingEnvs/data-agent-sft", split="train") |
| |
| trainer = SFTTrainer( |
| model="Qwen/Qwen2.5-3B-Instruct", # any tool-capable chat template |
| train_dataset=ds, # messages + tools are picked up automatically |
| args=SFTConfig(assistant_only_loss=True, max_length=8192), |
| ) |
| trainer.train() |
| ``` |
| The `messages` + `tools` columns render through your model's chat template, and |
| `assistant_only_loss=True` trains on the assistant's tokens only — no dataset wrangling needed. |
|
|
