README.md

---
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.

## Citation

```bibtex
@misc{fineenvs,
  author = {Kolavi, Adithya S},
  title  = {FineEnvs: Open Source RL Environments for LLM Agents},
  year   = {2026},
  url    = {https://github.com/adithya-s-k/FineEnvs}
}
```