---
license: other
license_name: openmdw
license_link: LICENSE
pretty_name: UltraChat Persona Conversations
language:
- en
task_categories:
- text-generation
size_categories:
- 100K<n<1M
tags:
- synthetic
- conversational
- self-play
- glm
- persona
---

# UltraChat Persona Conversations

**This dataset contains no text copied from its two source datasets.** It is
built from [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
(`train_sft` split — real, human-written opening prompts) and
[nvidia/Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA)
(synthetic persona profiles), but instead of redistributing their text, every
row here carries only the **foreign keys** — `prompt_id` and `persona_uuid` —
back to the original row in each source. An `enrich.py` script is included
to reconstruct the full conversation locally by joining against those two
datasets yourself, under their own respective licenses.

Everything that *is* included as text — every conversational turn — was
generated by **GLM-5.3**: it played a persona (conditioned on the sampled
Nemotron profile) continuing the real UltraChat opening prompt, and separately
played a normal helpful assistant responding to it, in a two-role self-play
loop. No real person wrote or received any of these conversations.

## How it was built

1. **Prompts**: the `prompt` field from all 207,865 rows of
   `ultrachat_200k`'s `train_sft` split — real user-written requests
   (how-tos, writing tasks, analysis questions, etc.).
2. **Personas**: one row sampled (with replacement, seeded per prompt for
   reproducibility) from the first shard of `Nemotron-Personas-USA` (90,910
   synthetic USA personas) for each prompt.
3. **Conversations**: GLM-5.3 played both sides of a chat, seeded with the
   real prompt as the opening user turn:
   - **User side**: the sampled persona, continuing the conversation in a
     voice consistent with its profile (interests, background, register).
     Each turn it emits a structured `{"message", "done"}` decision;
     `done: true` ends the conversation in character — satisfied, done
     engaging, or just moving on. Agreement/satisfaction is never forced.
   - **Assistant side**: a normal helpful assistant, responding naturally to
     whatever the persona said.
   - A soft cap of 20 total messages forces a stop if a conversation runs
     long (0.065% of conversations hit it — see Stats).
4. **Redaction**: the opening prompt's text (`messages[0].content`) was
   stripped to `null` before release, since it is `ultrachat_200k`'s content,
   not this dataset's. No persona text was ever stored in a conversation
   record in the first place — persona conditioning only used `persona_uuid`
   plus profile fields at generation time.
5. **Scope**: this covers 200,389 of the 207,865 `train_sft` prompts
   (96.4%) — the run was stopped before covering the remaining prompts.
   `prompt_id` values not present here are simply prompts that weren't
   processed; there's nothing wrong with them.

## Fields

| field | type | description |
|---|---|---|
| `prompt_id` | string | Foreign key into `HuggingFaceH4/ultrachat_200k` (`train_sft` split, `prompt_id` column). Use `enrich.py` (or your own join) to recover the actual opening prompt text. |
| `persona_uuid` | string | Foreign key into `nvidia/Nemotron-Personas-USA` (`train` split, shard 0, `uuid` column). Use `enrich.py` to recover the persona description and demographics. |
| `messages` | list[{role, content}] | The full transcript. `messages[0].content` is `null` (redacted — see above); every other turn is GLM-5.3's own generated text. |
| `turns` | int | Number of messages in `messages` |
| `end_reason` | string | `resolved` (persona chose to end it in character) or `max_turns` (hit the 20-message soft cap) |

## Using `enrich.py`

```bash
pip install pandas pyarrow
python3 enrich.py --in train.parquet --out enriched.parquet
```

This downloads the `train_sft` split of `ultrachat_200k` (~730MB) and shard 0
of `Nemotron-Personas-USA` (~244MB) on first run, joins them in, and writes
an enriched local parquet with `messages[0].content` filled in and a
`persona` (plus `persona_age`/`persona_sex`/`persona_occupation`/
`persona_city`/`persona_state`) column added. That enriched file is yours to
use locally — please don't redistribute it as-is, since it would contain
`ultrachat_200k`/`Nemotron-Personas-USA` content subject to their own
licenses.

## Stats

- 200,389 conversations, one per prompt (200,389 unique `prompt_id`s, 80,810
  unique `persona_uuid`s — personas were sampled with replacement)
- Turn count: min 3, median 5, mean 5.1, max 20
- `end_reason`: `resolved` 200,258 (99.93%), `max_turns` 131 (0.07%)

## Known limitations

- **Synthetic, not human data**, aside from the (redacted, foreign-keyed)
  opening prompts. Both conversational roles past turn 1 are generated by
  the same model family.
- **Persona/prompt mismatch is not filtered.** Personas were sampled
  independently of prompt content, so some pairings are a poor fit (e.g. a
  retiree persona handed a prompt asking for JavaScript code). In practice
  GLM-5.3 sometimes has the persona simply disown the prompt in-character
  ("I think there's been a mix-up...") and redirect — this happens in
  roughly 0.6% of a related, similarly-built dataset from the same project;
  not separately measured here but the same dynamic applies.
- `end_reason: resolved` describes how the conversation ended in character,
  not whether the persona was "satisfied" in any evaluative sense — that's
  not a concept this dataset tracks.
- Not all 207,865 `train_sft` prompts are covered (see "Scope" above).

## License

The conversational content generated for this dataset (everything in
`messages` except the redacted `null` at index 0) is released under the
[OpenMDW License](https://openmdw.ai/) ("Open Model, Data and Weights",
version 1.1 — see `LICENSE`). This dataset does not grant you any rights to
`ultrachat_200k` or `Nemotron-Personas-USA` content — obtain those under
their own licenses if you use `enrich.py`.