---
language:
- en
task_categories:
- text-generation
tags:
- synthetic
- sft
- conversational
size_categories:
- 10K<n<100K
pretty_name: TSM
---

# TSM4

28,477 synthetic English chats (user + assistant) for supervised fine-tuning, each with generation metadata. Mostly short: 1 to 3 exchanges.

## Files and format
- `data/data.parquet`: Parquet, ZSTD, ~26 MB, 6 row groups.
- Columns: `id` (unique string), `messages` (JSON string: list of `{role, content}`, optional leading `system`), `meta` (JSON string, 25 fields, schema v2).
- Integrity scan: no duplicate or missing ids, no invalid metadata.

```python
import json
from datasets import load_dataset
row = load_dataset("dragosMediv/TSM4", split="train")[0]
messages, meta = json.loads(row["messages"]), json.loads(row["meta"])
```

## What it contains
| `meta.shape` | Records | Share |
|---|---:|---:|
| one_turn | 13,755 | 48.3% |
| two_turn | 9,353 | 32.8% |
| three_turn | 2,638 | 9.3% |
| short / medium / long (older, up to 16 turns) | 2,731 | 9.6% |

- **Topics:** 47 domains (Python, everyday life, casual chat, ML/AI, earth and space, philosophy, sports, ...), 404 subtopics, 41 task types (writing, factual questions, troubleshooting, recommendations, quizzes, comparisons, brainstorming, calculations, explanations, advice, planning, vague and trick questions).
- **Style controls:** greeting start none 86% / with request 11.5% / greeting only 2.3%; system prompt in 20.1%; answer length short 8,000 / medium 10,223 / long 4,533 and format light 10,112 / prose 7,980 / rich 4,664 (both null when a system prompt sets the style).
- **Personas:** ~11k distinct, 34 job labels, with varied expertise, tone and typing style.
- **Size:** 14.5M completion tokens; per record mean 510, median 299, max 11,364.
- **Meta fields:** identity (`schema`, `harness`, `idx`, `seed_base`), structure (`shape`, `turns`, `greeting_mode`, `farewell`, `directives`, ...), topic (`domain`, `subtopic`, `task_type`), persona, style, provenance (`model`, `gen_seconds`, `completion_tokens`) and `qc`. `gen_seconds` is wall time under heavy concurrency, not a benchmark.

## How it was generated
Each record starts from a seeded random spec: domain and subtopic, task type, situation, persona (job, expertise, tone, typing style), greeting and farewell options, an optional system prompt (about 20 in the bank), and a preferred answer length and format. Multi-turn data adds moves like follow-ups, pushback, user mistakes, topic switches, distractions and format changes. Hidden assistant guidelines (be accurate, admit uncertainty, no filler openers, follow requested format) were used during generation but are not stored. Runs used two NVIDIA T4s.

| `meta.harness` | Records | Model | Method |
|---|---:|---|---|
| v4-oneshot | 24,530 | Qwen3-30B-A3B-Instruct-2507 (AWQ 4-bit, vLLM) | whole 1-3 exchange chat written in one call from a spec, shared cached system prompt |
| v2-vllm | 1,745 | same | turn by turn: simulated-user call plus assistant call per turn |
| v2-vllm2 | 856 | same | like v2-vllm, trimmed prompts |
| v2 | 1,346 | Qwen3.6-35B-A3B | earlier llama.cpp pipeline, turn by turn |

**Filtering:** drops truncated output, broken turn order, CJK characters in English chats, generator-identity mentions (Qwen/Alibaba), unfilled placeholders, claimed-but-missing pasted content, repeated persona mentions, and leaked reasoning tags. Filler openers ("Great question!") are removed and stock closers mostly removed. `v4-oneshot` drops exact duplicate first messages within a run; there is no global dedup.

## Quality
Only 172 records (0.6%) carry `qc` scores (1-5): user realism 4.84, assistant quality 4.33, format fit 4.94, 0 broken, 63 with flagged suspect facts. This is not a whole-dataset evaluation.

## Limitations
- Synthetic: facts, code and numbers are not verified.
- English only; 95% from one model, so phrasing repeats (e.g. "Sure, here's a...", "You're absolutely right to..." in turn-by-turn data).
- Users are LLM-simulated, so more fluent and cooperative than real users.
- Few long conversations. Pipelines are mixed; filter by `meta.harness`.