Datasets:

DrJohnCode
/

CompactLM-Synthetic-Tasks-600

---
license: cc0-1.0
language:
- en
task_categories:
- text-generation
pretty_name: CompactLM Synthetic Tasks 600
tags:
- synthetic
- instruction-following
- small-language-models
- demo
---
# CompactLM Synthetic Tasks 600

A **fully synthetic, unofficial demonstration** instruction dataset for experimenting with text-processing pipelines and Hugging Face Datasets. Contains 600 template-based records spanning 20 AI engineering topics and 6 task categories (definition, glossary, summarization, rewriting, extraction, evaluation). There are 540 training records and 60 validation records.

## Important limitations

- All entries were generated using deterministic templates, not sampled from people or real users.
- Responses are repetitive and have not been independently reviewed. This dataset is **not a benchmark**, a validated training corpus, or evidence of model performance.
- Train/validation splits can share closely related templates and subjects; **do not treat validation scores as unbiased generalization evidence**.
- Not affiliated with Hugging Face, NVIDIA, or another organization.

## Format

Each JSONL object contains `id`, `category`, `topic`, `messages` (user and assistant turns), `synthetic`, `source`, and `quality_note`.

## Example

```json
{"id":"compactlm-0001","category":"definition","topic":"GPU inference","messages":[{"role":"user","content":"Explain GPU inference in one sentence for a beginner."},{"role":"assistant","content":"GPU inference means running a trained model on a graphics processor to produce predictions or outputs."}],"synthetic":true,"source":"template-generated","quality_note":"unreviewed demonstration sample"}
```

## Loading

```python
from datasets import load_dataset
dataset = load_dataset("json", data_files={"train": "train.jsonl", "validation": "validation.jsonl"})
print(dataset["train"][0])
```

## Intended use

Education, data loading demos, schema testing, and experiments with instruction formatting. Not suitable as a sole training dataset for a production assistant.

	---
	license: cc0-1.0
	language:
	- en
	task_categories:
	- text-generation
	pretty_name: CompactLM Synthetic Tasks 600
	tags:
	- synthetic
	- instruction-following
	- small-language-models
	- demo
	---

	# CompactLM Synthetic Tasks 600

	A fully synthetic, unofficial demonstration instruction dataset for experimenting with text-processing pipelines and Hugging Face Datasets. Contains 600 template-based records spanning 20 AI engineering topics and 6 task categories (definition, glossary, summarization, rewriting, extraction, evaluation). There are 540 training records and 60 validation records.

	## Important limitations

	- All entries were generated using deterministic templates, not sampled from people or real users.
	- Responses are repetitive and have not been independently reviewed. This dataset is not a benchmark, a validated training corpus, or evidence of model performance.
	- Train/validation splits can share closely related templates and subjects; do not treat validation scores as unbiased generalization evidence.
	- Not affiliated with Hugging Face, NVIDIA, or another organization.

	## Format

	Each JSONL object contains `id`, `category`, `topic`, `messages` (user and assistant turns), `synthetic`, `source`, and `quality_note`.

	## Example

	```json
	{"id":"compactlm-0001","category":"definition","topic":"GPU inference","messages":[{"role":"user","content":"Explain GPU inference in one sentence for a beginner."},{"role":"assistant","content":"GPU inference means running a trained model on a graphics processor to produce predictions or outputs."}],"synthetic":true,"source":"template-generated","quality_note":"unreviewed demonstration sample"}
	```

	## Loading

	```python
	from datasets import load_dataset

	dataset = load_dataset("json", data_files={"train": "train.jsonl", "validation": "validation.jsonl"})
	print(dataset["train"][0])
	```

	## Intended use

	Education, data loading demos, schema testing, and experiments with instruction formatting. Not suitable as a sole training dataset for a production assistant.