---
license: other
license_name: mixed-per-source-reference-and-attribution
pretty_name: "QuranLab — Qur'an and Hadith Training Mix"
language:
  - ar
  - en
  - tr
  - ur
  - id
  - fr
  - bn
  - ru
task_categories:
  - text-generation
  - question-answering
  - text-retrieval
annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
  - found
multilinguality:
  - multilingual
source_datasets:
  - original
size_categories:
  - 1M<n<10M
tags:
  - quran
  - hadith
  - islam
  - arabic
  - ahl-sunnah
  - instruction-tuning
  - preference-data
  - rag
  - retrieval
  - grounded-generation
  - hallucination
  - abstention
  - religious-text
  - text
  - datasets
  - pandas
  - dask
  - mlcroissant
configs:
  - config_name: cpt
    data_files:
      - split: train
        path: "cpt/train-*.parquet"
  - config_name: dpo
    data_files:
      - split: train
        path: "dpo/train-*.parquet"
  - config_name: eval
    data_files:
      - split: test
        path: "eval/test-*.parquet"
  - config_name: hard_negatives
    data_files:
      - split: train
        path: "hard_negatives/train-*.parquet"
  - config_name: rag_chunks
    data_files:
      - split: train
        path: "rag_chunks/train-*.parquet"
  - config_name: reranker
    data_files:
      - split: train
        path: "reranker/train-*.parquet"
  - config_name: retriever
    data_files:
      - split: train
        path: "retriever/train-*.parquet"
  - config_name: rlvr
    data_files:
      - split: train
        path: "rlvr/train-*.parquet"
  - config_name: sft
    data_files:
      - split: train
        path: "sft/train-*.parquet"
  - config_name: source_edges
    data_files:
      - split: train
        path: "source_edges/train-*.parquet"
  - config_name: source_units
    data_files:
      - split: train
        path: "source_units/train-*.parquet"
---

# QuranLab — Qur'an and Hadith Training Mix

Training-ready data derived from the QuranLab corpora: continued-pretraining text,
grounded instruction data, preference pairs, verifiable prompts, retrieval pairs and
a held-out evaluation set — all built on the same verse and ḥadīth keys as
[`quranlab/quran`](https://huggingface.co/datasets/quranlab/quran) and
[`quranlab/hadith`](https://huggingface.co/datasets/quranlab/hadith).

QuranLab is a volunteer effort. Our aim is to present these works carefully and at high quality, and to help them travel faithfully — in the spirit in which they were written — not to claim them as ours.

The corpora behind this mix reach you through the work of Tanzil Project, fawazahmed0/quran-api, QuranEnc.com, Tafsir Center for Quranic Studies and Quranic Universal Library (QUL), among others. Who to thank, and what we consulted without ever quoting, is set out in [`SOURCES.md`](SOURCES.md).

The `cpt` subset carries a **`meta.mix_weight`** per row: the raw corpus is dominated
by classical commentary, so the weights rebalance it (tafsīr 57% → 30%, Qur'anic text
0.3% → 10%) without discarding text. The recipe is in
[`cpt_mixture.yaml`](cpt_mixture.yaml).

The emphasis throughout is on models that **cite what they use and decline what they
cannot support**: a large share of the instruction and preference data teaches
abstention, wrong-context rejection and exact-quote fidelity rather than fluent recall.

## Subsets

| config | rows | split |
|---|---:|---|
| `cpt` | 1,933,774 | train |
| `dpo` | 18,935 | train |
| `eval` | 2,877 | test |
| `hard_negatives` | 9,094 | train |
| `rag_chunks` | 503,489 | train |
| `reranker` | 3,070 | train |
| `retriever` | 3,070 | train |
| `rlvr` | 9,000 | train |
| `sft` | 23,935 | train |
| `source_edges` | 1,950,107 | train |
| `source_units` | 2,027,763 | train |

## Scope

This mix contains **Qur'an and ḥadīth derived material only**. Risale-i Nur derived
rows are governed separately under their own permissions and are excluded here —
406,055 rows were filtered out on export, and a gate re-reads the staging to
prove none survived.

## Usage

```python
from datasets import load_dataset

sft  = load_dataset("quranlab/islamic-llm-training", "sft",  split="train")
dpo  = load_dataset("quranlab/islamic-llm-training", "dpo",  split="train")
cpt  = load_dataset("quranlab/islamic-llm-training", "cpt",  split="train")
ev   = load_dataset("quranlab/islamic-llm-training", "eval", split="test")
```

## For agents and pipelines

Deterministic facts an automated consumer needs, so nothing has to be inferred
from a sample.

- **Config naming:** one config per training stage: `cpt`, `sft`, `dpo`, `rlvr`, `retriever`, `reranker`, `hard_negatives`, `eval`
- **Join key:** `meta.source_ids` carries the originating `verse_key` / `hadith_key`, so any row can be traced back to the source corpora
- **Default config:** `sft`
- **Format:** Parquet, one directory per config, split `train`.
- **Terms and credit** are per config in `metadata/`, never per row — join on the
  config name when you need them.
- **`eval` is a held-out split** — the sources behind it were removed from the training targets, so keep it out of training.
- `meta.task_type` names what a row teaches (abstention, quote repair, grade explanation, …); filter on it to build a focused mix.
- **`cpt` rows carry `meta.mix_weight`.** The raw corpus is dominated by classical commentary, so sample each source in proportion to its weight rather than reading the file straight through — `cpt_mixture.yaml` in this repo has the full recipe.

Inspect it without downloading the data:

```bash
# every config and split
curl https://datasets-server.huggingface.co/splits?dataset=quranlab/islamic-llm-training

# a first page of rows
curl "https://datasets-server.huggingface.co/first-rows?dataset=quranlab/islamic-llm-training&config=sft&split=train"

# machine-readable schema and provenance (Croissant JSON-LD)
curl https://huggingface.co/api/datasets/quranlab/islamic-llm-training/croissant
```

Query it in place with SQL — no full download:

```bash
hf datasets sql quranlab/islamic-llm-training "SELECT * FROM 'sft' LIMIT 5"
```

```python
import duckdb
duckdb.sql("SELECT count(*) FROM 'hf://datasets/quranlab/islamic-llm-training/sft/*.parquet'")
```

## Quality, and how to check it yourself

Nothing is released until the integrity gate passes. Every claim below is
something you can re-run rather than take on trust — here on the published data,
not in our build tree:

| What is guaranteed | Evidence |
|---|---|
| Evaluation is genuinely held out | whole surahs (19, 36, 55, 112) plus a hash-selected hadith bucket were removed from training targets; measured target leakage is 0 |
| Quotes are exact | quoted source text is byte-verified against the corpora rather than paraphrased |
| Grades are never invented | hadith grading rows carry the grader; wrong-grade and wrong-collection traps are included so a model that guesses is caught |
| Chat format is validated | role order and structure are checked on every row before release; violations block the build |
| Gated material is excluded structurally | rows are filtered on their provenance fields, not by text matching, and a post-build gate re-reads the staging to confirm none remain |

```python
from datasets import load_dataset

sft = load_dataset("quranlab/islamic-llm-training", "sft", split="train")
roles = {m["role"] for r in sft for m in r["messages"]}
assert roles <= {"system", "user", "assistant"}

# nothing from the gated corpus
assert not any("risale" in str(r["meta"]).lower() for r in sft)
```

Where a source is genuinely missing, the row is published **empty and labelled**
rather than filled by guessing from a neighbouring entry. Coverage numbers in
this card count only real content.

## Terms

Public-domain and CC sources are kept verbatim; everything else is included with credit
to its author or publisher and removed on request via the **Community** tab. Per-source
terms are documented in the upstream corpora.

## Citation

```bibtex
@misc{quranlab_islamic_llm_training_2026,
  title        = {QuranLab — Qur'an and Hadith Training Mix},
  author       = {QuranLab},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/quranlab/islamic-llm-training}}
}
```