Datasets:

parallell
/

MOPD-Com-Training-Data

---
pretty_name: MOPD-Com Training Data
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- mopd
- knowledge-distillation
- math
- code
- instruction-following
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train_prompt2048.parquet
---
# MOPD-Com Training Data

This repository contains the final labeled training mixture prepared for [MOPD-Com](https://github.com/Lorsense/MOPD-Com). It contains **65,994 training examples** in one Parquet file. It is a source-labeled, preprocessed dataset, not a claim of an exact reproduction of an unpublished paper preprocessing pipeline.

## Domain composition

| Routing label (`extra_info.opd_teacher`) | Examples |
| --- | ---: |
| `math` | 25,000 |
| `code` | 25,000 |
| `instruction_following` | 15,994 |
| Total | 65,994 |

The original sampled mixture had 25,000 math, 25,000 code, and 16,000 instruction-following examples. Six sampled HIR examples exceeded the 2,048-token prompt limit and were removed. The remaining mixture order is preserved. Domain labels were assigned from source provenance; no model was called for labeling.

## Sources and sampling

- Math: [icemoon28/MOPD-Training-Data](https://huggingface.co/datasets/icemoon28/MOPD-Training-Data), `DeepMath-103K/train_filtered_level6.parquet` (57,046 source rows).
- Code: the same dataset, `Eurus/code_train.parquet` (25,276 source rows).
- Instruction following: [sastpg/HIR-16K](https://huggingface.co/datasets/sastpg/HIR-16K), `HIR_trainv1.jsonl` (16,968 source rows). The instruction-following subset of icemoon28/MOPD-Training-Data was not used.

Pinned icemoon28 revision: `96e88d579cb1ee68c9183c52e5d133f58cf370bf`.
Pinned HIR revision: `2a95f69eb56cc47edc16a45f939cde479673a4cb`.
Per-domain shuffle seeds: math 42, code 43, instruction following 44; final mixture shuffle seed 42. No deduplication was applied.

## Prompt construction

Math and code message contents are preserved. HIR prompts include the original question followed by all provided criteria, using this format:

```text
{original question}
The output must follow the following rules:
1. {first criterion}
2. {second criterion}
...
```

This corresponds to a layout supported by the [official HIR helper](https://github.com/sastpg/HIR/blob/ffd6919ca69a9dafa0c9977be745d209856c8a39/HIR/verl/trainer/ppo/ray_trainer.py). No system prompt is included in any example. No target assistant answer is appended to the prompt.

Prompt lengths were measured using the Qwen/Qwen3-1.7B tokenizer with the chat template, `add_generation_prompt=True`, and `enable_thinking=False`. Every retained example has a prompt length of at most 2,048 tokens. Other tokenizers or chat template settings may produce different lengths.

## Schema

- `data_source`: source identifier.
- `prompt`: list of messages with `role` and `content`, suitable for the project's chat template interface.
- `ability`: ability/domain metadata.
- `reward_model`: struct containing `style` and string `ground_truth`. Original reference/verifier content is preserved; JSON content is serialized as a string where needed.
- `extra_info`: routing label `opd_teacher`, unique index, source dataset/revision/file/row and original metadata, original HIR prompt and criteria, prompt construction metadata, prompt SHA256, prompt token length, and length/system-prompt flags.

The pure on-policy distillation training configuration uses generated rollouts and teacher/student probabilities. The preserved ground truth is not an assistant completion inserted into the input prompt.

## Usage

```python
from datasets import load_dataset
train = load_dataset('parallell/MOPD-Com-Training-Data', split='train')
```

To download the original prepared Parquet file:

```python
from huggingface_hub import hf_hub_download
path = hf_hub_download(
    repo_id='parallell/MOPD-Com-Training-Data',
    repo_type='dataset',
    filename='data/train_prompt2048.parquet',
    local_dir='mopd_training_data',
)
```

For local MOPD-Com training, point the training data path to the downloaded Parquet file. This repository contains only the train split; no held-out validation split is provided.

## Integrity

- File: `data/train_prompt2048.parquet`
- Bytes: 785742653
- Rows: 65994
- SHA256: `96d7fef2f0b4a72e7282b76d7a40d55413e7b4d84387d1c417e1ef08e612a7ec`

## Upstream terms and attribution

This is a processed mixture of the linked upstream datasets. Consult the upstream dataset cards and original sources for applicable licenses and use conditions. This card does not assign a new blanket license or replace upstream terms.

	---
	pretty_name: MOPD-Com Training Data
	size_categories:
	- 10K<n<100K
	task_categories:
	- text-generation
	tags:
	- mopd
	- knowledge-distillation
	- math
	- code
	- instruction-following
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train_prompt2048.parquet
	---

	# MOPD-Com Training Data

	This repository contains the final labeled training mixture prepared for [MOPD-Com](https://github.com/Lorsense/MOPD-Com). It contains 65,994 training examples in one Parquet file. It is a source-labeled, preprocessed dataset, not a claim of an exact reproduction of an unpublished paper preprocessing pipeline.

	## Domain composition

	\| Routing label (`extra_info.opd_teacher`) \| Examples \|
	\| --- \| ---: \|
	\| `math` \| 25,000 \|
	\| `code` \| 25,000 \|
	\| `instruction_following` \| 15,994 \|
	\| Total \| 65,994 \|

	The original sampled mixture had 25,000 math, 25,000 code, and 16,000 instruction-following examples. Six sampled HIR examples exceeded the 2,048-token prompt limit and were removed. The remaining mixture order is preserved. Domain labels were assigned from source provenance; no model was called for labeling.

	## Sources and sampling

	- Math: [icemoon28/MOPD-Training-Data](https://huggingface.co/datasets/icemoon28/MOPD-Training-Data), `DeepMath-103K/train_filtered_level6.parquet` (57,046 source rows).
	- Code: the same dataset, `Eurus/code_train.parquet` (25,276 source rows).
	- Instruction following: [sastpg/HIR-16K](https://huggingface.co/datasets/sastpg/HIR-16K), `HIR_trainv1.jsonl` (16,968 source rows). The instruction-following subset of icemoon28/MOPD-Training-Data was not used.

	Pinned icemoon28 revision: `96e88d579cb1ee68c9183c52e5d133f58cf370bf`.
	Pinned HIR revision: `2a95f69eb56cc47edc16a45f939cde479673a4cb`.
	Per-domain shuffle seeds: math 42, code 43, instruction following 44; final mixture shuffle seed 42. No deduplication was applied.

	## Prompt construction

	Math and code message contents are preserved. HIR prompts include the original question followed by all provided criteria, using this format:

	```text
	{original question}

	The output must follow the following rules:
	1. {first criterion}
	2. {second criterion}
	...
	```

	This corresponds to a layout supported by the [official HIR helper](https://github.com/sastpg/HIR/blob/ffd6919ca69a9dafa0c9977be745d209856c8a39/HIR/verl/trainer/ppo/ray_trainer.py). No system prompt is included in any example. No target assistant answer is appended to the prompt.

	Prompt lengths were measured using the Qwen/Qwen3-1.7B tokenizer with the chat template, `add_generation_prompt=True`, and `enable_thinking=False`. Every retained example has a prompt length of at most 2,048 tokens. Other tokenizers or chat template settings may produce different lengths.

	## Schema

	- `data_source`: source identifier.
	- `prompt`: list of messages with `role` and `content`, suitable for the project's chat template interface.
	- `ability`: ability/domain metadata.
	- `reward_model`: struct containing `style` and string `ground_truth`. Original reference/verifier content is preserved; JSON content is serialized as a string where needed.
	- `extra_info`: routing label `opd_teacher`, unique index, source dataset/revision/file/row and original metadata, original HIR prompt and criteria, prompt construction metadata, prompt SHA256, prompt token length, and length/system-prompt flags.

	The pure on-policy distillation training configuration uses generated rollouts and teacher/student probabilities. The preserved ground truth is not an assistant completion inserted into the input prompt.

	## Usage

	```python
	from datasets import load_dataset
	train = load_dataset('parallell/MOPD-Com-Training-Data', split='train')
	```

	To download the original prepared Parquet file:

	```python
	from huggingface_hub import hf_hub_download
	path = hf_hub_download(
	repo_id='parallell/MOPD-Com-Training-Data',
	repo_type='dataset',
	filename='data/train_prompt2048.parquet',
	local_dir='mopd_training_data',
	)
	```

	For local MOPD-Com training, point the training data path to the downloaded Parquet file. This repository contains only the train split; no held-out validation split is provided.

	## Integrity

	- File: `data/train_prompt2048.parquet`
	- Bytes: 785742653
	- Rows: 65994
	- SHA256: `96d7fef2f0b4a72e7282b76d7a40d55413e7b4d84387d1c417e1ef08e612a7ec`

	## Upstream terms and attribution

	This is a processed mixture of the linked upstream datasets. Consult the upstream dataset cards and original sources for applicable licenses and use conditions. This card does not assign a new blanket license or replace upstream terms.