Datasets:
File size: 1,447 Bytes
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language:
- en
license: other
task_categories:
- text-generation
tags:
- python
- code
- pretraining
- qwen3.5
- conversation
- instruction-tuning
size_categories:
- 10K
---
# CodeParrot Clean Python (sample)
## Description
Teaches domain-specific instruction following and code generation for this expert.
## Source
- codeparrot/codeparrot-clean - 500 MB streamed sample of 12.8 GB; the per-file licence field is preserved in the text
Formatted for the MoE-orchestrator project
(https://github.com/michaelowusuntim6/MoE-orchestrator). Expert target:
`code_python`.
## Format
Each record is a JSON object with a `messages` field formatted for Qwen3.5's
native chat template:
```json
{"messages": [
{"role": "system", "content": "..."},
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
]}
```
The records are consumed via `tokenizer.apply_chat_template()`. Special tokens
(`<|im_start|>`, `<|im_end|>`) are added by the template, never embedded in
content.
## Splits
- `train`: 47,048 records
- `val`: 975 records
## Usage
```python
from datasets import load_dataset
ds = load_dataset("michaelowusuntim6/python-codeparrot-qwen35", split="train")
print(ds[0]["messages"])
```
## License
per-file (mostly MIT/Apache-2.0). Upstream sources keep their own licences - see the source list
above and `docs/DATASET_SOURCES.md` in the MoE-orchestrator repository for
per-source detail.
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