Datasets:

michaelowusuntim6
/

python-codeparrot-qwen35

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:

{"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

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.