BigCode Python-Only Model Training Guide | Generated by AI

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Question: How do I get only one programming language (e.g. Python) for training? Does BigCode provide that?

Answer:

Yes. BigCode’s datasets are one of the best sources if you want to train a Python-only model.

The main options are:

Dataset Python only? Quality Scale
The Stack v2 Yes (filter by language) Very good TBs
The Stack Yes Good Large
CodeParrot Yes Good Hundreds of GB
GitHub public repositories Yes (crawl/filter yourself) Varies Huge

Option 1 — The Stack v2 (recommended)

The Stack stores metadata including language.

Example:

from datasets import load_dataset

ds = load_dataset(
    "bigcode/the-stack-v2",
    split="train",
)

python_ds = ds.filter(lambda x: x["lang"] == "Python")

Or if using parquet files:

import pyarrow.parquet as pq

table = pq.read_table(file)
python_only = table.filter(table["lang"] == "Python")

The dataset already labels files by language, so you don’t need to detect them yourself.

Option 2 — CodeParrot

If you literally only care about Python:

load_dataset("codeparrot/codeparrot-clean")

It is almost entirely Python source code.

This is much smaller than The Stack and easier to experiment with.

Option 3 — Build your own dataset

Many people training code LLMs:

GitHub
    ↓
clone repos
    ↓
keep only *.py
    ↓
deduplicate
    ↓
remove generated/vendor files
    ↓
tokenize
    ↓
train

This gives you full control.

How much Python data do you actually need?

For a small decoder model:

Model Tokens
10M 1–5B
100M 10–30B
760M 30–80B
1.5B 50–150B

Python is relatively information-dense because the vocabulary and syntax are constrained. You generally need fewer tokens than for an equivalently capable natural-language model.

A simple preprocessing pipeline

The Stack v2
      │
      ├── lang == Python
      │
      ├── remove duplicates
      │
      ├── remove autogenerated files
      │
      ├── remove notebooks/tests if desired
      │
      ├── tokenizer
      │
      └── NanoChat training

For your experiments with NanoChat, this is a straightforward and practical pipeline.

References:

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