QuickCoder: Code Completion Training Checkpoints

20 GiB JSONL checkpoints for training code completion and fill-in-the-middle models. The dataset is structured in upload-ready units, with a long-term target of 400 GiB total. It is authored by aisamdasu and was last updated on June 11, 2026.

Use Cases

• Train code completion models based on the JSONL training checkpoints.
• Fine-tune fill-in-the-middle models using the structured checkpoint files.
• Load pre-processed training data from cloud storage for large-scale training jobs.

Strengths

• Checkpoints are structured in approximately 20 GiB units, facilitating cloud storage management.
• The dataset has a defined long-term target size of 400 GiB, indicating a planned scope.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface

Freshness

Last updated 2026-06-11 08:57:32; freshness should be verified.