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language:
- en
license: apache-2.0
task_categories:
- text-generation
- fill-mask
tags:
- code
- rust
- hyperswitch
- repo-specific-finetuning
pretty_name: hyperswitch Code Corpus (Track A Split)
size_categories:
- n<1K
---
# archit11/hyperswitch-code-corpus-track-a
Repository-specific code corpus extracted from `hyperswitch` and split by file for training/evaluation.
## What is in this dataset
- Source corpus: `data/code_corpus_hyperswitch`
- Total files: 300
- Train files: 270
- Validation files: 30
- Test files: 0
- File type filter: .rs
- Split mode: `file` (file-level holdout)
Each row has:
- `file_name`: flattened source file name
- `text`: full file contents
## Training context
This dataset was used for extended pretraining of:
- Model repo: `https://huggingface.co/archit11/qwen2.5-coder-3b-hyperswitch-track-a-lora`
- Base model: `/root/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-3B/snapshots/09d9bc5d376b0cfa0100a0694ea7de7232525803`
- Sequence curriculum: [768, 1024, 1536]
- Learning rate: 0.001
- Batch size: 1
Evaluation from this run:
( from held out dataset )
- Baseline perplexity: 2.2832
- Post-training perplexity: 1.5429
Filtering
- Source repo restricted to crates/ Rust files only (.rs) in
data_preparation.py:48 and data_preparation.py:44.
- Hard path exclusions for noisy dirs like tests, docs, examples, migrations,
scripts, etc. in data_preparation.py:49.
- Dropped empty/generated files (generated by, auto-generated, do not edit, etc.)
in data_preparation.py:97 and data_preparation.py:149.
- Kept files only if line count in [25, 4000] (data_preparation.py:45,
data_preparation.py:46, data_preparation.py:195).
- Kept only structurally rich files (functions + types >= 2) in
data_preparation.py:205.
- Ranked by a quality score and kept top 300 files (data_preparation.py:47,
data_preparation.py:209, data_preparation.py:229).
- Actual corpus stats: 300 files, 370,212 lines in data/
corpus_metadata_hyperswitch.json.
Split
- For this run (results/track_a_hyperswitch_metrics_lr1e3_curr.json): 270 train
files, 30 validation files, effectively no test set recorded.
- Current script does file split after random.shuffle(all_files)
(track_a_pretraining.py:361, track_a_pretraining.py:377).
Chunking
- no ast based chuking yet since the compute constrains and would be hard to make it work since sequence len is limited
- Files are concatenated per split with a // FILE: <name> header
(track_a_pretraining.py:157).
- Tokenization uses add_special_tokens=False; chunks are fixed-size, non-
overlapping windows (stride = block size) in track_a_pretraining.py:176.
- Curriculum for this run: 768 -> 1024 -> 1536 (results/
track_a_hyperswitch_metrics_lr1e3_curr.json).
- Validation chunks were capped to 160 (seen in run metrics), via random subset
trimming logic in track_a_pretraining.py:196.
Perplexity eval
- PPL is computed from average token-level CE loss over eval chunks
(track_a_pretraining.py:267).
- This run reported 2.2832 -> 1.5429 (baseline -> post).
## Load with datasets
```python
from datasets import load_dataset
ds = load_dataset("archit11/hyperswitch-code-corpus-track-a")
print(ds)
print(ds["train"][0]["file_name"])
```
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