fyaronskiy/code_retriever-saved-checkpoints

code_retriever training checkpoints

Full Hugging Face Trainer / SentenceTransformer checkpoints for the code_retriever project.

Each checkpoint directory contains everything needed to resume training: model.safetensors, optimizer.pt, scheduler.pt, rng_state.pth, trainer_state.json, training_args.bin, tokenizer files, and pooling config.

Contents

Run	Checkpoints	Notes
RuModernBERT-base_bs64_lr_2e-05	checkpoint-12400, checkpoint-33600, checkpoint-46400, checkpoint-82600	1st epoch, batch size 64
RuModernBERT-base_bs128_lr_2e-05_2nd_epoch	checkpoint-27200, checkpoint-45400	2nd epoch, batch size 128

Base model: deepvk/RuModernBERT-base

Download all checkpoints

huggingface-cli download fyaronskiy/code_retriever-saved-checkpoints \
  --repo-type model \
  --local-dir models/saved_checkpoints

Download a single checkpoint

huggingface-cli download fyaronskiy/code_retriever-saved-checkpoints \
  --repo-type model \
  --include "RuModernBERT-base_bs64_lr_2e-05/checkpoint-82600/*" \
  --local-dir models/saved_checkpoints

Resume training

1. Download the desired run folder or checkpoint.
2. In train/train.py, point resume_checkpoint to the checkpoint path and set model_dir to the corresponding run directory under models/.

run_name = "RuModernBERT-base_bs64_lr_2e-05"
model_dir = f"../models/{run_name}"
resume_checkpoint = "../models/saved_checkpoints/RuModernBERT-base_bs64_lr_2e-05/checkpoint-82600"
do_resume_train = True
auto_resume = False

3. Launch training as usual, e.g. bash train/train_accelerate.sh.

Load for inference only

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "fyaronskiy/code_retriever-saved-checkpoints/RuModernBERT-base_bs64_lr_2e-05/checkpoint-82600"
)

For production inference, prefer the published model: fyaronskiy/code_retriever_ru_en.