0399-tv-full-base-fp

0399 Team Victory Full-Parameter SFT Checkpoint

This repository contains intermediate/full-parameter SFT checkpoints from LLM-jp experiment 0399, using Team Victory valid-clean math reasoning data.

Model Variants

Two model repositories were trained under the same data and training recipe, differing only in initialization:

Current uploaded checkpoint:

• checkpoint-500/
• Upload excludes large optimizer/FSDP duplicate states from Hub.
• Local full training state, including optimizer state, is retained under the ABCI experiment directory.

Experiment Links

• GitHub issue: llm-jp/experiments#399
• W&B project: argo-lab/llmjp4-8b-teamvictory-sft-difficulty-20260629
• Thinking W&B run: 0399_tv-full-thinking_fp
• Base W&B run: 0399_tv-full-base_fp
• Tokenized dataset: argo11/0399-tv-valid-clean-sft-tokenized-llmjp4-8b

Intended Use

These checkpoints are intended for research on Japanese/English mathematical reasoning and reinforcement-learning initialization.

Primary intended uses:

• Compare thinking initialization vs base initialization after identical Team Victory SFT.
• Use as candidate initial checkpoints for later RL / GRPO-style math reasoning experiments.
• Evaluate whether Team Victory SFT improves AIME-style pass@1 while preserving broader ability.

Not intended uses:

• Production deployment without additional evaluation.
• Safety-critical mathematical, financial, legal, medical, or educational grading decisions.
• Claims of benchmark superiority before full evaluation is complete.

Training Data

Source dataset:

• HayatoHongoEveryonesAI/qa_verify_cot_new_6M_v6

Filtered dataset:

• TV_valid_clean
• Rows: 5,461,079
• Clean parquet SHA-256: b1fbc4d5c05dbacbf9366055200a034551a46d70bfb2bff4c6432f2175a10d9b
• Filter rule:
  • is_valid == 1
  • contamination quarantine applied against target benchmark problems
• Tokenized reusable dataset:
  • argo11/0399-tv-valid-clean-sft-tokenized-llmjp4-8b
  • Rows: 5,461,079
  • Shards: 110
  • Max length: 4096
  • Columns: input_ids, attention_mask, labels, prompt_len, seq_len

The tokenized dataset was prepared on CPU and uploaded to Hugging Face to avoid repeated expensive tokenization on GPU nodes.

Training Procedure

Training mode:

• Full-parameter SFT
• No LoRA / PEFT
• Hugging Face Trainer
• FSDP: full_shard auto_wrap
• Assistant response tokens only are trained.
• Prompt tokens are masked with -100.

Core hyperparameters:

Infrastructure:

• ABCI 3.0
• Group: gcg51557
• Reserved queue: R9920261000
• Experiment directory: /groups/gcg51557/experiments/0399_tv_sft
• SFT jobs:
  • Thinking: 2004401.pbs1
  • Base: 2004402.pbs1
• HF checkpoint sync job:
  • 2004479.pbs1

Monitoring Status

The initial production SFT run was monitored past checkpoint-500.

Observed stability:

Notes:

• Both runs skipped GPU-side JSONL regeneration.
• Both runs used the uploaded tokenized dataset.
• W&B online logging was confirmed.
• checkpoint-500 was written locally and uploaded to Hugging Face Hub.
• Base run showed grad_norm=inf in early logs while loss remained finite. This should be considered during downstream quality review.

Uploaded Checkpoint Contents

Each checkpoint-500/ directory on Hub contains:

• model.safetensors
• config.json
• generation_config.json
• tokenizer files
• trainer_state.json
• training_args.bin
• RNG states
• scheduler.pt

The following large local training-state files are intentionally not uploaded to Hub:

• optimizer.bin
• pytorch_model_fsdp.bin

They are retained in the ABCI experiment directory for local recovery/debugging.

Evaluation

Full benchmark evaluation is not yet included in this model card.

Planned gates for experiment 0399:

Primary math gates:

• AIME 2024
• AIME 2025
• AIME 2026
• MATH-500

Regression gates:

• LiveCodeBench
• IFEval
• MT-Bench

Final-candidate-only gates:

• GPQA Diamond
• BBH
• MMLU-Pro

Do not treat this checkpoint as validated until these evaluations are complete.

Limitations

• This is an intermediate/full-param SFT checkpoint from an active experiment.
• The checkpoint is optimized for math reasoning style data and may regress in non-math tasks.
• Training data may contain long chain-of-thought style solutions; generated outputs may be verbose.
• Benchmark contamination mitigation was applied, but no contamination process is perfect.
• The uploaded checkpoint-500 is early in a longer training run and should not be interpreted as final model quality.
• Safety alignment was not the primary target of this experiment.

Citation / Attribution

Base models:

@misc{llmjp4,
  title = {LLM-jp-4 8B Models},
  author = {LLM-jp},
  year = {2026},
  url = {https://huggingface.co/llm-jp}
}

Experiment tracking:

• GitHub issue: https://github.com/llm-jp/experiments/issues/399
• W&B project: https://wandb.ai/argo-lab/llmjp4-8b-teamvictory-sft-difficulty-20260629

Reproducibility Metadata

Experiment ID: 0399

Experiment slug: tv_sft

Canonical experiment directory:

/groups/gcg51557/experiments/0399_tv_sft

Key manifests:

/groups/gcg51557/experiments/0399_tv_sft/manifests/tokenized_sft_tv_valid_clean_llmjp4_8b.json
/groups/gcg51557/experiments/0399_tv_sft/manifests/sft_stability_monitor_20260701.json
/groups/gcg51557/experiments/0399_tv_sft/manifests/hf_checkpoint_sync_2004479.pbs1.json

Training configs:

/groups/gcg51557/experiments/0399_tv_sft/configs/sft_full_thinking.yaml
/groups/gcg51557/experiments/0399_tv_sft/configs/sft_full_base.yaml