Datasets:

kepton0117
/

sn38-train-2021

date timestamp[s]date 2021-11-26 22:41:28 2021-12-09 12:19:05	text stringlengths 181 200k	source stringclasses 1 value
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End of preview. Expand in Data Studio

SN38 Round 12 — 2021 train algorithm and recipe

Public dataset + continue-pretrain recipe for Bittensor subnet 38, year 2021.

Submitted model: kepton0117/chrono-2021-sn38 @ f62e238265e3ca4a4ed20b300475513446d9e1fc (step-4000)
Data file: train_2021.jsonl (~23G, 13.97M docs, ~3.83B words after filter)
Cutoff: 2021-12-31
Base: anacoluthe89/chrono-2020 @ 633eb832c3de8eceb0b5bba1c5fb94faa8e777c6 (UID 131)
Do not submit that base SHA. Train until average weight cosine vs init is < 0.95.

Code copies live in recipe/. The trainer imports sn38.architectures (nanochrono). Clone github.com/kepton0117/sn38 on the H200 box; do not run recipe/train_2018_nanochrono.py alone.

Algorithm

Full-parameter continue-pretrain (CPT) of a causal LM. Not LoRA. Not SFT.

Same idea as Ibrahim et al. 2024 (simple CPT + replay + cosine re-warm):

loss = cross_entropy(model(tokens[:-1]), tokens[1:])

Piece	What we used
Objective	next-token causal LM
Parameters	all 2.02B `sn38-nanochrono` weights
Replay	15% FineWeb 2017–2020 so 2020 knowledge does not collapse
Packing	concatenate docs, cut at 2048, EOS between docs
Optimizer	AdamW, wd 0.1, betas (0.9, 0.95)
Schedule	cosine, 3% warmup
Parallel	8-GPU DDP (`torchrun`)
Copy gate	mean per-tensor cosine vs UID 131 every 500 steps

Mix

Share	Source	Why
60%	FineWeb dump subsets `CC-MAIN-2021-49` … `2021-31`	Late-2021 web fluency
15%	FineWeb `2020-50`, `2019-51`, `2018-51`, `2017-51`	Replay
25%	`brimmann2/enwiki-dec2021`	Known-gate facts

Never use FineWeb name='default' (includes 2022–2025). Never use wikipedia 20220301.en (March 2022 leak). Do not switch to FineWeb-Edu (leak gate failed on that mix last round).

Each jsonl row:

{"date": "2021-11-27T00:01:45Z", "text": "...", "source": "CC-MAIN-2021-49"}

Wiki rows use "date": "2021-12-31" and "source": "wikipedia-2021-12".

Filter (recipe/filter_2021_jsonl.py) dropped short/nav/repeat junk: kept 13,971,973 / dropped 1,992,448 (~12.5%). Packed to 2,610,770 × 2048 ≈ 5.35B tokens.

Train (8× H200)

Knob	Planned	What produced the pin
Hardware	8× H200	8× H200
Seq	2048	2048
Micro-batch / GPU	16	16
Grad accum	2	2
Tokens / opt step	524,288	524,288
Steps	15,000 (~7.9B tokens)	step-4000 submitted
LR	`3e-4` in launcher	`1e-3` (1e-4 and 3e-4 left cosine ~0.999)
Cosine vs 131 @ 4000	—	0.9294 (pass < 0.95)

On the H200:

hf download kepton0117/sn38-train-2021 train_2021.jsonl --repo-type dataset --local-dir data
# repo with sn38.architectures + scripts:
#   git clone git@github.com:kepton0117/sn38.git && cd sn38
LR=1e-3 bash scripts/run_train_h200x8_2021.sh

That launch is recipe/run_train_h200x8_2021.sh. After a checkpoint:

python3 scripts/train_2018_nanochrono.py --check-dedup \
  --base ./base-131-2020 --out ./chrono-2021
python3 debug/test_automodel.py ./chrono-2021

Submit the earliest checkpoint with cosine < 0.95 and clean completions. Keep the model public through Monday 13:00 UTC.

Rebuild the jsonl (optional)

python3 recipe/download_2021_data.py --out data/train_2021.jsonl --max-tokens 4000000000
python3 recipe/filter_2021_jsonl.py --in data/train_2021.jsonl --out data/train_2021.clean.jsonl

Files in this repo

Path	What
`train_2021.jsonl`	Filtered mix (the train file)
`README.md`	This card
`recipe/run_train_h200x8_2021.sh`	8× H200 launcher
`recipe/train_2018_nanochrono.py`	DDP CPT trainer
`recipe/download_2021_data.py`	FineWeb dumps + Dec-2021 wiki
`recipe/filter_2021_jsonl.py`	Junk filter
`recipe/upload_2021_data.sh`	How this dataset was uploaded

Downloads last month: -

Number of rows:

1,485,996

Estimated number of rows:

4,721,442

Total file size:

24.3 GB