First post-training milestone (docs/18). Lands the verifiable task + its data
pipeline, all verified host-side (no CUDA); the SFT run itself reuses the
existing --sft-tsv path on the GPU box.
- task.rs: the shared task spec — two-operand integer arithmetic, answer in
\boxed{N}, with parse_boxed_answer + check_answer (exact-match rule-based
reward). One module reused by M1 (SFT data), M3 (DPO pairs), M4 (GRPO reward).
- gen_arith_task bin: writes arith_sft.tsv (--sft-tsv format) + held-out
arith_eval_prompts.txt (greedy_sample format) + arith_eval_gold.txt; train
deduped, eval disjoint from train.
- data.rs: extract assistant-only masking into a pure, testable sft_row()
(behavior-preserving; single-turn bit-identical to fbf4ac2).
Gate (verified locally, no_cuda): cargo test -p xtrain-train --lib = 9/9 pass
(masking, SFT-target self-consistency over 2000 samples, parser edges, seed
determinism); a 200/50 gen run = clean 2-col TSV, correct gold incl. negatives,
0 train/eval leakage. SFT training run + format-eval pending on dash5.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Enable assistant-only supervised fine-tuning and a fixed chat-prompt eval path
used by the v12 SFT runs:
- cross_entropy ignores negative targets (-100 ignore-index), normalizing by
valid rows instead of all rows; CUDA fwd/bwd skip t<0 (ops.rs, nn.cu).
- Corpus gains optional labels + load_sft_tsv_cached: two-column TSV is
formatted as 'User: .. \nAssistant:' + answer + <|endoftext|>, prompt tokens
masked to -100 while answer+EOS are supervised; i32 label cache alongside the
u16 token cache; sample() retries windows that are fully masked; eval uses
target_window so masking applies to val loss too (data.rs, train_loop.rs).
- train + train_ddp: --sft-tsv selects the TSV loader, --init-ckpt continues
training from a base checkpoint.
- greedy_sample: --prompts-file/--prompt/--temperature for fixed chat-prompt
generation eval.
Test fixtures updated for the new Corpus.labels field; dropout.rs carries
incidental rustfmt. Not rebuilt locally (no CUDA toolchain on this checkout);
correctness rests on the documented v12 base+SFT runs on the GPU box.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Corpus::load_cached: tokenize the (large) corpus ONCE, cache the id stream to
<corpus>.u16.bin (gpt2 vocab 50257 < 65536 → exact u16), read cache on reruns.
- Corpus::split_tail: hold out a tail slice as a validation corpus.
- train(): take an optional valid corpus + eval_every/eval_batches; periodic
deterministic val-loss eval that checkpoints the BEST val model; returns
TrainResult{train_losses, evals, best_val}. T6 fixed-cadence path preserved.
- bin/train + bin/export_safetensors: read architecture (--heads/--head-dim/
--layers/--ffn) + opt knobs (--steps/--batch/--seq/--max-lr/--val-tokens/
--eval-every) from CLI flags; defaults reproduce the v0-baseline tiny config.
- gitignore the multi-GB corpus + *.u16.bin caches + *.ckpt (dash5-only).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New xtrain-train crate scaffold. Data pipeline reuses xserv's from-scratch
GPT-2/Qwen BPE via a path-dep (../../../xserv/crates/xserv-tokenizer, resolves
on both ~/projects and dash5 /opt/wjh/projects): Corpus::load tokenizes the
corpus into one id stream and samples fixed-length (input, target) next-token
windows (LCG-seeded, reproducible). Trims a range-downloaded file to whole
stories (<|endoftext|> boundaries).
Also the host-only training math: LrSchedule (linear warmup + cosine decay)
and global L2 grad-norm + clip scale, each with a local unit test.
Corpus: data/tinystories-valid-3mb.txt — first ~3MB of TinyStories-valid
(fetched on dash5 via hf-mirror.com; HF direct unreachable). Substitution
noted: a real TinyStories subset, not the full set.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
