docs: backfill v9/v10 scaling runs + reframe README to v0–v10 / three phases
Add per-run design+result docs for the two Chinchilla-axis runs that were done but never committed: - v9 (dim1280 true-GQA, core 357M, 6.01B FineWeb tokens): double-axis scale, best moving-tail val 2.8854 (~3.2% below v8) — direction validated, gain still incremental, greedy repetition remains. - v10 (same arch, data-only top-up to 6.765B): moving-tail 2.8816; fixed eval v1 v6→v10 = 3.2328/3.1850/3.1515/2.9278/2.8814. Extend the comparison tables in docs/runs/README.md and docs/evolution.md to v10, and reframe README to v0–v10 with Phase 3 = the v9 double-axis run. No code changes. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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docs/runs/09-v9-fineweb-edu-dim1280-gqa.md
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# Scaling Run v9: Chinchilla 双轴 — dim1280/18L true GQA(core 356.9M) + FineWeb-edu 6.01B token + Phase-2 stack — Design Document
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## Goal
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v8 给出的元结论是：单独拨容量轴有用，但只有约 3% 的边际；单独重复旧数据也只有约 1.6% 的边际。要继续明显超过
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v8，必须把 **模型容量 + 新 token** 一起放大，而不是只拨一根轴。
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v9 就是这个双轴点：
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1. **模型轴**：dim1024/core 226M -> **dim1280/core 356.9M**，同时启用真 GQA（40 query heads / 10 kv heads）。
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2. **数据轴**：v6-v8 的 2.255B FineWeb 子集 -> **6.013B token**，追加了新 FineWeb-edu shards 003-009。
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3. **系统栈**：使用 Phase-2 现代路径：`--flash + --accum-steps + bf16 + recompute + DDP`。dropout 设为 0，按标准预训练。
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> v9 的 val 仍是 FineWeb-edu 分布，不能和 v0-v5 的 TinyStories val 直接比。注意：v9 扩展 cache 后默认
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> tail-heldout 已经从 v6-v8 的旧 tail 移到新 shards 末尾；严格横比后续以 fixed eval v1 为准。
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## Data
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| 项 | 值 |
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|----|----|
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| 来源 | FineWeb-edu `sample/10BT`，原 shards 000-002 + 新 shards 003-009 |
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| token cache | `data/fineweb-edu.txt.u16.bin` |
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| 总 token | **6,013,639,492** |
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| held-out val | 末尾 **1,000,000** token |
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| train corpus | 6,012,639,492 token |
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| 训练消费 token | **6,012,600,320** = 91745 steps x effective batch 256 x seq 256 |
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| epoch | ~1.00 |
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P3-DATA 目标本来是约 7B token；shard 010 下载 `curl rc=18` 中断，所以最终停在 6.01B。对 core 356.9M 来说，
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D/N 约 **16.8 token/param**，低于理想 Chinchilla 20，但已经远高于 v8 的约 10.4，是一个干净的双轴 scale 点。
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## Architecture
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| 项 | v8 | **v9** |
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|----|----|----|
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| dim | 1024 | **1280** |
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| layers | 18 | 18 |
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| query heads x head_dim | 32 x 32 | **40 x 32** |
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| kv heads | 32 (MHA) | **10 (true GQA, group=4)** |
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| ffn | 2730 | **4096** |
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| core params | 226.50M | **356.89M** |
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| total params | 329.42M | **485.55M** |
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| export tensors | 201 | **201** |
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`config.json` writes real `num_key_value_heads = 10`, so xserv loads v9 as true GQA rather than MHA.
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## Training
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| 项 | 值 |
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|----|----|
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| optimizer | hand-written AdamW, wd=0.1 |
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| schedule | warmup -> cosine, max_lr 6e-4 -> min_lr 6e-5 |
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| grad clip | global norm 1.0 |
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| steps | **91745** |
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| effective global batch | **256** (`--batch 128 --accum-steps 2`) |
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| seq_len | 256 |
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| precision | bf16 mixed precision, fp32 master |
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| memory stack | activation recompute + flash-attention + gradient accumulation |
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| world size | 8 x RTX 5090 |
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| wall clock | **21h15m** |
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| steady throughput | **~78.6K tok/s** |
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| peak observed memory | ~17GB / GPU |
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Command:
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```sh
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 cargo run -p xtrain-distributed --release --bin train_ddp -- \
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/opt/wjh/models/gpt2/tokenizer.json data/fineweb-edu.txt \
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--heads 40 --head-dim 32 --kv-heads 10 --layers 18 --ffn 4096 \
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--steps 91745 --batch 128 --accum-steps 2 --seq 256 \
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--max-lr 6e-4 --min-lr 6e-5 --val-tokens 1000000 --eval-every 1000 \
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--eval-batches 64 --bf16 --recompute --flash --dropout 0.0 \
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--ckpt /dashscope-tmp/wjh/xtrain_v9.ckpt
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```
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## Results
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- train loss: **11.1550 -> 2.9340**
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- first val: step 1000 = **5.1517**
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- best val: step 91000 = **2.8854**
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- final val: step 91745 = **2.8873**
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- exit code: **0**
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FineWeb val curve milestones:
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| step | 1000 | 10000 | 20000 | 30000 | 40000 | 50000 | 60000 | 70000 | 80000 | 90000 | 91000 | final |
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|------|------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|
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| val | 5.1517 | 3.4820 | 3.2953 | 3.2026 | 3.1422 | 3.0844 | 3.0148 | 2.9616 | 2.9160 | 2.8915 | **2.8854** | 2.8873 |
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The curve kept improving into the last 1K-step window, then the final eval bounced slightly from 2.8854 to 2.8873. This is close to
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the floor for this run, but not a clear overfit failure.
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## Comparison
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| | v6 | v7 | v8 | **v9** |
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|---|---|---|---|---|
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| model | dim768/core127M | dim768/core127M | dim1024/core226M | **dim1280/core357M + GQA** |
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| data | 2.29B | 3.28B same subset | 2.36B same subset | **6.01B expanded shards** |
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| best val | 3.0652 | 3.0149 | 2.9801 | **2.8854** |
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On the run-local moving tail, v9 beats v8 by **0.0947** val loss (~3.2% relative), essentially the same size as the
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v6->v8 capacity gain but now on top of it. A later fixed eval v1 check still supports the same direction
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(v8 3.1515 -> v9 2.9278 on shard010-tail holdout), while making the moving-tail caveat explicit. This confirms
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the v8 prediction: **双轴 scale 有效**. It is still an incremental gain, not a qualitative jump.
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## Samples
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xserv greedy samples (`--max-tokens 60`) are more coherent than the v8 examples on some prompts, but repetition remains:
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```text
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[The history of] the United States is the story of the people, the places, and the events that have shaped the nation...
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[In science,] the term "scientific method" is used to describe the process of gathering information and testing it...
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[The most important] thing is to be aware of the symptoms and to seek medical attention...
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[Water is] a natural resource that is essential for human life...
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```
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The model writes real explanatory English and the domain mix is FineWeb-like. Greedy decoding still falls into repeated clauses on
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some prompts (`scientific method`, symptoms, and earlier fixed prompts), so the val gain is more visible in the metric than in a
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dramatic sample-quality leap.
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## xserv validation
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Registry path:
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```text
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/opt/wjh/projects/tiny-models/v9-fineweb-edu-dim1280-gqa
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```
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Files:
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- `config.json`
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- `model.safetensors` (BF16, 201 tensors, 927MB)
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- `tokenizer.json`
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- `xtrain.ckpt` (fp32 master checkpoint, 1.9GB)
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- `RUN.md`
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xserv loads v9 as:
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```text
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Model: qwen3, layers=18, hidden=1280, heads=40/10 kv, vocab=50257
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Loaded 201 tensors
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Ready (KV cache, dtype=bf16).
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```
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Token-match check against xtrain greedy (`max-tokens 40`):
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- `Once upon a time`: xtrain and xserv matched through the checked continuation.
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- `One day`: diverged after "large, dark," (`very tall man` vs `metallic object`) from BF16 greedy tie sensitivity.
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- `The little`: same repetitive pattern, with a short BF16 path divergence.
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This is the same class of BF16-vs-f32 greedy drift seen in v8; the important integration result is that xserv successfully loads
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true GQA (`kv_heads=10 < heads=40`) and generates from the exported weights.
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