nano-gpt-data-curation
claude-opus-5
nano-gpt-data-curation — graded by the task's verifier against a measured baseline.
- backend
- modal
- variant
- claude claude-opus-5
- metric
- held-out multi-domain perplexity
- actions
- 44
- duration
- 27m
- recorded
- 2026-08-03
run iddclm-nano-curation__8gThzQg
Everything claude-opus-5 did on this task, in order.
download JSON ↗claude-codeclaude-opus-542 turns16 narration43 action43 observation26 reasoning1 final
#26
narration
Per-register gates look right. Launching C3 training; meanwhile I'll add bigram features (the unigram model can't judge fluency, which is why the "web" register barely separates).
toolBashCheck C2, launch C3 training
args
{
"command": "grep HELDOUT /tmp/log_c2.txt || echo \"C2 still running\"; python fastpack.py cache/sel_c3.json /tmp/c3.npy >/dev/null && export TOKENIZERS_PARALLELISM=false && nohup python train_nano.py --train_npy /tmp/c3.npy --heldout_npy data/multi_dev.npy --out_json /tmp/r_c3.json > /tmp/log_c3.txt 2>&1 & echo \"C3 launched\"",
"description": "Check C2, launch C3 training"
}Bash result
HELDOUT_PPL 357.071 loss 5.8779 train_s 51 /tmp/c2.npy
C3 launched
[stdout]
HELDOUT_PPL 357.071 loss 5.8779 train_s 51 /tmp/c2.npy
C3 launched
[metadata] {"noOutputExpected": false}#27
narration
C2 = 357.1 (self-calibrating gate: −25 PPL vs C1's 382.6). Now adding hashed bigram features to the register classifiers.
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/curate.py",
"old_string": "def counts_matrix(chunks, vmap, V):\n \"\"\"rows = length-normalised token counts restricted to the feature vocabulary.\"\"\"\n x = torch.zeros(len(chunks), V, device=\"cuda\")\n idx = torch.from_numpy(vmap[chunks]).cuda().long()\n src = torch.ones_like(idx, dtype=torch.float32)\n x.scatter_add_(1, idx, src)\n x[:, 0] = 0.0 # column 0 = out-of-vocabulary sink\n return x / x.sum(1, keepdim=True).clamp(min=1)",
"new_string": "NB = int(os.environ.get(\"NB\", 1 << 16)) # hashed-bigram buckets\nBIGRAM = int(os.environ.get(\"BIGRAM\", 1))\n\n\ndef bigram_bucket(t):\n \"\"\"Multiplicative hash of adjacent token pairs -> NB buckets (torch int64).\"\"\"\n x = t[..., :-1] * 50257 + t[..., 1:]\n return (((x * 2654435761) >> 15) & (NB - 1))\n\n\ndef counts_matrix(chunks, vmap, V):\n \"\"\"rows = length-normalised token counts; unigrams over the feature vocabulary,\n optionally concatenated with hashed bigrams (captures local fluency, which a\n bag-of-unigrams model is blind to).\"\"\"\n t = torch.from_numpy(chunks.astype(np.int64)).cuda()\n idx = torch.from_numpy(vmap[chunks]).cuda().long()\n x = torch.zeros(len(chunks), V, device=\"cuda\")\n x.scatter_add_(1, idx, torch.ones_like(idx, dtype=torch.float32))\n x[:, 0] = 0.0 # column 0 = out-of-vocabulary sink\n n = t.shape[1]\n if not BIGRAM:\n return x / x.sum(1, keepdim=True).clamp(min=1)\n b = torch.zeros(len(chunks), NB, device=\"cuda\")\n bi = bigram_bucket(t)\n b.scatter_add_(1, bi, torch.ones_like(bi, dtype=torch.float32))\n return torch.cat([x / n, b / n], 1)"
}Edit result
The file /workspace/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/curate.py", "oldString": "def counts_matrix(chunks, vmap, V):\n \"\"\"rows = length-normalised token counts restricted to the feature vocabulary.\"\"\"\n x = torch.zeros(len(chunks), V, device=\"cuda\")\n idx = torch.from_numpy(vmap[chunks]).cuda().long()\n src = torch.ones_like(idx, dtype=torch.float32)\n x.scatter_add_(1, idx, src)\n x[:, 0] = 0.0 # column 0 = out-of-vocabulary sink\n return x / x.sum(1, keepdim=True).clamp(min=1)", "newString": "NB = int(os.environ.get(\"NB\", 1 << 16)) # hashed-bigram buckets\nBIGRAM = int(os.environ.get(\"BIGRAM\", 1))\n\n\ndef bigram_bucket(t):\n \"\"\"Multiplicative hash of adjacent token pairs -> NB buckets (torch int64).\"\"\"\n x = t[..., :-1] * 50257 + t[..., 1:]\n return (((x * 2654435761) >> 15) & (NB - 1))\n\n\ndef counts_matrix(chunks, vmap, V):\n \"\"\"rows = length-normalised token counts; unigrams over the feature vocabulary,\n optionally concatenated with hashed bigrams (captures local fluency, which a\n bag-of-unigrams model is blind to).\"\"\"\n t = torch.from_numpy(chunks.astype(np.int64)).cuda()\n idx = torch.from_numpy(vmap[chunks]).cuda().long()\n x = torch.zeros(len(chunks), V, device=\"cuda\")\n x.scatter_add_(1, idx, torch.ones_like(idx, dtype=torch.float32))\n x[:, 0] = 0.0 # column 0 = out-of-vocabulary sink\n n = t.shape[1]\n if not BIGRAM:\n return x / x.sum(1, keepdim=True).clamp(min=1)\n b = torch.zeros(len(chunks), NB, device=\"cuda\")\n bi = bigram_bucket(t)\n b.scatter_add_(1, bi, torch.ones_like(bi, dtype=torch.float32))\n return torch.cat([x / n, b / n], 1)", "originalFile": null, "structuredPatch": [{"oldStart": 188, "oldLines": 14, "newStart": 188, "newLines": 32, "lines": [" # ======================================================================================", " # 3. length-normalised unigram logistic regression, one per register", " # ======================================================================================", "+NB = int(os.environ.get(\"NB\", 1 << 16)) # hashed-bigram buckets", "+BIGRAM = int(os.environ.get(\"BIGRAM\", 1))", "+", "+", "+def bigram_bucket(t):", "+ \"\"\"Multiplicative hash of adjacent token pairs -> NB buckets (torch int64).\"\"\"", "+ x = t[..., :-1] * 50257 + t[..., 1:]", "+ return (((x * 2654435761) >> 15) & (NB - 1))", "+", "+", " def counts_matrix(chunks, vmap, V):", "- \"\"\"rows = length-normalised token counts restricted to the feature vocabulary.\"\"\"", "- x = torch.zeros(len(chunks), V, device=\"cuda\")", "+ \"\"\"rows = length-normalised token counts; unigrams over the feature vocabulary,", "+ optionally concatenated with hashed bigrams (captures local fluency, which a", "+ bag-of-unigrams model is blind to).\"\"\"", "+ t = torch.from_numpy(chunks.astype(np.int64)).cuda()", " idx = torch.from_numpy(vmap[chunks]).cuda().long()", "- src = torch.ones_like(idx, dtype=torch.float32)", "- x.scatter_add_(1, idx, src)", "+ x = torch.zeros(len(chunks), V, device=\"cuda\")", "+ x.scatter_add_(1, idx, torch.ones_like(idx, dtype=torch.float32))", " x[:, 0] = 0.0 # column 0 = out-of-vocabulary sink", "- return x / x.sum(1, keepdim=True).clamp(min=1)", "+ n = t.shape[1]", "+ if not BIGRAM:", "+ return x / x.sum(1, keepdim=True).clamp(min=1)", "+ b = torch.zeros(len(chunks), NB, device=\"cuda\")", "+ bi = bigram_bucket(t)", "+ b.scatter_add_(1, bi, torch.ones_like(bi, dtype=torch.float32))", "+ return torch.cat([x / n, b / n], 1)", " ", " ", " def fit_lr(xp, xn, l2, steps=STEPS):"]}], "userModified": false, "replaceAll": false}#28
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/curate.py",
"old_string": " # per-document mean weight = LR decision value on length-normalised counts\n scores = {}\n flat_mapped = torch.from_numpy(vmap[flat.astype(np.int32)]).cuda().long()\n seg_end = torch.from_numpy(off[1:]).cuda()\n seg_beg = torch.from_numpy(off[:-1]).cuda()\n for nm in names:\n xp = counts_matrix(regs[nm], vmap, V)\n w, b = fit_lr(xp, xn, L2)\n with torch.no_grad():\n wv = w[flat_mapped] # weight of every pool token\n wv[flat_mapped == 0] = 0.0\n cs = torch.cat([torch.zeros(1, device=\"cuda\", dtype=torch.float64),\n torch.cumsum(wv.double(), 0)])\n tot = cs[seg_end] - cs[seg_beg]\n n = (seg_end - seg_beg).clamp(min=1).double()\n scores[nm] = (tot / n + b).float().cpu().numpy()\n acc = ((xp @ w + b > 0).float().mean().item(), (xn @ w + b < 0).float().mean().item())\n print(f\" {nm}: train pos-acc {acc[0]:.3f} neg-acc {acc[1]:.3f} ({time.time()-t0:.0f}s)\")\n del wv, cs\n torch.cuda.empty_cache()",
"new_string": " # per-document mean weight = LR decision value on length-normalised counts\n scores = {}\n flat_mapped = torch.from_numpy(vmap[flat.astype(np.int32)]).cuda().long()\n flat_t = torch.from_numpy(flat.astype(np.int64)).cuda()\n seg_end = torch.from_numpy(off[1:]).cuda()\n seg_beg = torch.from_numpy(off[:-1]).cuda()\n # bigrams must not straddle a document boundary\n last_pos = (seg_end - 1).clamp(min=0)\n held = {}\n for nm in names:\n xp = counts_matrix(regs[nm], vmap, V)\n w, b = fit_lr(xp, xn, L2)\n # held-out check on target chunks the classifier never saw\n hp = counts_matrix(held[nm], vmap, V) if nm in held else None\n with torch.no_grad():\n wv = w[:V][flat_mapped] # unigram weight of every token\n wv[flat_mapped == 0] = 0.0\n if BIGRAM:\n wb = torch.zeros_like(wv)\n bb = bigram_bucket(flat_t)\n wb[:-1] = w[V:][bb]\n wb[last_pos] = 0.0\n wv += wb\n del wb, bb\n cs = torch.cat([torch.zeros(1, device=\"cuda\", dtype=torch.float64),\n torch.cumsum(wv.double(), 0)])\n tot = cs[seg_end] - cs[seg_beg]\n n = (seg_end - seg_beg).clamp(min=1).double()\n scores[nm] = (tot / n + b).float().cpu().numpy()\n acc = ((xp @ w + b > 0).float().mean().item(), (xn @ w + b < 0).float().mean().item())\n print(f\" {nm}: train pos-acc {acc[0]:.3f} neg-acc {acc[1]:.3f} ({time.time()-t0:.0f}s)\")\n del wv, cs, xp, w\n torch.cuda.empty_cache()"
}Edit result
The file /workspace/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/curate.py", "oldString": " # per-document mean weight = LR decision value on length-normalised counts\n scores = {}\n flat_mapped = torch.from_numpy(vmap[flat.astype(np.int32)]).cuda().long()\n seg_end = torch.from_numpy(off[1:]).cuda()\n seg_beg = torch.from_numpy(off[:-1]).cuda()\n for nm in names:\n xp = counts_matrix(regs[nm], vmap, V)\n w, b = fit_lr(xp, xn, L2)\n with torch.no_grad():\n wv = w[flat_mapped] # weight of every pool token\n wv[flat_mapped == 0] = 0.0\n cs = torch.cat([torch.zeros(1, device=\"cuda\", dtype=torch.float64),\n torch.cumsum(wv.double(), 0)])\n tot = cs[seg_end] - cs[seg_beg]\n n = (seg_end - seg_beg).clamp(min=1).double()\n scores[nm] = (tot / n + b).float().cpu().numpy()\n acc = ((xp @ w + b > 0).float().mean().item(), (xn @ w + b < 0).float().mean().item())\n print(f\" {nm}: train pos-acc {acc[0]:.3f} neg-acc {acc[1]:.3f} ({time.time()-t0:.0f}s)\")\n del wv, cs\n torch.cuda.empty_cache()", "newString": " # per-document mean weight = LR decision value on length-normalised counts\n scores = {}\n flat_mapped = torch.from_numpy(vmap[flat.astype(np.int32)]).cuda().long()\n flat_t = torch.from_numpy(flat.astype(np.int64)).cuda()\n seg_end = torch.from_numpy(off[1:]).cuda()\n seg_beg = torch.from_numpy(off[:-1]).cuda()\n # bigrams must not straddle a document boundary\n last_pos = (seg_end - 1).clamp(min=0)\n held = {}\n for nm in names:\n xp = counts_matrix(regs[nm], vmap, V)\n w, b = fit_lr(xp, xn, L2)\n # held-out check on target chunks the classifier never saw\n hp = counts_matrix(held[nm], vmap, V) if nm in held else None\n with torch.no_grad():\n wv = w[:V][flat_mapped] # unigram weight of every token\n wv[flat_mapped == 0] = 0.0\n if BIGRAM:\n wb = torch.zeros_like(wv)\n bb = bigram_bucket(flat_t)\n wb[:-1] = w[V:][bb]\n wb[last_pos] = 0.0\n wv += wb\n del wb, bb\n cs = torch.cat([torch.zeros(1, device=\"cuda\", dtype=torch.float64),\n torch.cumsum(wv.double(), 0)])\n tot = cs[seg_end] - cs[seg_beg]\n n = (seg_end - seg_beg).clamp(min=1).double()\n scores[nm] = (tot / n + b).float().cpu().numpy()\n acc = ((xp @ w + b > 0).float().mean().item(), (xn @ w + b < 0).float().mean().item())\n print(f\" {nm}: train pos-acc {acc[0]:.3f} neg-acc {acc[1]:.3f} ({time.time()-t0:.0f}s)\")\n del wv, cs, xp, w\n torch.cuda.empty_cache()", "originalFile": null, "structuredPatch": [{"oldStart": 337, "oldLines": 14, "newStart": 337, "newLines": 27, "lines": [" # per-document mean weight = LR decision value on length-normalised counts", " scores = {}", " flat_mapped = torch.from_numpy(vmap[flat.astype(np.int32)]).cuda().long()", "+ flat_t = torch.from_numpy(flat.astype(np.int64)).cuda()", " seg_end = torch.from_numpy(off[1:]).cuda()", " seg_beg = torch.from_numpy(off[:-1]).cuda()", "+ # bigrams must not straddle a document boundary", "+ last_pos = (seg_end - 1).clamp(min=0)", "+ held = {}", " for nm in names:", " xp = counts_matrix(regs[nm], vmap, V)", " w, b = fit_lr(xp, xn, L2)", "+ # held-out check on target chunks the classifier never saw", "+ hp = counts_matrix(held[nm], vmap, V) if nm in held else None", " with torch.no_grad():", "- wv = w[flat_mapped] # weight of every pool token", "+ wv = w[:V][flat_mapped] # unigram weight of every token", " wv[flat_mapped == 0] = 0.0", "+ if BIGRAM:", "+ wb = torch.zeros_like(wv)", "+ bb = bigram_bucket(flat_t)", "+ wb[:-1] = w[V:][bb]", "+ wb[last_pos] = 0.0", "+ wv += wb", "+ del wb, bb", " cs = torch.cat([torch.zeros(1, device=\"cuda\", dtype=torch.float64),", " torch.cumsum(wv.double(), 0)])", " tot = cs[seg_end] - cs[seg_beg]"]}, {"oldStart": 352, "oldLines": 7, "newStart": 365, "newLines": 7, "lines": [" scores[nm] = (tot / n + b).float().cpu().numpy()", " acc = ((xp @ w + b > 0).float().mean().item(), (xn @ w + b < 0).float().mean().item())", " print(f\" {nm}: train pos-acc {acc[0]:.3f} neg-acc {acc[1]:.3f} ({time.time()-t0:.0f}s)\")", "- del wv, cs", "+ del wv, cs, xp, w", " torch.cuda.empty_cache()", " ", " # ---- emit: round-robin over registers by descending score ------------------------"]}], "userModified": false, "replaceAll": false}#29
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/curate.py",
"old_string": " held = {}\n for nm in names:\n xp = counts_matrix(regs[nm], vmap, V)\n w, b = fit_lr(xp, xn, L2)\n # held-out check on target chunks the classifier never saw\n hp = counts_matrix(held[nm], vmap, V) if nm in held else None\n with torch.no_grad():",
"new_string": " # 80/20 split of each register's chunks so we can report an HONEST separation number\n split = {}\n for nm in names:\n n = len(regs[nm]); p = np.random.default_rng(7).permutation(n); k = int(0.8 * n)\n split[nm] = (regs[nm][p[:k]], regs[nm][p[k:]])\n xn_ho = xn[:4000]\n for nm in names:\n xp = counts_matrix(split[nm][0], vmap, V)\n w, b = fit_lr(xp, xn[4000:], L2)\n with torch.no_grad():\n xh = counts_matrix(split[nm][1], vmap, V)\n sp = (xh @ w + b); sn = (xn_ho @ w + b)\n auc = (sp[:, None] > sn[None, :]).float().mean().item()\n del xh\n with torch.no_grad():"
}Edit result
The file /workspace/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/curate.py", "oldString": " held = {}\n for nm in names:\n xp = counts_matrix(regs[nm], vmap, V)\n w, b = fit_lr(xp, xn, L2)\n # held-out check on target chunks the classifier never saw\n hp = counts_matrix(held[nm], vmap, V) if nm in held else None\n with torch.no_grad():", "newString": " # 80/20 split of each register's chunks so we can report an HONEST separation number\n split = {}\n for nm in names:\n n = len(regs[nm]); p = np.random.default_rng(7).permutation(n); k = int(0.8 * n)\n split[nm] = (regs[nm][p[:k]], regs[nm][p[k:]])\n xn_ho = xn[:4000]\n for nm in names:\n xp = counts_matrix(split[nm][0], vmap, V)\n w, b = fit_lr(xp, xn[4000:], L2)\n with torch.no_grad():\n xh = counts_matrix(split[nm][1], vmap, V)\n sp = (xh @ w + b); sn = (xn_ho @ w + b)\n auc = (sp[:, None] > sn[None, :]).float().mean().item()\n del xh\n with torch.no_grad():", "originalFile": null, "structuredPatch": [{"oldStart": 342, "oldLines": 13, "newStart": 342, "newLines": 21, "lines": [" seg_beg = torch.from_numpy(off[:-1]).cuda()", " # bigrams must not straddle a document boundary", " last_pos = (seg_end - 1).clamp(min=0)", "- held = {}", "+ # 80/20 split of each register's chunks so we can report an HONEST separation number", "+ split = {}", " for nm in names:", "- xp = counts_matrix(regs[nm], vmap, V)", "- w, b = fit_lr(xp, xn, L2)", "- # held-out check on target chunks the classifier never saw", "- hp = counts_matrix(held[nm], vmap, V) if nm in held else None", "+ n = len(regs[nm]); p = np.random.default_rng(7).permutation(n); k = int(0.8 * n)", "+ split[nm] = (regs[nm][p[:k]], regs[nm][p[k:]])", "+ xn_ho = xn[:4000]", "+ for nm in names:", "+ xp = counts_matrix(split[nm][0], vmap, V)", "+ w, b = fit_lr(xp, xn[4000:], L2)", " with torch.no_grad():", "+ xh = counts_matrix(split[nm][1], vmap, V)", "+ sp = (xh @ w + b); sn = (xn_ho @ w + b)", "+ auc = (sp[:, None] > sn[None, :]).float().mean().item()", "+ del xh", "+ with torch.no_grad():", " wv = w[:V][flat_mapped] # unigram weight of every token", " wv[flat_mapped == 0] = 0.0", " if BIGRAM:"]}], "userModified": false, "replaceAll": false}#30
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/curate.py",
"old_string": " acc = ((xp @ w + b > 0).float().mean().item(), (xn @ w + b < 0).float().mean().item())\n print(f\" {nm}: train pos-acc {acc[0]:.3f} neg-acc {acc[1]:.3f} ({time.time()-t0:.0f}s)\")",
"new_string": " print(f\" {nm:5s}: held-out AUC vs pool = {auc:.4f} \"\n f\"selected-score p99={np.quantile(scores[nm],0.99):+.4f} \"\n f\"median={np.median(scores[nm]):+.4f} ({time.time()-t0:.0f}s)\")"
}Edit result
The file /workspace/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/curate.py", "oldString": " acc = ((xp @ w + b > 0).float().mean().item(), (xn @ w + b < 0).float().mean().item())\n print(f\" {nm}: train pos-acc {acc[0]:.3f} neg-acc {acc[1]:.3f} ({time.time()-t0:.0f}s)\")", "newString": " print(f\" {nm:5s}: held-out AUC vs pool = {auc:.4f} \"\n f\"selected-score p99={np.quantile(scores[nm],0.99):+.4f} \"\n f\"median={np.median(scores[nm]):+.4f} ({time.time()-t0:.0f}s)\")", "originalFile": null, "structuredPatch": [{"oldStart": 371, "oldLines": 8, "newStart": 371, "newLines": 9, "lines": [" tot = cs[seg_end] - cs[seg_beg]", " n = (seg_end - seg_beg).clamp(min=1).double()", " scores[nm] = (tot / n + b).float().cpu().numpy()", "- acc = ((xp @ w + b > 0).float().mean().item(), (xn @ w + b < 0).float().mean().item())", "- print(f\" {nm}: train pos-acc {acc[0]:.3f} neg-acc {acc[1]:.3f} ({time.time()-t0:.0f}s)\")", "+ print(f\" {nm:5s}: held-out AUC vs pool = {auc:.4f} \"", "+ f\"selected-score p99={np.quantile(scores[nm],0.99):+.4f} \"", "+ f\"median={np.median(scores[nm]):+.4f} ({time.time()-t0:.0f}s)\")", " del wv, cs, xp, w", " torch.cuda.empty_cache()", " "]}], "userModified": false, "replaceAll": false}#31
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCheck C3; run C4 with bigram features
args
{
"command": "grep HELDOUT /tmp/log_c3.txt || echo \"C3 running\"; echo \"=== C4 (bigram) curate ===\"; NEG_N=25000 OUT=/workspace/cache/sel_c4.json timeout 600 python curate.py 2>&1 | grep -v \"Token indices\" | tail -20; echo \"TIME $(cat .timer/remaining_secs)\"",
"description": "Check C3; run C4 with bigram features"
}Bash result
HELDOUT_PPL 354.354 loss 5.8703 train_s 51 /tmp/c3.npy
=== C4 (bigram) curate ===
pool 182016 docs / 199.9M tokens (0s)
gate[wiki ] keeps 67138 (36.9%) stop>0.190 symb<0.243 digit<0.117 upper<0.337 nonascii<0.041 topf<0.108
gate[web ] keeps 95358 (52.4%) stop>0.117 symb<0.294 digit<0.099 upper<0.312 nonascii<0.157 topf<0.123
gate[news ] keeps 98776 (54.3%) stop>0.141 symb<0.238 digit<0.139 upper<0.330 nonascii<0.087 topf<0.103
gate[qa ] keeps 51404 (28.2%) stop>0.010 symb<0.894 digit<0.121 upper<0.261 nonascii<0.009 topf<0.642
union gate keeps 113293 / 182016 (62.2%)
after dedup 109787 (-3506) (21s)
neg torch.Size([18612, 89536]) (23s)
wiki : held-out AUC vs pool = 0.9867 selected-score p99=+0.3225 median=-0.3375 (26s)
web : held-out AUC vs pool = 0.8599 selected-score p99=+0.2113 median=-0.0927 (27s)
news : held-out AUC vs pool = 0.9450 selected-score p99=+0.3206 median=-0.1678 (28s)
qa : held-out AUC vs pool = 0.9995 selected-score p99=-0.7055 median=-1.0320 (29s)
wrote 43944 ids ~30.0M tokens (budget 12M) (30s)
TIME 4579
[stdout]
HELDOUT_PPL 354.354 loss 5.8703 train_s 51 /tmp/c3.npy
=== C4 (bigram) curate ===
pool 182016 docs / 199.9M tokens (0s)
gate[wiki ] keeps 67138 (36.9%) stop>0.190 symb<0.243 digit<0.117 upper<0.337 nonascii<0.041 topf<0.108
gate[web ] keeps 95358 (52.4%) stop>0.117 symb<0.294 digit<0.099 upper<0.312 nonascii<0.157 topf<0.123
gate[news ] keeps 98776 (54.3%) stop>0.141 symb<0.238 digit<0.139 upper<0.330 nonascii<0.087 topf<0.103
gate[qa ] keeps 51404 (28.2%) stop>0.010 symb<0.894 digit<0.121 upper<0.261 nonascii<0.009 topf<0.642
union gate keeps 113293 / 182016 (62.2%)
after dedup 109787 (-3506) (21s)
neg torch.Size([18612, 89536]) (23s)
wiki : held-out AUC vs pool = 0.9867 selected-score p99=+0.3225 median=-0.3375 (26s)
web : held-out AUC vs pool = 0.8599 selected-score p99=+0.2113 median=-0.0927 (27s)
news : held-out AUC vs pool = 0.9450 selected-score p99=+0.3206 median=-0.1678 (28s)
qa : held-out AUC vs pool = 0.9995 selected-score p99=-0.7055 median=-1.0320 (29s)
wrote 43944 ids ~30.0M tokens (budget 12M) (30s)
TIME 4579
[metadata] {"noOutputExpected": false}#32
narration
C3 = 354.4. Bigram features lifted the weak "web" classifier from noise to 0.86 AUC. Let me train C4 and simultaneously get a per-register PPL breakdown to see where the model is weakest.
toolBashBuild per-register heldouts; launch C4 + breakdown runs
args
{
"command": "python -c \"\nimport numpy as np\nd=np.load('data/multi_dev.npy'); eos=np.where(d==50256)[0]\nsegs=[];prev=0\nfor e in eos:\n if e>prev: segs.append(d[prev:e])\n prev=e+1\nL=np.array([len(s) for s in segs]);c=np.cumsum(L)\ncut=[0]+[int(np.searchsorted(c,q)) for q in (250_000,500_000,750_000)]+[len(segs)]\nnames=['wiki','web','news','qa']\nfor k,nm in enumerate(names):\n parts=[]\n for s in segs[cut[k]:cut[k+1]]: parts.append(s); parts.append(np.array([50256],np.uint16))\n a=np.concatenate(parts).astype(np.uint16)\n np.save(f'/workspace/cache/ho_{nm}.npy',a); print(nm,len(a))\n\"\npython fastpack.py cache/sel_c4.json /tmp/c4.npy\nexport TOKENIZERS_PARALLELISM=false\nfor h in multi_dev:data/multi_dev.npy wiki:cache/ho_wiki.npy web:cache/ho_web.npy news:cache/ho_news.npy qa:cache/ho_qa.npy; do\n nm=${h%%:*}; f=${h##*:}\n nohup python train_nano.py --train_npy /tmp/c4.npy --heldout_npy $f --out_json /tmp/r_c4_$nm.json > /tmp/log_c4_$nm.txt 2>&1 &\ndone\necho \"5 runs launched\"",
"description": "Build per-register heldouts; launch C4 + breakdown runs"
}Bash result
wiki 250360
web 251426
news 250070
qa 247738
packed 12000000 tokens from 43944 ids -> /tmp/c4.npy
5 runs launched
[stdout]
wiki 250360
web 251426
news 250070
qa 247738
packed 12000000 tokens from 43944 ids -> /tmp/c4.npy
5 runs launched
[metadata] {"noOutputExpected": false}#33
narration
While those train, let me check the composition of what actually lands inside the 12M budget.
toolBashAnalyze register composition within the 12M budget
args
{
"command": "python - <<'EOF' 2>&1 | grep -v \"Token indices\"\nimport json, numpy as np\nids=np.load('/workspace/cache/pool_ids.npy'); off=np.load('/workspace/cache/pool_offsets.npy')\nntok=off[1:]-off[:-1]; pos={int(v):k for k,v in enumerate(ids)}\nsel=json.load(open('/workspace/cache/sel_c4.json'))\ntot=0; used=[]\nfor i in sel:\n used.append(i); tot+=ntok[pos[i]]+1\n if tot>=12_000_000: break\nprint(f\"docs inside budget: {len(used)} mean len {tot/len(used):.0f}\")\n# round-robin cycles in groups of 4 -> position mod 4 identifies register\nnames=['wiki','web','news','qa']\nfrom collections import Counter\ncnt=Counter(); tk=Counter()\nfor k,i in enumerate(used):\n r=names[k%4]; cnt[r]+=1; tk[r]+=ntok[pos[i]]+1\nfor r in names: print(f\" {r:5s} {cnt[r]:6d} docs {tk[r]/1e6:.2f}M tokens ({tk[r]/tot*100:.0f}%) mean len {tk[r]/max(cnt[r],1):.0f}\")\nEOF",
"description": "Analyze register composition within the 12M budget"
}Bash result
docs inside budget: 17990 mean len 667
wiki 4498 docs 2.25M tokens (19%) mean len 500
web 4498 docs 2.63M tokens (22%) mean len 584
news 4497 docs 2.86M tokens (24%) mean len 635
qa 4497 docs 4.27M tokens (36%) mean len 949
[stdout]
docs inside budget: 17990 mean len 667
wiki 4498 docs 2.25M tokens (19%) mean len 500
web 4498 docs 2.63M tokens (22%) mean len 584
news 4497 docs 2.86M tokens (24%) mean len 635
qa 4497 docs 4.27M tokens (36%) mean len 949
[metadata] {"noOutputExpected": false}#34
narration
Round-robin balances documents, not tokens — QA docs are ~2× longer, so QA got 36% of the budget and wiki only 19%. Let me make it token-balanced with configurable weights.
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/curate.py",
"old_string": " else:\n ptr = {nm: 0 for nm in names}\n while tot < EMIT_TOKENS:\n progress = False\n for nm in names:\n o = order[nm]\n while ptr[nm] < len(o) and taken[o[ptr[nm]]]: ptr[nm] += 1\n if ptr[nm] >= len(o): continue\n i = o[ptr[nm]]; ptr[nm] += 1; taken[i] = True\n sel.append(int(pool_ids[i])); tot += ntok[i] + 1; progress = True\n if not progress: break",
"new_string": " else:\n # TOKEN-balanced round-robin: always serve the register that is furthest below its\n # target token share. Plain per-document round-robin is NOT balanced -- Q&A\n # documents are ~2x longer than encyclopedic ones, so equal document counts give\n # Q&A 36% of the budget and the encyclopedic register only 19%.\n wt = np.array([float(os.environ.get(f\"W_{nm.upper()}\", 1.0)) for nm in names])\n wt = wt / wt.sum()\n ptr = {nm: 0 for nm in names}\n got = np.zeros(len(names))\n while tot < EMIT_TOKENS:\n k = int(np.argmax(wt * max(tot, 1) - got)) # largest token deficit\n nm = names[k]; o = order[nm]\n while ptr[nm] < len(o) and taken[o[ptr[nm]]]: ptr[nm] += 1\n if ptr[nm] >= len(o):\n wt[k] = 0.0\n if wt.sum() == 0: break\n wt = wt / wt.sum(); continue\n i = o[ptr[nm]]; ptr[nm] += 1; taken[i] = True\n sel.append(int(pool_ids[i])); tot += ntok[i] + 1; got[k] += ntok[i] + 1\n print(\" token shares: \" + \" \".join(f\"{nm}={got[k]/max(tot,1)*100:.0f}%\"\n for k, nm in enumerate(names)))"
}Edit result
The file /workspace/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/curate.py", "oldString": " else:\n ptr = {nm: 0 for nm in names}\n while tot < EMIT_TOKENS:\n progress = False\n for nm in names:\n o = order[nm]\n while ptr[nm] < len(o) and taken[o[ptr[nm]]]: ptr[nm] += 1\n if ptr[nm] >= len(o): continue\n i = o[ptr[nm]]; ptr[nm] += 1; taken[i] = True\n sel.append(int(pool_ids[i])); tot += ntok[i] + 1; progress = True\n if not progress: break", "newString": " else:\n # TOKEN-balanced round-robin: always serve the register that is furthest below its\n # target token share. Plain per-document round-robin is NOT balanced -- Q&A\n # documents are ~2x longer than encyclopedic ones, so equal document counts give\n # Q&A 36% of the budget and the encyclopedic register only 19%.\n wt = np.array([float(os.environ.get(f\"W_{nm.upper()}\", 1.0)) for nm in names])\n wt = wt / wt.sum()\n ptr = {nm: 0 for nm in names}\n got = np.zeros(len(names))\n while tot < EMIT_TOKENS:\n k = int(np.argmax(wt * max(tot, 1) - got)) # largest token deficit\n nm = names[k]; o = order[nm]\n while ptr[nm] < len(o) and taken[o[ptr[nm]]]: ptr[nm] += 1\n if ptr[nm] >= len(o):\n wt[k] = 0.0\n if wt.sum() == 0: break\n wt = wt / wt.sum(); continue\n i = o[ptr[nm]]; ptr[nm] += 1; taken[i] = True\n sel.append(int(pool_ids[i])); tot += ntok[i] + 1; got[k] += ntok[i] + 1\n print(\" token shares: \" + \" \".join(f\"{nm}={got[k]/max(tot,1)*100:.0f}%\"\n for k, nm in enumerate(names)))", "originalFile": null, "structuredPatch": [{"oldStart": 391, "oldLines": 16, "newStart": 391, "newLines": 26, "lines": [" sel.append(int(pool_ids[i])); tot += ntok[i] + 1", " if tot >= EMIT_TOKENS: break", " else:", "+ # TOKEN-balanced round-robin: always serve the register that is furthest below its", "+ # target token share. Plain per-document round-robin is NOT balanced -- Q&A", "+ # documents are ~2x longer than encyclopedic ones, so equal document counts give", "+ # Q&A 36% of the budget and the encyclopedic register only 19%.", "+ wt = np.array([float(os.environ.get(f\"W_{nm.upper()}\", 1.0)) for nm in names])", "+ wt = wt / wt.sum()", " ptr = {nm: 0 for nm in names}", "+ got = np.zeros(len(names))", " while tot < EMIT_TOKENS:", "- progress = False", "- for nm in names:", "- o = order[nm]", "- while ptr[nm] < len(o) and taken[o[ptr[nm]]]: ptr[nm] += 1", "- if ptr[nm] >= len(o): continue", "- i = o[ptr[nm]]; ptr[nm] += 1; taken[i] = True", "- sel.append(int(pool_ids[i])); tot += ntok[i] + 1; progress = True", "- if not progress: break", "+ k = int(np.argmax(wt * max(tot, 1) - got)) # largest token deficit", "+ nm = names[k]; o = order[nm]", "+ while ptr[nm] < len(o) and taken[o[ptr[nm]]]: ptr[nm] += 1", "+ if ptr[nm] >= len(o):", "+ wt[k] = 0.0", "+ if wt.sum() == 0: break", "+ wt = wt / wt.sum(); continue", "+ i = o[ptr[nm]]; ptr[nm] += 1; taken[i] = True", "+ sel.append(int(pool_ids[i])); tot += ntok[i] + 1; got[k] += ntok[i] + 1", "+ print(\" token shares: \" + \" \".join(f\"{nm}={got[k]/max(tot,1)*100:.0f}%\"", "+ for k, nm in enumerate(names)))", " ", " assert len(sel) == len(set(sel))", " os.makedirs(os.path.dirname(OUT), exist_ok=True)"]}], "userModified": false, "replaceAll": false}#35
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCheck C4 breakdown results
args
{
"command": "for nm in multi_dev wiki web news qa; do printf \"%-10s \" $nm; grep -o \"HELDOUT_PPL [0-9.]*\" /tmp/log_c4_$nm.txt 2>/dev/null || echo running; done; echo \"TIME $(cat .timer/remaining_secs)\"",
"description": "Check C4 breakdown results"
}Bash result
multi_dev running
wiki running
web running
news running
qa running
TIME 4509
[stdout]
multi_dev running
wiki running
web running
news running
qa running
TIME 4509
[metadata] {"noOutputExpected": false}#36
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashWait and check C4 breakdown
args
{
"command": "sleep 100; for nm in multi_dev wiki web news qa; do printf \"%-10s \" $nm; grep -o \"HELDOUT_PPL [0-9.]*\" /tmp/log_c4_$nm.txt 2>/dev/null || echo running; done",
"description": "Wait and check C4 breakdown"
}Bash result
multi_dev running
wiki running
web running
news running
qa running
[stdout]
multi_dev running
wiki running
web running
news running
qa running
[metadata] {"noOutputExpected": false}#37
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashWait for C4 breakdown results
args
{
"command": "sleep 90; for nm in multi_dev wiki web news qa; do printf \"%-10s \" $nm; grep -o \"HELDOUT_PPL [0-9.]*\" /tmp/log_c4_$nm.txt 2>/dev/null || echo running; done; echo \"TIME $(cat .timer/remaining_secs)\"",
"description": "Wait for C4 breakdown results"
}Bash result
multi_dev running
wiki running
web running
news running
qa running
TIME 4308
[stdout]
multi_dev running
wiki running
web running
news running
qa running
TIME 4308
[metadata] {"noOutputExpected": false}#38
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCheck GPU utilization and run progress
args
{
"command": "nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv; ps aux | grep -c \"[t]rain_nano\"; sleep 60; for nm in multi_dev wiki web news qa; do printf \"%-10s \" $nm; grep -o \"HELDOUT_PPL [0-9.]*\" /tmp/log_c4_$nm.txt 2>/dev/null || echo running; done",
"description": "Check GPU utilization and run progress"
}Bash result
utilization.gpu [%], memory.used [MiB]
100 %, 47672 MiB
8
multi_dev HELDOUT_PPL 350.801
wiki HELDOUT_PPL 764.967
web HELDOUT_PPL 279.591
news HELDOUT_PPL 371.665
qa HELDOUT_PPL 190.247
[stdout]
utilization.gpu [%], memory.used [MiB]
100 %, 47672 MiB
8
multi_dev HELDOUT_PPL 350.801
wiki HELDOUT_PPL 764.967
web HELDOUT_PPL 279.591
news HELDOUT_PPL 371.665
qa HELDOUT_PPL 190.247
[metadata] {"noOutputExpected": false}#39
narration
C4 = 350.8 overall. The breakdown is decisive: wiki 765, news 372, web 280, qa 190. The encyclopedic register dominates the loss. Let me check whether the pool contains any WikiText-style formatting at all.
toolBashCheck pool for WikiText-style spaced punctuation
args
{
"command": "cd /workspace; echo \"docs containing ' @-@ ':\"; grep -c ' @-@ ' data/pool.jsonl || true; echo \"docs containing ' @.@ ':\"; grep -c ' @.@ ' data/pool.jsonl || true\npython - <<'EOF'\nimport numpy as np\n# spaced-punctuation rate per doc, from cached tokens: how often ' ,' / ' .' style tokens appear\nfrom transformers import AutoTokenizer\ntok=AutoTokenizer.from_pretrained('gpt2')\n# GPT-2 ids for \" ,\" \" .\" \" )\" \" ;\" etc (space-prefixed punctuation = WikiText signature)\ncands=[' ,',' .',' ;',' :',' )',' \"',\" '\"]\nids=[tok(c,add_special_tokens=False)['input_ids'] for c in cands]\nsp=[i[0] for i,c in zip(ids,cands) if len(i)==1]\nprint('space-punct ids',sp,[tok.decode([i]) for i in sp])\nflat=np.load('cache/pool_tokens.npy'); off=np.load('cache/pool_offsets.npy')\nmask=np.zeros(50257,bool); mask[sp]=True\nv=mask[flat].view(np.uint8)\ncs=np.concatenate(([0],np.cumsum(v,dtype=np.int64)))\nn=np.maximum(off[1:]-off[:-1],1)\nr=(cs[off[1:]]-cs[off[:-1]])/n\nprint('pool spaced-punct rate: median %.5f p99 %.5f max %.5f'%(np.median(r),np.quantile(r,.99),r.max()))\nprint('docs with rate>0.02:',(r>0.02).sum(),' >0.04:',(r>0.04).sum())\n# same for the wiki target block (pre-denormalisation)\nd=np.load('data/multi_dev.npy'); eos=np.where(d==50256)[0]\nsegs=[];prev=0\nfor e in eos:\n if e>prev: segs.append(d[prev:e])\n prev=e+1\nL=np.array([len(s) for s in segs]);c=np.cumsum(L)\nw=np.concatenate(segs[:int(np.searchsorted(c,250_000))])\nprint('WIKI TARGET spaced-punct rate: %.5f'%(mask[w].mean()))\nEOF",
"description": "Check pool for WikiText-style spaced punctuation"
}Bash result
docs containing ' @-@ ':
0
docs containing ' @.@ ':
0
space-punct ids [837, 764, 2162, 1058, 1267, 366, 705] [' ,', ' .', ' ;', ' :', ' )', ' "', " '"]
pool spaced-punct rate: median 0.00000 p99 0.02326 max 0.30567
docs with rate>0.02: 2450 >0.04: 532
WIKI TARGET spaced-punct rate: 0.10652
[stdout]
docs containing ' @-@ ':
0
docs containing ' @.@ ':
0
space-punct ids [837, 764, 2162, 1058, 1267, 366, 705] [' ,', ' .', ' ;', ' :', ' )', ' "', " '"]
pool spaced-punct rate: median 0.00000 p99 0.02326 max 0.30567
docs with rate>0.02: 2450 >0.04: 532
WIKI TARGET spaced-punct rate: 0.10652
[metadata] {"noOutputExpected": false}#40
narration
Zero pool docs have @-@, and the wiki target has a 10.7% rate of space-prefixed punctuation tokens vs pool median 0%. That's a large, partly-learnable surprise on 25% of the eval. Let me look at the pool docs that do have it.
toolBashInspect pool docs with spaced punctuation
args
{
"command": "cd /workspace; python - <<'EOF'\nimport numpy as np, json\nfrom transformers import AutoTokenizer\ntok=AutoTokenizer.from_pretrained('gpt2')\nsp=[837,764,2162,1058,1267,366,705]\nflat=np.load('cache/pool_tokens.npy'); off=np.load('cache/pool_offsets.npy'); ids=np.load('cache/pool_ids.npy')\nmask=np.zeros(50257,bool); mask[sp]=True\nv=mask[flat].view(np.uint8); cs=np.concatenate(([0],np.cumsum(v,dtype=np.int64)))\nntok=off[1:]-off[:-1]; n=np.maximum(ntok,1)\nr=(cs[off[1:]]-cs[off[:-1]])/n\nprint('aggregate pool rate of spaced-punct: %.5f'%(v.mean()))\ncand=np.flatnonzero((r>0.03)&(ntok>=128))\nprint('docs r>0.03 & len>=128:',len(cand),' total tokens %.2fM'%(ntok[cand].sum()/1e6))\ntop=cand[np.argsort(-r[cand])]\nwant=set(int(ids[i]) for i in top[:6])\nseen={}\nfor line in open('data/pool.jsonl'):\n o=json.loads(line)\n if o['id'] in want: seen[o['id']]=o['text']\n if len(seen)==len(want): break\nfor i in top[:6]:\n k=int(ids[i]); print('='*20,'id',k,'rate %.3f'%r[i],'len',ntok[i]); print(seen[k][:260].replace('\\n',' | '))\nEOF",
"description": "Inspect pool docs with spaced punctuation"
}Bash result
aggregate pool rate of spaced-punct: 0.00241
docs r>0.03 & len>=128: 854 total tokens 2.17M
==================== id 178386 rate 0.306 len 2398
c) 2013 turbonuke<|endoftext|>Keranjang Belanja - MWN | English | English | English | Login | Daftar | Lihat Keranjang Belanja | Toggle navigation | Client Area | Store | Browse All | ----- | Shared Hosting Linux (cPanel/WHM) | WOLFPRESS (WordPress Hosting) | Shared Hosting Linux (Plesk)
==================== id 164681 rate 0.300 len 1692
with JavaScript enabled<|endoftext|>Shopping Cart - supportHQ.net | SupportHQ - web hosting | Home | Features | Plans | Shared Hosting Plans | Lifetime Hosting Plan | FAQ | About | 100% Wind Powered | Contact | Clients | Shopping Cart Please login or register | Home | Announcements | Know
==================== id 136429 rate 0.287 len 3163
.<|endoftext|>WHMCS-bridge – LokuraNetworks | DOMINIOS | Hosting | Cloud Hosting SSD | Cloud VPS SSD | Cloud Dedicado SSD | CloudFlare | Datacenter | SERVICIOS | INTERNET | IPTV | Streaming | TELEFONÍA | Blog | Contacto | Choose language | العربية | Azerbaijani | Català | 中文 | Hrvatski | Čeština | Dansk
==================== id 159085 rate 0.287 len 3163
LokuraNetworks | DOMINIOS | Hosting | Cloud Hosting SSD | Cloud VPS SSD | Cloud Dedicado SSD | CloudFlare | Datacenter | SERVICIOS | INTERNET | IPTV | Streaming | TELEFONÍA | Blog | Contacto | Choose language | العربية | Azerbaijani | Català | 中文 | Hrvatski | Čeština | Dansk | Nederlands | English | Estonian
==================== id 105413 rate 0.235 len 153
Saddleback College in Mission Viejo, Calif. . . . Was an all-conference pick a year ago on the JCO level . . . Spent two seasons with the Ganchos . . . Had 68 total tackles and an interception in 2012 . . . Totaled 41 stops in 2011 . . . Played prep football
==================== id 143912 rate 0.192 len 32475
innunhiekka edullisesti zooplussalta | Ostoskorisi | Ostokorisi on tyhjä | Ostoskoriin | Tee lemmikkisi onnelliseksi .... Säästä nyt! | Ostoskoriin | {{/if}} {{#if isCartFull}} | Ostoskorisi | {{#each articles}} | {{this.name}} | {{this.variantName}} | {{this.quantity}} x | {{this.su
[stdout]
aggregate pool rate of spaced-punct: 0.00241
docs r>0.03 & len>=128: 854 total tokens 2.17M
==================== id 178386 rate 0.306 len 2398
c) 2013 turbonuke<|endoftext|>Keranjang Belanja - MWN | English | English | English | Login | Daftar | Lihat Keranjang Belanja | Toggle navigation | Client Area | Store | Browse All | ----- | Shared Hosting Linux (cPanel/WHM) | WOLFPRESS (WordPress Hosting) | Shared Hosting Linux (Plesk)
==================== id 164681 rate 0.300 len 1692
with JavaScript enabled<|endoftext|>Shopping Cart - supportHQ.net | SupportHQ - web hosting | Home | Features | Plans | Shared Hosting Plans | Lifetime Hosting Plan | FAQ | About | 100% Wind Powered | Contact | Clients | Shopping Cart Please login or register | Home | Announcements | Know
==================== id 136429 rate 0.287 len 3163
.<|endoftext|>WHMCS-bridge – LokuraNetworks | DOMINIOS | Hosting | Cloud Hosting SSD | Cloud VPS SSD | Cloud Dedicado SSD | CloudFlare | Datacenter | SERVICIOS | INTERNET | IPTV | Streaming | TELEFONÍA | Blog | Contacto | Choose language | العربية | Azerbaijani | Català | 中文 | Hrvatski | Čeština | Dansk
==================== id 159085 rate 0.287 len 3163
LokuraNetworks | DOMINIOS | Hosting | Cloud Hosting SSD | Cloud VPS SSD | Cloud Dedicado SSD | CloudFlare | Datacenter | SERVICIOS | INTERNET | IPTV | Streaming | TELEFONÍA | Blog | Contacto | Choose language | العربية | Azerbaijani | Català | 中文 | Hrvatski | Čeština | Dansk | Nederlands | English | Estonian
==================== id 105413 rate 0.235 len 153
Saddleback College in Mission Viejo, Calif. . . . Was an all-conference pick a year ago on the JCO level . . . Spent two seasons with the Ganchos . . . Had 68 total tackles and an interception in 2012 . . . Totaled 41 stops in 2011 . . . Played prep football
==================== id 143912 rate 0.192 len 32475
innunhiekka edullisesti zooplussalta | Ostoskorisi | Ostokorisi on tyhjä | Ostoskoriin | Tee lemmikkisi onnelliseksi .... Säästä nyt! | Ostoskoriin | {{/if}} {{#if isCartFull}} | Ostoskorisi | {{#each articles}} | {{this.name}} | {{this.variantName}} | {{this.quantity}} x | {{this.su
[metadata] {"noOutputExpected": false}#41
narration
Those are junk (hosting-nav boilerplate, non-English) — a raw spaced-punctuation filter is a garbage magnet. The principled version: let the bigram classifier learn the format from non-denormalised wiki positives, so it must trade format against content. Let me implement that as a 5th stream and test three variants.
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/curate.py",
"old_string": " names = [\"wiki\", \"web\", \"news\", \"qa\"]\n out = {}\n for k, nm in enumerate(names):\n txt = \"\\n\\n\".join(tok.decode(s.tolist()) for s in segs[cut[k]:cut[k + 1]])\n if nm == \"wiki\": txt = denorm_wikitext(txt)\n ids = tok(txt, add_special_tokens=False)[\"input_ids\"]\n ids = np.asarray(ids, dtype=np.int32)\n n = len(ids) // CHUNK\n out[nm] = ids[:n * CHUNK].reshape(n, CHUNK)\n return names, out",
"new_string": " names = [\"wiki\", \"web\", \"news\", \"qa\"]\n out = {}\n for k, nm in enumerate(names):\n txt = \"\\n\\n\".join(tok.decode(s.tolist()) for s in segs[cut[k]:cut[k + 1]])\n if nm == \"wiki\": txt = denorm_wikitext(txt)\n ids = tok(txt, add_special_tokens=False)[\"input_ids\"]\n ids = np.asarray(ids, dtype=np.int32)\n n = len(ids) // CHUNK\n out[nm] = ids[:n * CHUNK].reshape(n, CHUNK)\n if RAWWIKI:\n # A 5th stream whose positives keep WikiText's pre-tokenised surface form\n # (spaced punctuation). 10.7% of the encyclopedic target's tokens are\n # space-prefixed punctuation versus 0.24% of the pool, and that register carries\n # by far the highest loss. Ranking by raw spaced-punctuation RATE alone selects\n # pure garbage (hosting-nav boilerplate, non-English), so instead we let the\n # bigram classifier decide: it must trade surface form off against content, since\n # the same weight vector also has to reject the boilerplate vocabulary.\n d0 = np.concatenate([np.asarray(s, np.int32) for s in segs[cut[0]:cut[1]]])\n n = len(d0) // CHUNK\n out[\"wikiraw\"] = d0[:n * CHUNK].reshape(n, CHUNK)\n names = names + [\"wikiraw\"]\n return names, out"
}Edit result
The file /workspace/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/curate.py", "oldString": " names = [\"wiki\", \"web\", \"news\", \"qa\"]\n out = {}\n for k, nm in enumerate(names):\n txt = \"\\n\\n\".join(tok.decode(s.tolist()) for s in segs[cut[k]:cut[k + 1]])\n if nm == \"wiki\": txt = denorm_wikitext(txt)\n ids = tok(txt, add_special_tokens=False)[\"input_ids\"]\n ids = np.asarray(ids, dtype=np.int32)\n n = len(ids) // CHUNK\n out[nm] = ids[:n * CHUNK].reshape(n, CHUNK)\n return names, out", "newString": " names = [\"wiki\", \"web\", \"news\", \"qa\"]\n out = {}\n for k, nm in enumerate(names):\n txt = \"\\n\\n\".join(tok.decode(s.tolist()) for s in segs[cut[k]:cut[k + 1]])\n if nm == \"wiki\": txt = denorm_wikitext(txt)\n ids = tok(txt, add_special_tokens=False)[\"input_ids\"]\n ids = np.asarray(ids, dtype=np.int32)\n n = len(ids) // CHUNK\n out[nm] = ids[:n * CHUNK].reshape(n, CHUNK)\n if RAWWIKI:\n # A 5th stream whose positives keep WikiText's pre-tokenised surface form\n # (spaced punctuation). 10.7% of the encyclopedic target's tokens are\n # space-prefixed punctuation versus 0.24% of the pool, and that register carries\n # by far the highest loss. Ranking by raw spaced-punctuation RATE alone selects\n # pure garbage (hosting-nav boilerplate, non-English), so instead we let the\n # bigram classifier decide: it must trade surface form off against content, since\n # the same weight vector also has to reject the boilerplate vocabulary.\n d0 = np.concatenate([np.asarray(s, np.int32) for s in segs[cut[0]:cut[1]]])\n n = len(d0) // CHUNK\n out[\"wikiraw\"] = d0[:n * CHUNK].reshape(n, CHUNK)\n names = names + [\"wikiraw\"]\n return names, out", "originalFile": null, "structuredPatch": [{"oldStart": 182, "oldLines": 6, "newStart": 182, "newLines": 18, "lines": [" ids = np.asarray(ids, dtype=np.int32)", " n = len(ids) // CHUNK", " out[nm] = ids[:n * CHUNK].reshape(n, CHUNK)", "+ if RAWWIKI:", "+ # A 5th stream whose positives keep WikiText's pre-tokenised surface form", "+ # (spaced punctuation). 10.7% of the encyclopedic target's tokens are", "+ # space-prefixed punctuation versus 0.24% of the pool, and that register carries", "+ # by far the highest loss. Ranking by raw spaced-punctuation RATE alone selects", "+ # pure garbage (hosting-nav boilerplate, non-English), so instead we let the", "+ # bigram classifier decide: it must trade surface form off against content, since", "+ # the same weight vector also has to reject the boilerplate vocabulary.", "+ d0 = np.concatenate([np.asarray(s, np.int32) for s in segs[cut[0]:cut[1]]])", "+ n = len(d0) // CHUNK", "+ out[\"wikiraw\"] = d0[:n * CHUNK].reshape(n, CHUNK)", "+ names = names + [\"wikiraw\"]", " return names, out", " ", " "]}], "userModified": false, "replaceAll": false}#42
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashRun three curate variants D1-D3
args
{
"command": "python - <<'EOF'\nimport re\np='curate.py'; s=open(p).read()\ns=s.replace('BALANCE = os.environ.get(\"BALANCE\", \"rr\") # rr | pooled',\n 'BALANCE = os.environ.get(\"BALANCE\", \"rr\") # rr | pooled\\nRAWWIKI = int(os.environ.get(\"RAWWIKI\", 0))')\nopen(p,'w').write(s)\nEOF\npkill -f train_nano; sleep 1\nfor v in \"D1:RAWWIKI=0\" \"D2:RAWWIKI=0 W_WIKI=2.0\" \"D3:RAWWIKI=1 W_WIKIRAW=0.5\"; do\n nm=${v%%:*}; envs=${v#*:}\n echo \"=== $nm ($envs)\"\n env $envs NEG_N=25000 OUT=/workspace/cache/sel_$nm.json python curate.py 2>&1 | grep -v \"Token indices\" | grep -E \"token shares|wrote|AUC\" \ndone; echo \"TIME $(cat .timer/remaining_secs)\"",
"description": "Run three curate variants D1-D3"
}Bash result
pkill: refusing to run — this pattern matches the Claude CLI process (PID 157). Narrow the pattern, or target your own children with `pkill -P $$ ...`.
=== D1 (RAWWIKI=0)
wiki : held-out AUC vs pool = 0.9867 selected-score p99=+0.3225 median=-0.3375 (26s)
web : held-out AUC vs pool = 0.8599 selected-score p99=+0.2113 median=-0.0927 (27s)
news : held-out AUC vs pool = 0.9450 selected-score p99=+0.3206 median=-0.1678 (28s)
qa : held-out AUC vs pool = 0.9995 selected-score p99=-0.7055 median=-1.0320 (29s)
token shares: wiki=25% web=25% news=25% qa=25%
wrote 44914 ids ~30.0M tokens (budget 12M) (29s)
=== D2 (RAWWIKI=0 W_WIKI=2.0)
wiki : held-out AUC vs pool = 0.9867 selected-score p99=+0.3225 median=-0.3375 (26s)
web : held-out AUC vs pool = 0.8599 selected-score p99=+0.2113 median=-0.0927 (27s)
news : held-out AUC vs pool = 0.9450 selected-score p99=+0.3206 median=-0.1678 (28s)
qa : held-out AUC vs pool = 0.9995 selected-score p99=-0.7055 median=-1.0320 (29s)
token shares: wiki=40% web=20% news=20% qa=20%
wrote 46218 ids ~30.0M tokens (budget 12M) (30s)
=== D3 (RAWWIKI=1 W_WIKIRAW=0.5)
wiki : held-out AUC vs pool = 0.9867 selected-score p99=+0.3225 median=-0.3375 (26s)
web : held-out AUC vs pool = 0.8599 selected-score p99=+0.2113 median=-0.0927 (27s)
news : held-out AUC vs pool = 0.9450 selected-score p99=+0.3206 median=-0.1678 (28s)
qa : held-out AUC vs pool = 0.9995 selected-score p99=-0.7055 median=-1.0320 (29s)
wikiraw: held-out AUC vs pool = 0.9999 selected-score p99=-0.2999 median=-0.8993 (30s)
token shares: wiki=22% web=22% news=22% qa=22% wikiraw=11%
wrote 46104 ids ~30.0M tokens (budget 12M) (30s)
TIME 4038
[stdout]
pkill: refusing to run — this pattern matches the Claude CLI process (PID 157). Narrow the pattern, or target your own children with `pkill -P $$ ...`.
=== D1 (RAWWIKI=0)
wiki : held-out AUC vs pool = 0.9867 selected-score p99=+0.3225 median=-0.3375 (26s)
web : held-out AUC vs pool = 0.8599 selected-score p99=+0.2113 median=-0.0927 (27s)
news : held-out AUC vs pool = 0.9450 selected-score p99=+0.3206 median=-0.1678 (28s)
qa : held-out AUC vs pool = 0.9995 selected-score p99=-0.7055 median=-1.0320 (29s)
token shares: wiki=25% web=25% news=25% qa=25%
wrote 44914 ids ~30.0M tokens (budget 12M) (29s)
=== D2 (RAWWIKI=0 W_WIKI=2.0)
wiki : held-out AUC vs pool = 0.9867 selected-score p99=+0.3225 median=-0.3375 (26s)
web : held-out AUC vs pool = 0.8599 selected-score p99=+0.2113 median=-0.0927 (27s)
news : held-out AUC vs pool = 0.9450 selected-score p99=+0.3206 median=-0.1678 (28s)
qa : held-out AUC vs pool = 0.9995 selected-score p99=-0.7055 median=-1.0320 (29s)
token shares: wiki=40% web=20% news=20% qa=20%
wrote 46218 ids ~30.0M tokens (budget 12M) (30s)
=== D3 (RAWWIKI=1 W_WIKIRAW=0.5)
wiki : held-out AUC vs pool = 0.9867 selected-score p99=+0.3225 median=-0.3375 (26s)
web : held-out AUC vs pool = 0.8599 selected-score p99=+0.2113 median=-0.0927 (27s)
news : held-out AUC vs pool = 0.9450 selected-score p99=+0.3206 median=-0.1678 (28s)
qa : held-out AUC vs pool = 0.9995 selected-score p99=-0.7055 median=-1.0320 (29s)
wikiraw: held-out AUC vs pool = 0.9999 selected-score p99=-0.2999 median=-0.8993 (30s)
token shares: wiki=22% web=22% news=22% qa=22% wikiraw=11%
wrote 46104 ids ~30.0M tokens (budget 12M) (30s)
TIME 4038
[metadata] {"noOutputExpected": false}