claude-opus-4-8

5400
---
total 14
drwxr-xr-x 1 root root  100 Aug  4 04:01 .
drwxr-xr-x 1 root root  160 Aug  4 04:01 ..
drwxr-xr-x 1 root root   60 Aug  4 04:01 .live
drwxr-xr-x 1 root root  100 Aug  4 04:01 .metrics
drwxr-xr-x 1 root root  140 Aug  4 04:01 .timer
drwxr-xr-x 1 root root   57 Aug  3 22:37 data
-rwxrwxr-x 1 root root  714 Aug  3 22:34 dev_eval.sh
-rw-rw-r-- 1 root root 5706 Aug  3 22:34 model.py
-rw-rw-r-- 1 root root  869 Aug  3 22:34 pack_selection.py
drwxr-xr-x 1 root root   10 Aug  3 22:37 submission
-rwxrwxr-x 1 root root 1509 Aug  3 22:34 timer.sh
-rw-rw-r-- 1 root root 2962 Aug  3 22:34 train_nano.py
---
total 792657
drwxr-xr-x 1 root root        57 Aug  3 22:37 .
drwxr-xr-x 1 root root       100 Aug  4 04:01 ..
-r--r--r-- 1 root root   2000128 Aug  3 22:34 multi_dev.npy
-r--r--r-- 1 root root 809679384 Aug  3 22:35 pool.jsonl
---
total 1
drwxr-xr-x 1 root root  10 Aug  3 22:37 .
drwxr-xr-x 1 root root 100 Aug  4 04:01 ..
---nvidia---
name, memory.total [MiB], memory.used [MiB]
NVIDIA H100 80GB HBM3, 81559 MiB, 4 MiB

[stdout]
5400
---
total 14
drwxr-xr-x 1 root root  100 Aug  4 04:01 .
drwxr-xr-x 1 root root  160 Aug  4 04:01 ..
drwxr-xr-x 1 root root   60 Aug  4 04:01 .live
drwxr-xr-x 1 root root  100 Aug  4 04:01 .metrics
drwxr-xr-x 1 root root  140 Aug  4 04:01 .timer
drwxr-xr-x 1 root root   57 Aug  3 22:37 data
-rwxrwxr-x 1 root root  714 Aug  3 22:34 dev_eval.sh
-rw-rw-r-- 1 root root 5706 Aug  3 22:34 model.py
-rw-rw-r-- 1 root root  869 Aug  3 22:34 pack_selection.py
drwxr-xr-x 1 root root   10 Aug  3 22:37 submission
-rwxrwxr-x 1 root root 1509 Aug  3 22:34 timer.sh
-rw-rw-r-- 1 root root 2962 Aug  3 22:34 train_nano.py
---
total 792657
drwxr-xr-x 1 root root        57 Aug  3 22:37 .
drwxr-xr-x 1 root root       100 Aug  4 04:01 ..
-r--r--r-- 1 root root   2000128 Aug  3 22:34 multi_dev.npy
-r--r--r-- 1 root root 809679384 Aug  3 22:35 pool.jsonl
---
total 1
drwxr-xr-x 1 root root  10 Aug  3 22:37 .
drwxr-xr-x 1 root root 100 Aug  4 04:01 ..
---nvidia---
name, memory.total [MiB], memory.used [MiB]
NVIDIA H100 80GB HBM3, 81559 MiB, 4 MiB
[metadata] {"noOutputExpected": false}

=== train_nano.py ===
"""From-scratch nano-GPT (30M) LM training on a 1D token .npy; report held-out perplexity.
Frozen recipe — the ONLY thing that varies between runs is the training token set."""
import argparse, math, time, numpy as np, torch
from model import GPT, GPTConfig

ap = argparse.ArgumentParser()
ap.add_argument("--train_npy", required=True)
ap.add_argument("--heldout_npy", required=True)
ap.add_argument("--out_json", required=True)
ap.add_argument("--max_iters", type=int, default=3000)
ap.add_argument("--warmup", type=int, default=150)
ap.add_argument("--batch", type=int, default=32)
ap.add_argument("--block", type=int, default=256)
ap.add_argument("--lr", type=float, default=6e-4)
ap.add_argument("--seed", type=int, default=1337)
a = ap.parse_args()

torch.manual_seed(a.seed); np.random.seed(a.seed)
dev = "cuda"
tr = np.load(a.train_npy); ho = np.load(a.heldout_npy)
tr = torch.from_numpy(tr.astype(np.int64)); ho = torch.from_numpy(ho.astype(np.int64))
rng = np.random.default_rng(a.seed)

model = GPT(GPTConfig(block_size=a.block, vocab_size=50257, n_layer=6, n_head=6,
                      n_embd=384, dropout=0.0, bias=False)).to(dev)
opt = model.configure_optimizers(0.1, a.lr, (0.9, 0.95), "cuda")

def lr_at(it):
    if it < a.warmup: return a.lr * (it + 1) / (a.warmup + 1)
    r = (it - a.warmup) / max(1, a.max_iters - a.warmup)
    return 0.1 * a.lr + 0.5 * (1 + math.cos(math.pi * r)) * (a.lr - 0.1 * a.lr)

def get_batch(src):
    ix = rng.integers(0, len(src) - a.block - 1, size=a.batch)
    x = torch.stack([src[i:i+a.block] for i in ix]).to(dev)
    y = torch.stack([src[i+1:i+1+a.block] for i in ix]).to(dev)
    return x, y

t0 = time.time()
model.train()
for it in range(a.max_iters):
    for g in opt.param_groups: g["lr"] = lr_at(it)
    x, y = get_batch(tr)
    with torch.autocast("cuda", dtype=torch.bfloat16):
        _, loss = model(x, y)
    opt.zero_grad(set_to_none=True); loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step()
train_s = time.time() - t0

# held-out perplexity: mean next-token CE over non-overlapping windows (window-weighted)
import json
model.eval()
nwin = (len(ho) - 1) // a.block
starts = [j * a.block for j in range(nwin)]
sum_loss, nseen = 0.0, 0
with torch.no_grad():
    for i in range(0, nwin, a.batch):
        bs = starts[i:i+a.batch]
        x = torch.stack([ho[s:s+a.block] for s in bs]).to(dev)
        y = torch.stack([ho[s+1:s+1+a.block] for s in bs]).to(dev)
        with torch.autocast("cuda", dtype=torch.bfloat16):
            _, l = model(x, y)
        sum_loss += l.item() * len(bs); nseen += len(bs)
mean_loss = sum_loss / max(1, nseen)
ppl = math.exp(mean_loss)
out = {"train_npy": a.train_npy, "heldout_ppl": ppl, "mean_loss": mean_loss,
       "max_iters": a.max_iters, "seed": a.seed, "train_s": round(train_s, 1)}
json.dump(out, open(a.out_json, "w"))
print(f"HELDOUT_PPL {ppl:.3f}  loss {mean_loss:.4f}  train_s {train_s:.0f}  {a.train_npy}")
=== pack_selection.py ===
"""Solver-side: pack a selection.json (ordered ids) into a 12M-token train array from pool.jsonl."""
import json, sys, numpy as np
from transformers import AutoTokenizer

POOL = "/workspace/data/pool.jsonl"
SEL = sys.argv[1] if len(sys.argv) > 1 else "/workspace/submission/selection.json"
OUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/dev_train.npy"
BUDGET = 12_000_000

tok = AutoTokenizer.from_pretrained("gpt2"); EOS = tok.eos_token_id
text = {}
for line in open(POOL):
    r = json.loads(line); text[r["id"]] = r["text"]
sel = json.load(open(SEL))
parts, tot = [], 0
for i in sel:
    ids = tok(text[i], add_special_tokens=False).input_ids
    parts.extend(ids); parts.append(EOS); tot += len(ids) + 1
    if tot >= BUDGET: break
arr = np.array(parts[:BUDGET], dtype=np.uint16)
np.save(OUT, arr)
print(f"packed {len(arr)} tokens from {len(sel)} ids -> {OUT}")
=== dev_eval.sh ===
#!/bin/bash
# Diagnostic: train the frozen 30M GPT on your selection and report DEV perplexity.
# Usage: bash dev_eval.sh [selection.json]   (default /workspace/submission/selection.json)
# NOTE: the official target is a DIFFERENT (hidden) sample from the same HQ domain.
set -euo pipefail
export TOKENIZERS_PARALLELISM=false
SEL="${1:-/workspace/submission/selection.json}"
cd /workspace
python3 pack_selection.py "$SEL" /tmp/dev_train.npy
python3 train_nano.py --train_npy /tmp/dev_train.npy --heldout_npy /workspace/data/multi_dev.npy \
    --out_json /tmp/dev.json --max_iters 3000 --seed 1337 2>&1 | grep HELDOUT_PPL
echo "(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)"
=== timer.sh ===
#!/usr/bin/env bash
# Budget timer daemon — writes /workspace/.timer/remaining_secs every 10s so the
# solver can poll time left instead of doing wall-clock arithmetic.
#
# Budget (seconds) comes from $TASK_BUDGET_SECS (set by the runner to the agent
# timeout). Idempotent via a PID file. On ScaleTrain the trial runner starts its
# own timer; this file is the fallback for the Modal/local harbor backends, which
# bootstrap /workspace/timer.sh on environment start. The fallback default below
# (7200 = 2 h) matches this task's [agent] timeout_sec and is only used if
# $TASK_BUDGET_SECS is unset.
set -u
TIMER_DIR="/workspace/.timer"
PID_FILE="$TIMER_DIR/timer.pid"
mkdir -p "$TIMER_DIR"

if [ -s "$PID_FILE" ] && kill -0 "$(cat "$PID_FILE" 2>/dev/null)" 2>/dev/null; then
    exit 0   # already running
fi
echo $$ > "$PID_FILE"
trap 'rm -f "$PID_FILE"' EXIT INT TERM

START=$(date +%s)
BUDGET="${TASK_BUDGET_SECS:-7200}"
echo "$START" > "$TIMER_DIR/start_epoch"
echo "$BUDGET" > "$TIMER_DIR/budget_secs"

while true; do
    NOW=$(date +%s)
    ELAPSED=$((NOW - START))
    REMAINING=$((BUDGET - ELAPSED))
    [ "$REMAINING" -lt 0 ] && REMAINING=0
    echo "$REMAINING" > "$TIMER_DIR/remaining_secs"
    echo "$ELAPSED" > "$TIMER_DIR/elapsed_secs"
    [ "$REMAINING" -le 1800 ] && [ ! -f "$TIMER_DIR/alert_30min" ] && touch "$TIMER_DIR/alert_30min"
    [ "$REMAINING" -le 600 ]  && [ ! -f "$TIMER_DIR/alert_10min" ] && touch "$TIMER_DIR/alert_10min"
    [ "$REMAINING" -le 0 ] && break
    sleep 10
done

[stdout]
=== train_nano.py ===
"""From-scratch nano-GPT (30M) LM training on a 1D token .npy; report held-out perplexity.
Frozen recipe — the ONLY thing that varies between runs is the training token set."""
import argparse, math, time, numpy as np, torch
from model import GPT, GPTConfig

ap = argparse.ArgumentParser()
ap.add_argument("--train_npy", required=True)
ap.add_argument("--heldout_npy", required=True)
ap.add_argument("--out_json", required=True)
ap.add_argument("--max_iters", type=int, default=3000)
ap.add_argument("--warmup", type=int, default=150)
ap.add_argument("--batch", type=int, default=32)
ap.add_argument("--block", type=int, default=256)
ap.add_argument("--lr", type=float, default=6e-4)
ap.add_argument("--seed", type=int, default=1337)
a = ap.parse_args()

torch.manual_seed(a.seed); np.random.seed(a.seed)
dev = "cuda"
tr = np.load(a.train_npy); ho = np.load(a.heldout_npy)
tr = torch.from_numpy(tr.astype(np.int64)); ho = torch.from_numpy(ho.astype(np.int64))
rng = np.random.default_rng(a.seed)

model = GPT(GPTConfig(block_size=a.block, vocab_size=50257, n_layer=6, n_head=6,
                      n_embd=384, dropout=0.0, bias=False)).to(dev)
opt = model.configure_optimizers(0.1, a.lr, (0.9, 0.95), "cuda")

def lr_at(it):
    if it < a.warmup: return a.lr * (it + 1) / (a.warmup + 1)
    r = (it - a.warmup) / max(1, a.max_iters - a.warmup)
    return 0.1 * a.lr + 0.5 * (1 + math.cos(math.pi * r)) * (a.lr - 0.1 * a.lr)

def get_batch(src):
    ix = rng.integers(0, len(src) - a.block - 1, size=a.batch)
    x = torch.stack([src[i:i+a.block] for i in ix]).to(dev)
    y = torch.stack([src[i+1:i+1+a.block] for i in ix]).to(dev)
    return x, y

t0 = time.time()
model.train()
for it in range(a.max_iters):
    for g in opt.param_groups: g["lr"] = lr_at(it)
    x, y = get_batch(tr)
    with torch.autocast("cuda", dtype=torch.bfloat16):
        _, loss = model(x, y)
    opt.zero_grad(set_to_none=True); loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step()
train_s = time.time() - t0

# held-out perplexity: mean next-token CE over non-overlapping windows (window-weighted)
import json
model.eval()
nwin = (len(ho) - 1) // a.block
starts = [j * a.block for j in range(nwin)]
sum_loss, nseen = 0.0, 0
with torch.no_grad():
    for i in range(0, nwin, a.batch):
        bs = starts[i:i+a.batch]
        x = torch.stack([ho[s:s+a.block] for s in bs]).to(dev)
        y = torch.stack([ho[s+1:s+1+a.block] for s in bs]).to(dev)
        with torch.autocast("cuda", dtype=torch.bfloat16):
            _, l = model(x, y)
        sum_loss += l.item() * len(bs); nseen += len(bs)
mean_loss = sum_loss / max(1, nseen)
ppl = math.exp(mean_loss)
out = {"train_npy": a.train_npy, "heldout_ppl": ppl, "mean_loss": mean_loss,
       "max_iters": a.max_iters, "seed": a.seed, "train_s": round(train_s, 1)}
json.dump(out, open(a.out_json, "w"))
print(f"HELDOUT_PPL {ppl:.3f}  loss {mean_loss:.4f}  train_s {train_s:.0f}  {a.train_npy}")
=== pack_selection.py ===
"""Solver-side: pack a selection.json (ordered ids) into a 12M-token train array from pool.jsonl."""
import json, sys, numpy as np
from transformers import AutoTokenizer

POOL = "/workspace/data/pool.jsonl"
SEL = sys.argv[1] if len(sys.argv) > 1 else "/workspace/submission/selection.json"
OUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/dev_train.npy"
BUDGET = 12_000_000

tok = AutoTokenizer.from_pretrained("gpt2"); EOS = tok.eos_token_id
text = {}
for line in open(POOL):
    r = json.loads(line); text[r["id"]] = r["text"]
sel = json.load(open(SEL))
parts, tot = [], 0
for i in sel:
    ids = tok(text[i], add_special_tokens=False).input_ids
    parts.extend(ids); parts.append(EOS); tot += len(ids) + 1
    if tot >= BUDGET: break
arr = np.array(parts[:BUDGET], dtype=np.uint16)
np.save(OUT, arr)
print(f"packed {len(arr)} tokens from {len(sel)} ids -> {OUT}")
=== dev_eval.sh ===
#!/bin/bash
# Diagnostic: train the frozen 30M GPT on your selection and report DEV perplexity.
# Usage: bash dev_eval.sh [selection.json]   (default /workspace/submission/selection.json)
# NOTE: the official target is a DIFFERENT (hidden) sample from the same HQ domain.
set -euo pipefail
export TOKENIZERS_PARALLELISM=false
SEL="${1:-/workspace/submission/selection.json}"
cd /workspace
python3 pack_selection.py "$SEL" /tmp/dev_train.npy
python3 train_nano.py --train_npy /tmp/dev_train.npy --heldout_npy /workspace/data/multi_dev.npy \
    --out_json /tmp/dev.json --max_iters 3000 --seed 1337 2>&1 | grep HELDOUT_PPL
echo "(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)"
=== timer.sh ===
#!/usr/bin/env bash
# Budget timer daemon — writes /workspace/.timer/remaining_secs every 10s so the
# solver can poll time left instead of doing wall-clock arithmetic.
#
# Budget (seconds) comes from $TASK_BUDGET_SECS (set by the runner to the agent
# timeout). Idempotent via a PID file. On ScaleTrain the trial runner starts its
# own timer; this file is the fallback for the Modal/local harbor backends, which
# bootstrap /workspace/timer.sh on environment start. The fallback default below
# (7200 = 2 h) matches this task's [agent] timeout_sec and is only used if
# $TASK_BUDGET_SECS is unset.
set -u
TIMER_DIR="/workspace/.timer"
PID_FILE="$TIMER_DIR/timer.pid"
mkdir -p "$TIMER_DIR"

if [ -s "$PID_FILE" ] && kill -0 "$(cat "$PID_FILE" 2>/dev/null)" 2>/dev/null; then
    exit 0   # already running
fi
echo $$ > "$PID_FILE"
trap 'rm -f "$PID_FILE"' EXIT INT TERM

START=$(date +%s)
BUDGET="${TASK_BUDGET_SECS:-7200}"
echo "$START" > "$TIMER_DIR/start_epoch"
echo "$BUDGET" > "$TIMER_DIR/budget_secs"

while true; do
    NOW=$(date +%s)
    ELAPSED=$((NOW - START))
    REMAINING=$((BUDGET - ELAPSED))
    [ "$REMAINING" -lt 0 ] && REMAINING=0
    echo "$REMAINING" > "$TIMER_DIR/remaining_secs"
    echo "$ELAPSED" > "$TIMER_DIR/elapsed_secs"
    [ "$REMAINING" -le 1800 ] && [ ! -f "$TIMER_DIR/alert_30min" ] && touch "$TIMER_DIR/alert_30min"
    [ "$REMAINING" -le 600 ]  && [ ! -f "$TIMER_DIR/alert_10min" ] && touch "$TIMER_DIR/alert_10min"
    [ "$REMAINING" -le 0 ] && break
    sleep 10
done
[metadata] {"noOutputExpected": false}

"""A small GPT (decoder-only Transformer) used as the distillation *student*.

Self-contained re-implementation in this repo's own idiom (no external GPT code).
GPT-2 byte-level BPE vocabulary (50257) so the student shares the teacher's
tokenizer. Standard pre-norm Transformer with weight-tied embeddings.
"""
from dataclasses import dataclass
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


@dataclass
class GPTConfig:
    block_size: int = 256
    vocab_size: int = 50257
    n_layer: int = 6
    n_head: int = 6
    n_embd: int = 384
    dropout: float = 0.0
    bias: bool = False


class LayerNorm(nn.Module):
    def __init__(self, ndim, bias):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(ndim))
        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None

=== pool sample ===
{"id": 0, "text": "|Viewing Single Post From: Spoilers for the Week of February 11th|\n|Lil||Feb 1 2013, 09:58 AM|\nDon't care about Chloe/Taniel/Jen-Jen. Don't care about Sami, really, but hoping that we get some good \"SAMANTHA GENE!!\" Marlena Death-Stares out of it. And \"newfound\" feelings. Please. If only.\nSTEFANO!! STEFANO, STEFANO, STEFANO!!!! :cheer:\n|Spoilers for the Week of February 11th \u00b7 DAYS: News, Spoilers & Discussion|"}
{"id": 1, "text": "*sigh* Fundamentalist community, let me pass on some advice to you I learned from the atheistic community:\nIf you have set yourself on fire, do not run.\nOkay? Okay?? Please?\nLook, D, you had two months to say to Harvard in private emails, \"Im sorry, I shouldnt have been using that animation in my paid presentations. I wont use it again. I really do like 'Inner Life', though, and would love to use it in classroom presentations, from the BioVisions site, if that is acceptable.\"\nI sat here, for two months, waiting for that to happen, anything to happen, and it didnt. Two months, on your own terms, you could have written a similar post to yesterdays. I would have given you the benefit of the doubt-- maybe you didnt know the credits werent visible to the audience, and I wouldnt have said a word beyond this, as its Harvards problem, not mine. This would have been a funny joke to those of us involved in dealing with you people, but it would have been a PR non-issue for you.\nBut after you set yourself on fire, you didnt douse it out with a bucket of ice cold reality and accountability. You ran. And youre still running.\nWhy not just state \"I screwed up. Sorry everyone.\" and move on? Why the excuses? Why the denial? Why the passive language? Why the vague words and cryptic capitalizations? Why the writes and rewrites of your 'press release'? We know it wasnt written of your own volition, or it would have been done *before* Harvard had to take action. And, your behavior before this, regarding this issue, is not indicative of someone who made an innocent mistake. Its weird.\nSo what with this frantic running? Is the inability to say \"I was wrong\" a pathological feature of Creationists? Or are you hiding something? Or is it both? Or is it more?\nAnd now we get Casey weighing in on the issue, according to cre8id at AboveTopSecret.com-- PBS/NOVA online - Intelligent Design on trial:\n...to my knowledge, Discovery Institute has neither authorized nor received nor is making use of any presentation that used that animation. We have had nothing to do with creating or selling a DVD of that animation, nor do we have anything to do with placing that presentation on Google Video.I dont know what he is talking about with that last part, but the first part sounds similar to DIs claims post-Dover (\"WE HAD NOTHING TO DO WITH DOVER!\"). Maybe Luskin is telling the truth. Maybe this was a magic non-science Creation-friendly narration with convenient edits that AiG or ICR would have killed for... but onl
=== line count ===
182016 data/pool.jsonl

[stdout]
"""A small GPT (decoder-only Transformer) used as the distillation *student*.

Self-contained re-implementation in this repo's own idiom (no external GPT code).
GPT-2 byte-level BPE vocabulary (50257) so the student shares the teacher's
tokenizer. Standard pre-norm Transformer with weight-tied embeddings.
"""
from dataclasses import dataclass
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


@dataclass
class GPTConfig:
    block_size: int = 256
    vocab_size: int = 50257
    n_layer: int = 6
    n_head: int = 6
    n_embd: int = 384
    dropout: float = 0.0
    bias: bool = False


class LayerNorm(nn.Module):
    def __init__(self, ndim, bias):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(ndim))
        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None

=== pool sample ===
{"id": 0, "text": "|Viewing Single Post From: Spoilers for the Week of February 11th|\n|Lil||Feb 1 2013, 09:58 AM|\nDon't care about Chloe/Taniel/Jen-Jen. Don't care about Sami, really, but hoping that we get some good \"SAMANTHA GENE!!\" Marlena Death-Stares out of it. And \"newfound\" feelings. Please. If only.\nSTEFANO!! STEFANO, STEFANO, STEFANO!!!! :cheer:\n|Spoilers for the Week of February 11th \u00b7 DAYS: News, Spoilers & Discussion|"}
{"id": 1, "text": "*sigh* Fundamentalist community, let me pass on some advice to you I learned from the atheistic community:\nIf you have set yourself on fire, do not run.\nOkay? Okay?? Please?\nLook, D, you had two months to say to Harvard in private emails, \"Im sorry, I shouldnt have been using that animation in my paid presentations. I wont use it again. I really do like 'Inner Life', though, and would love to use it in classroom presentations, from the BioVisions site, if that is acceptable.\"\nI sat here, for two months, waiting for that to happen, anything to happen, and it didnt. Two months, on your own terms, you could have written a similar post to yesterdays. I would have given you the benefit of the doubt-- maybe you didnt know the credits werent visible to the audience, and I wouldnt have said a word beyond this, as its Harvards problem, not mine. This would have been a funny joke to those of us involved in dealing with you people, but it would have been a PR non-issue for you.\nBut after you set yourself on fire, you didnt douse it out with a bucket of ice cold reality and accountability. You ran. And youre still running.\nWhy not just state \"I screwed up. Sorry everyone.\" and move on? Why the excuses? Why the denial? Why the passive language? Why the vague words and cryptic capitalizations? Why the writes and rewrites of your 'press release'? We know it wasnt written of your own volition, or it would have been done *before* Harvard had to take action. And, your behavior before this, regarding this issue, is not indicative of someone who made an innocent mistake. Its weird.\nSo what with this frantic running? Is the inability to say \"I was wrong\" a pathological feature of Creationists? Or are you hiding something? Or is it both? Or is it more?\nAnd now we get Casey weighing in on the issue, according to cre8id at AboveTopSecret.com-- PBS/NOVA online - Intelligent Design on trial:\n...to my knowledge, Discovery Institute has neither authorized nor received nor is making use of any presentation that used that animation. We have had nothing to do with creating or selling a DVD of that animation, nor do we have anything to do with placing that presentation on Google Video.I dont know what he is talking about with that last part, but the first part sounds similar to DIs claims post-Dover (\"WE HAD NOTHING TO DO WITH DOVER!\"). Maybe Luskin is telling the truth. Maybe this was a magic non-science Creation-friendly narration with convenient edits that AiG or ICR would have killed for... but onl
=== line count ===
182016 data/pool.jsonl
[metadata] {"noOutputExpected": false}

dtype uint16 len 1000000
' Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donations proved hard to come by . Nevertheless , in February 1907 , Shackleton presented to the Royal Geographic Society his plans for an Antarctic expedition , the details of which , under the name British Antarctic Expedition , were published in the Royal Society \'s newsletter , Geographic Journal . The aim was the conquest of both the geographical South Pole and the South Magnetic Pole . Shackleton then worked hard to persuade others of his wealthy friends and acquaintances to contribute , including Sir Philip Lee Brocklehurst , who subscribed £ 2 @,@ 000 ( 2011 equivalent £ 157 @,@ 000 ) to secure a place on the expedition ; author Campbell Mackellar ; and Guinness baron Lord Iveagh , whose contribution was secured less than two weeks before the departure of the expedition ship Nimrod . On 4 August 1907 , Shackleton was appointed a Member of the Royal Victorian Order , 4th Class ( MVO ; the present @-@ day grade of Lieutenant ) . \n<|endoftext|> On 1 January 1908 , Nimrod sailed for the Antarctic from Lyttelton Harbour , New Zealand . Shackleton \'s original plans had envisaged using the old Discovery base in McMurdo Sound to launch his attempts on the South Pole and South Magnetic Pole . However , before leaving England , he had been pressured to give an undertaking to Scott that he would not base himself in the McMurdo area , which Scott was claiming as his own field of work . Shackleton reluctantly agreed to look for winter quarters at either the Barrier Inlet ( which Discovery had briefly visited in 1902 ) or King Edward VII Land . \n<|endoftext|> To conserve coal , the ship was towed 1 @,@ 650 miles ( 2 @,@ 655 km ) by the steamer Koonya to the Antarctic ice , after Shackleton had persuaded the New Zealand government and the Union Steamship Company to share the cost . In accordance with Shackleton \'s promise to Scott , the ship headed for the eastern sector of the Great Ice Barrier , arriving there on 21 January 1908 . They found that the Barrier Inlet had expanded to form a large bay , in which were hundreds of whales , which led to the immediate christening of the area as the Bay of Whales . It was noted that ice conditions were unstable , precluding the establishment of a safe base there . An extended search for an anchorage at King Edward VII Land proved equally fruitless , so Shackleton was forced to break his undertaking to Scott and set sail for McMurdo Sound , a decision which , according to second officer Arthur Harbord , was " dictated by common sense " in view of the difficulties of ice pressure , coal shortage and the lack of any nearer known base . \n<|endoftext|> Nimrod arrived at McMurdo Sound on 29 January , but was stopped by ice 16 miles ( 26 km ) north of Discovery \'s old base at Hut Point . After considerable weather delays , Shackleton \'s base was eventually established at Cape Royds , about 24 miles ( 39 km ) north of Hut Point'

[stdout]
dtype uint16 len 1000000
' Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donations proved hard to come by . Nevertheless , in February 1907 , Shackleton presented to the Royal Geographic Society his plans for an Antarctic expedition , the details of which , under the name British Antarctic Expedition , were published in the Royal Society \'s newsletter , Geographic Journal . The aim was the conquest of both the geographical South Pole and the South Magnetic Pole . Shackleton then worked hard to persuade others of his wealthy friends and acquaintances to contribute , including Sir Philip Lee Brocklehurst , who subscribed £ 2 @,@ 000 ( 2011 equivalent £ 157 @,@ 000 ) to secure a place on the expedition ; author Campbell Mackellar ; and Guinness baron Lord Iveagh , whose contribution was secured less than two weeks before the departure of the expedition ship Nimrod . On 4 August 1907 , Shackleton was appointed a Member of the Royal Victorian Order , 4th Class ( MVO ; the present @-@ day grade of Lieutenant ) . \n<|endoftext|> On 1 January 1908 , Nimrod sailed for the Antarctic from Lyttelton Harbour , New Zealand . Shackleton \'s original plans had envisaged using the old Discovery base in McMurdo Sound to launch his attempts on the South Pole and South Magnetic Pole . However , before leaving England , he had been pressured to give an undertaking to Scott that he would not base himself in the McMurdo area , which Scott was claiming as his own field of work . Shackleton reluctantly agreed to look for winter quarters at either the Barrier Inlet ( which Discovery had briefly visited in 1902 ) or King Edward VII Land . \n<|endoftext|> To conserve coal , the ship was towed 1 @,@ 650 miles ( 2 @,@ 655 km ) by the steamer Koonya to the Antarctic ice , after Shackleton had persuaded the New Zealand government and the Union Steamship Company to share the cost . In accordance with Shackleton \'s promise to Scott , the ship headed for the eastern sector of the Great Ice Barrier , arriving there on 21 January 1908 . They found that the Barrier Inlet had expanded to form a large bay , in which were hundreds of whales , which led to the immediate christening of the area as the Bay of Whales . It was noted that ice conditions were unstable , precluding the establishment of a safe base there . An extended search for an anchorage at King Edward VII Land proved equally fruitless , so Shackleton was forced to break his undertaking to Scott and set sail for McMurdo Sound , a decision which , according to second officer Arthur Harbord , was " dictated by common sense " in view of the difficulties of ice pressure , coal shortage and the lack of any nearer known base . \n<|endoftext|> Nimrod arrived at McMurdo Sound on 29 January , but was stopped by ice 16 miles ( 26 km ) north of Discovery \'s old base at Hut Point . After considerable weather delays , Shackleton \'s base was eventually established at Cape Royds , about 24 miles ( 39 km ) north of Hut Point'
[metadata] {"noOutputExpected": false}

Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'sklearn'
---
=== frac 0.25 ===
"Description of a very high speed transit (VHST) system operating in its own rarefied atmosphere in evacuated tubes in underground tunnels. Most cases considered took less time to go coast-to-coast (e.g., 21 min) than it takes an aircraft to climb to an efficient operating altitude. VHST's tubecraft ride on, and are driven by, electromagnetic (EM) waves. In accelerating, it employs the energy of the surrounding EM field; in decelerating, it returns most of this energy to the"
=== frac 0.5 ===
'I fucking hate when he does this shit pic.twitter.com/kpmcHnW4Cz — Miley Ray Cyrus (@MileyCyrus) April 22, 2018\nSinger-actress Miley Cyrus has shared a rare video of herself with her partner, actor Liam Hemsworth, on social media. She shared a video of them both in a car, which showed Hemsworth listening to rap music as he drove them to their destination.Cyrus captioned the image: "I f***ing hate it when he'
=== frac 0.75 ===
"<p>I found the platform module but it says it returns 'Windows' and it's returning 'Microsoft' on my machine. I notice in another thread here on stackoverflow it returns 'Vista' sometimes.</p>\n<p>So, the question is, how do implemement?</p>\n<pre><code>if is_windows():\n  ...\n</code></pre>\n<p>In a forward compatible way? If I have to check for things like 'Vista' then it"
=== frac 0.95 ===
' to the debug perspective, I can only see the class instance (on which the function was called) and the two parameters. I cannot expand the class instance to see the values of its member variables. There is a space to the left of the instance so I would assume that there should be an arrow there so one is able to expand it in a similar way as in the Outline.</p>\n\n<p>Is there a configuration or something I must enable for this? Or have I misunderstood the variables window?</p>\n\n'

[stdout]
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'sklearn'
---
=== frac 0.25 ===
"Description of a very high speed transit (VHST) system operating in its own rarefied atmosphere in evacuated tubes in underground tunnels. Most cases considered took less time to go coast-to-coast (e.g., 21 min) than it takes an aircraft to climb to an efficient operating altitude. VHST's tubecraft ride on, and are driven by, electromagnetic (EM) waves. In accelerating, it employs the energy of the surrounding EM field; in decelerating, it returns most of this energy to the"
=== frac 0.5 ===
'I fucking hate when he does this shit pic.twitter.com/kpmcHnW4Cz — Miley Ray Cyrus (@MileyCyrus) April 22, 2018\nSinger-actress Miley Cyrus has shared a rare video of herself with her partner, actor Liam Hemsworth, on social media. She shared a video of them both in a car, which showed Hemsworth listening to rap music as he drove them to their destination.Cyrus captioned the image: "I f***ing hate it when he'
=== frac 0.75 ===
"<p>I found the platform module but it says it returns 'Windows' and it's returning 'Microsoft' on my machine. I notice in another thread here on stackoverflow it returns 'Vista' sometimes.</p>\n<p>So, the question is, how do implemement?</p>\n<pre><code>if is_windows():\n  ...\n</code></pre>\n<p>In a forward compatible way? If I have to check for things like 'Vista' then it"
=== frac 0.95 ===
' to the debug perspective, I can only see the class instance (on which the function was called) and the two parameters. I cannot expand the class instance to see the values of its member variables. There is a space to the left of the instance so I would assume that there should be an arrow there so one is able to expand it in a similar way as in the Outline.</p>\n\n<p>Is there a configuration or something I must enable for this? Or have I misunderstood the variables window?</p>\n\n'
[metadata] {"noOutputExpected": false}

total        used        free      shared  buff/cache   available
Mem:            1024           0        1023           0           0        1023
---timing---
20k docs in 1.9 s; avg words/doc 531

[stdout]
               total        used        free      shared  buff/cache   available
Mem:            1024           0        1023           0           0        1023
---timing---
20k docs in 1.9 s; avg words/doc 531
[metadata] {"noOutputExpected": false}

File created successfully at: /workspace/submission/curate.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/submission/curate.py", "content": "\"\"\"Curate the raw web pool for training a small LM whose held-out target is a\nBROAD high-quality English mix (Wikipedia + high-quality web prose + news +\ntechnical Q&A).\n\nCriterion (stated, reproducible):\n  Rank every pool document by how much its word-ngram distribution looks like the\n  DISCLOSED target domain rather than the raw web background -- i.e. a DSIR-style\n  (Data Selection via Importance Resampling) importance weight\n\n      score(doc) = mean over the doc's unigrams+bigrams of\n                     [ log P_target(ngram) - log P_raw(ngram) ]\n\n  P_target is estimated from the disclosed dev target (multi_dev.npy, decoded back\n  to text with the GPT-2 tokenizer -- it IS a sample of the scoring domain).\n  P_raw is estimated from the whole pool (the background web distribution).\n  A high score means the document uses the vocabulary/phrasing of clean\n  encyclopedic / news / prose / technical-QA English far more than generic web\n  spam does, so training on it should lower held-out perplexity on that domain.\n\nLight quality gates on top of the domain score:\n  - drop documents shorter than MIN_WORDS (too little signal, noisy per-token score)\n  - drop exact-duplicate texts (dedup by normalized-text hash) so the 12M-token\n    budget is not wasted re-reading the same document.\n\nOutput: /workspace/submission/selection.json -- pool ids in priority order\n(best-first), enough to comfortably exceed the 12M-token training budget.\n\"\"\"\nimport json, re, zlib, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\n\nB         = 1 << 20     # hashed n-gram buckets\nPRIME     = 1000003     # bigram mixing prime\nSMOOTH    = 1.0         # additive smoothing on counts\nMIN_WORDS = 50          # minimum words to keep a doc\nN_OUT     = 50000       # how many ranked ids to emit (>> enough for 12M tokens)\n\nWORD = re.compile(r\"[a-z0-9']+\")\n\ndef words(text):\n    return WORD.findall(text.lower())\n\n# ---- stable per-word hash (deterministic across processes) ----------------\n_wid_cache = {}\ndef wid(w):\n    v = _wid_cache.get(w)\n    if v is None:\n        v = zlib.crc32(w.encode(\"utf-8\")) & 0xFFFFFFFF\n        _wid_cache[w] = v\n    return v\n\ndef doc_buckets(ws):\n    \"\"\"unigram + bigram bucket ids for a list of words, as a uint32 array.\"\"\"\n    if not ws:\n        return np.empty(0, np.uint32)\n    ids = np.fromiter((wid(w) for w in ws), dtype=np.uint64, count=len(ws))\n    uni = (ids % B).astype(np.uint32)\n    if len(ids) > 1:\n        big = (((ids[:-1] * PRIME) + ids[1:]) % B).astype(np.uint32)\n        return np.concatenate([uni, big])\n    return uni\n\n# ---- 1. target distribution from the disclosed dev sample -----------------\nprint(\"decoding dev target ...\", flush=True)\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV)\ndev_text = tok.decode(dev.tolist())\ntgt_counts = np.bincount(doc_buckets(words(dev_text)), minlength=B).astype(np.float64)\n\n# ---- 2. read pool, hash every doc, accumulate raw distribution ------------\nprint(\"reading + hashing pool ...\", flush=True)\nids, offsets, all_b, wlens = [], [0], [], []\nseen = set()\nraw_counts = np.zeros(B, np.float64)\nfor line in open(POOL):\n    r = json.loads(line)\n    ws = words(r[\"text\"])\n    # exact-duplicate dedup on normalized text\n    h = zlib.crc32(\" \".join(ws).encode(\"utf-8\"))\n    if h in seen:\n        continue\n    seen.add(h)\n    b = doc_buckets(ws)\n    raw_counts += np.bincount(b, minlength=B)\n    ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))\n    wlens.append(len(ws))\nids = np.array(ids); wlens = np.array(wlens)\nbig_b = np.concatenate(all_b) if all_b else np.empty(0, np.uint32)\noffsets = np.array(offsets)\nprint(f\"  kept {len(ids)} docs after dedup\", flush=True)\n\n# ---- 3. log importance-weight lookup table --------------------------------\ntgt_p = (tgt_counts + SMOOTH) / (tgt_counts.sum() + SMOOTH * B)\nraw_p = (raw_counts + SMOOTH) / (raw_counts.sum() + SMOOTH * B)\nlogr  = np.log(tgt_p) - np.log(raw_p)           # per-bucket log importance weight\n\n# ---- 4. per-doc mean log importance weight --------------------------------\nprint(\"scoring docs ...\", flush=True)\nper_bucket = logr[big_b]                         # value for every ngram occurrence\ncsum = np.concatenate([[0.0], np.cumsum(per_bucket)])\ndoc_sum = csum[offsets[1:]] - csum[offsets[:-1]]\nnb = (offsets[1:] - offsets[:-1]).astype(np.float64)\nscores = np.where(nb > 0, doc_sum / nb, -1e9)\n\n# ---- 5. filter + rank -----------------------------------------------------\nkeep = wlens >= MIN_WORDS\norder = np.argsort(-scores)\norder = order[keep[order]]\nselection = ids[order][:N_OUT].tolist()\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids -> {OUT}\")\nprint(\"score range kept: %.3f .. %.3f\" % (scores[order][0], scores[order][min(len(order)-1, N_OUT-1)]))\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bwhcwd58q). Output is being written to: /tmp/claude-0/-workspace/0f98a815-3e2d-46b4-89e2-accd4d6daa8e/tasks/bwhcwd58q.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bwhcwd58q", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

done

[stdout]
done
[metadata] {"noOutputExpected": false}

The file /workspace/submission/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "ids, offsets, all_b, wlens = [], [0], [], []\nseen = set()\nraw_counts = np.zeros(B, np.float64)\nfor line in open(POOL):\n    r = json.loads(line)\n    ws = words(r[\"text\"])\n    # exact-duplicate dedup on normalized text\n    h = zlib.crc32(\" \".join(ws).encode(\"utf-8\"))\n    if h in seen:\n        continue\n    seen.add(h)\n    b = doc_buckets(ws)\n    raw_counts += np.bincount(b, minlength=B)\n    ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))\n    wlens.append(len(ws))\nids = np.array(ids); wlens = np.array(wlens)\nbig_b = np.concatenate(all_b) if all_b else np.empty(0, np.uint32)\noffsets = np.array(offsets)\nprint(f\"  kept {len(ids)} docs after dedup\", flush=True)", "newString": "ids, offsets, all_b, wlens = [], [0], [], []\nseen = set()\nfor line in open(POOL):\n    r = json.loads(line)\n    ws = words(r[\"text\"])\n    # exact-duplicate dedup on normalized text\n    h = zlib.crc32(\" \".join(ws).encode(\"utf-8\"))\n    if h in seen:\n        continue\n    seen.add(h)\n    b = doc_buckets(ws)\n    ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))\n    wlens.append(len(ws))\nids = np.array(ids); wlens = np.array(wlens)\nbig_b = np.concatenate(all_b) if all_b else np.empty(0, np.uint32)\noffsets = np.array(offsets)\nraw_counts = np.bincount(big_b, minlength=B).astype(np.float64)  # single global count\nprint(f\"  kept {len(ids)} docs after dedup\", flush=True)", "originalFile": "\"\"\"Curate the raw web pool for training a small LM whose held-out target is a\nBROAD high-quality English mix (Wikipedia + high-quality web prose + news +\ntechnical Q&A).\n\nCriterion (stated, reproducible):\n  Rank every pool document by how much its word-ngram distribution looks like the\n  DISCLOSED target domain rather than the raw web background -- i.e. a DSIR-style\n  (Data Selection via Importance Resampling) importance weight\n\n      score(doc) = mean over the doc's unigrams+bigrams of\n                     [ log P_target(ngram) - log P_raw(ngram) ]\n\n  P_target is estimated from the disclosed dev target (multi_dev.npy, decoded back\n  to text with the GPT-2 tokenizer -- it IS a sample of the scoring domain).\n  P_raw is estimated from the whole pool (the background web distribution).\n  A high score means the document uses the vocabulary/phrasing of clean\n  encyclopedic / news / prose / technical-QA English far more than generic web\n  spam does, so training on it should lower held-out perplexity on that domain.\n\nLight quality gates on top of the domain score:\n  - drop documents shorter than MIN_WORDS (too little signal, noisy per-token score)\n  - drop exact-duplicate texts (dedup by normalized-text hash) so the 12M-token\n    budget is not wasted re-reading the same document.\n\nOutput: /workspace/submission/selection.json -- pool ids in priority order\n(best-first), enough to comfortably exceed the 12M-token training budget.\n\"\"\"\nimport json, re, zlib, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\n\nB         = 1 << 20     # hashed n-gram buckets\nPRIME     = 1000003     # bigram mixing prime\nSMOOTH    = 1.0         # additive smoothing on counts\nMIN_WORDS = 50          # minimum words to keep a doc\nN_OUT     = 50000       # how many ranked ids to emit (>> enough for 12M tokens)\n\nWORD = re.compile(r\"[a-z0-9']+\")\n\ndef words(text):\n    return WORD.findall(text.lower())\n\n# ---- stable per-word hash (deterministic across processes) ----------------\n_wid_cache = {}\ndef wid(w):\n    v = _wid_cache.get(w)\n    if v is None:\n        v = zlib.crc32(w.encode(\"utf-8\")) & 0xFFFFFFFF\n        _wid_cache[w] = v\n    return v\n\ndef doc_buckets(ws):\n    \"\"\"unigram + bigram bucket ids for a list of words, as a uint32 array.\"\"\"\n    if not ws:\n        return np.empty(0, np.uint32)\n    ids = np.fromiter((wid(w) for w in ws), dtype=np.uint64, count=len(ws))\n    uni = (ids % B).astype(np.uint32)\n    if len(ids) > 1:\n        big = (((ids[:-1] * PRIME) + ids[1:]) % B).astype(np.uint32)\n        return np.concatenate([uni, big])\n    return uni\n\n# ---- 1. target distribution from the disclosed dev sample -----------------\nprint(\"decoding dev target ...\", flush=True)\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV)\ndev_text = tok.decode(dev.tolist())\ntgt_counts = np.bincount(doc_buckets(words(dev_text)), minlength=B).astype(np.float64)\n\n# ---- 2. read pool, hash every doc, accumulate raw distribution ------------\nprint(\"reading + hashing pool ...\", flush=True)\nids, offsets, all_b, wlens = [], [0], [], []\nseen = set()\nraw_counts = np.zeros(B, np.float64)\nfor line in open(POOL):\n    r = json.loads(line)\n    ws = words(r[\"text\"])\n    # exact-duplicate dedup on normalized text\n    h = zlib.crc32(\" \".join(ws).encode(\"utf-8\"))\n    if h in seen:\n        continue\n    seen.add(h)\n    b = doc_buckets(ws)\n    raw_counts += np.bincount(b, minlength=B)\n    ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))\n    wlens.append(len(ws))\nids = np.array(ids); wlens = np.array(wlens)\nbig_b = np.concatenate(all_b) if all_b else np.empty(0, np.uint32)\noffsets = np.array(offsets)\nprint(f\"  kept {len(ids)} docs after dedup\", flush=True)\n\n# ---- 3. log importance-weight lookup table --------------------------------\ntgt_p = (tgt_counts + SMOOTH) / (tgt_counts.sum() + SMOOTH * B)\nraw_p = (raw_counts + SMOOTH) / (raw_counts.sum() + SMOOTH * B)\nlogr  = np.log(tgt_p) - np.log(raw_p)           # per-bucket log importance weight\n\n# ---- 4. per-doc mean log importance weight --------------------------------\nprint(\"scoring docs ...\", flush=True)\nper_bucket = logr[big_b]                         # value for every ngram occurrence\ncsum = np.concatenate([[0.0], np.cumsum(per_bucket)])\ndoc_sum = csum[offsets[1:]] - csum[offsets[:-1]]\nnb = (offsets[1:] - offsets[:-1]).astype(np.float64)\nscores = np.where(nb > 0, doc_sum / nb, -1e9)\n\n# ---- 5. filter + rank -----------------------------------------------------\nkeep = wlens >= MIN_WORDS\norder = np.argsort(-scores)\norder = order[keep[order]]\nselection = ids[order][:N_OUT].tolist()\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids -> {OUT}\")\nprint(\"score range kept: %.3f .. %.3f\" % (scores[order][0], scores[order][min(len(order)-1, N_OUT-1)]))\n", "structuredPatch": [{"oldStart": 74, "oldLines": 7, "newStart": 74, "newLines": 6, "lines": [" print(\"reading + hashing pool ...\", flush=True)", " ids, offsets, all_b, wlens = [], [0], [], []", " seen = set()", "-raw_counts = np.zeros(B, np.float64)", " for line in open(POOL):", "     r = json.loads(line)", "     ws = words(r[\"text\"])"]}, {"oldStart": 84, "oldLines": 12, "newStart": 83, "newLines": 12, "lines": ["         continue", "     seen.add(h)", "     b = doc_buckets(ws)", "-    raw_counts += np.bincount(b, minlength=B)", "     ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))", "     wlens.append(len(ws))", " ids = np.array(ids); wlens = np.array(wlens)", " big_b = np.concatenate(all_b) if all_b else np.empty(0, np.uint32)", " offsets = np.array(offsets)", "+raw_counts = np.bincount(big_b, minlength=B).astype(np.float64)  # single global count", " print(f\"  kept {len(ids)} docs after dedup\", flush=True)", " ", " # ---- 3. log importance-weight lookup table --------------------------------"]}], "userModified": false, "replaceAll": false}

decoding dev target ...
reading + hashing pool ...
  kept 170244 docs after dedup
scoring docs ...
  scores = np.where(nb > 0, doc_sum / nb, -1e9)
wrote 50000 ids -> /workspace/submission/selection.json
score range kept: 0.534 .. -0.299

real	1m12.791s
user	1m8.150s
sys	0m1.650s

[stdout]
decoding dev target ...
reading + hashing pool ...
  kept 170244 docs after dedup
scoring docs ...
  scores = np.where(nb > 0, doc_sum / nb, -1e9)
wrote 50000 ids -> /workspace/submission/selection.json
score range kept: 0.534 .. -0.299

real	1m12.791s
user	1m8.150s
sys	0m1.650s
[metadata] {"noOutputExpected": false}

=== TOP 3 ===
[124335] '.<|endoftext|>Prime number 189650000 - 189655000\nPrime-Numbers.org\nPrime Numbers: 189650000-189655000\n<Home> <Prev> <Next>\n189650003      189650009      189650057      189650063\n189650093      189650119      189650177      189650207\n189650213      189650233      189650239      18' 

[169616] 'ancel\nSuccess\nOK<|endoftext|>FloraPix\nTropical Plant Picture Gallery (15497)\nFloraPix all\n>Home >Info >Guestbook >New (recently uploaded)\n1-garden\n1-general\n?\nAbroma\nAbromeitiella\nAcacallis\nAcacia\nAcanthaceae\nAcanthocereus\nAcanthostachys\nAcineta\nAcronia\nAda\nAdenium\nAechmea\nAechme' 

[123496] ' Out the Box<|endoftext|>Prime numbers\nPrime-Numbers.org\nPrime Numbers: 802500000\n<Home> <Prev> <Next>\n802500000\xa0\xa0\xa0\xa0802550000\xa0\xa0\xa0\xa0802600000\xa0\xa0\xa0\xa0802650000\n802700000\xa0\xa0\xa0\xa0802750000\xa0\xa0\xa0\xa0802800000\xa0\xa0\xa0\xa0802850000\n802900000\xa0\xa0\xa0\xa0802950000\xa0\xa0\xa0\xa0803000000\xa0\xa0\xa0\xa0803050000\n803100000\xa0\xa0\xa0\xa0803150000\xa0\xa0\xa0\xa08032' 

=== rank ~11000 (near budget edge) ===
[20368] "But I’m clear that executive pay is a matter for Boards and shareholders – not for Governments.\nI'm clear that executive pay is a matter for boards and shareholders, not for governments and regulators" 

[80033] ".<|endoftext|>Taman Idaman Hati Residence\nSingle Storey Terrace\nLand Area 20' x 69'\nBuilt-up Area 1,080 Sq.Ft.\nJalan Kebun, Shah Alam\nSection 30 is a small town located in the area of Shah Alam, Selan" 

=== BOTTOM of selection (rank 49999) ===
'�re throwing a bash, and it’ll be epic—or a total flop. It depends on having the right amount of booze. Too little means a drink run midparty; too much, and good luck kicking people out.\nTime for a li'

[stdout]
=== TOP 3 ===
[124335] '.<|endoftext|>Prime number 189650000 - 189655000\nPrime-Numbers.org\nPrime Numbers: 189650000-189655000\n<Home> <Prev> <Next>\n189650003      189650009      189650057      189650063\n189650093      189650119      189650177      189650207\n189650213      189650233      189650239      18' 

[169616] 'ancel\nSuccess\nOK<|endoftext|>FloraPix\nTropical Plant Picture Gallery (15497)\nFloraPix all\n>Home >Info >Guestbook >New (recently uploaded)\n1-garden\n1-general\n?\nAbroma\nAbromeitiella\nAcacallis\nAcacia\nAcanthaceae\nAcanthocereus\nAcanthostachys\nAcineta\nAcronia\nAda\nAdenium\nAechmea\nAechme' 

[123496] ' Out the Box<|endoftext|>Prime numbers\nPrime-Numbers.org\nPrime Numbers: 802500000\n<Home> <Prev> <Next>\n802500000\xa0\xa0\xa0\xa0802550000\xa0\xa0\xa0\xa0802600000\xa0\xa0\xa0\xa0802650000\n802700000\xa0\xa0\xa0\xa0802750000\xa0\xa0\xa0\xa0802800000\xa0\xa0\xa0\xa0802850000\n802900000\xa0\xa0\xa0\xa0802950000\xa0\xa0\xa0\xa0803000000\xa0\xa0\xa0\xa0803050000\n803100000\xa0\xa0\xa0\xa0803150000\xa0\xa0\xa0\xa08032' 

=== rank ~11000 (near budget edge) ===
[20368] "But I’m clear that executive pay is a matter for Boards and shareholders – not for Governments.\nI'm clear that executive pay is a matter for boards and shareholders, not for governments and regulators" 

[80033] ".<|endoftext|>Taman Idaman Hati Residence\nSingle Storey Terrace\nLand Area 20' x 69'\nBuilt-up Area 1,080 Sq.Ft.\nJalan Kebun, Shah Alam\nSection 30 is a small town located in the area of Shah Alam, Selan" 

=== BOTTOM of selection (rank 49999) ===
'�re throwing a bash, and it’ll be epic—or a total flop. It depends on having the right amount of booze. Too little means a drink run midparty; too much, and good luck kicking people out.\nTime for a li'
[metadata] {"noOutputExpected": false}

The file /workspace/submission/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "B         = 1 << 20     # hashed n-gram buckets\nPRIME     = 1000003     # bigram mixing prime\nSMOOTH    = 1.0         # additive smoothing on counts\nMIN_WORDS = 50          # minimum words to keep a doc\nN_OUT     = 50000       # how many ranked ids to emit (>> enough for 12M tokens)\n\nWORD = re.compile(r\"[a-z0-9']+\")", "newString": "B         = 1 << 20     # hashed n-gram buckets\nPRIME     = 1000003     # bigram mixing prime\nSMOOTH    = 1.0         # additive smoothing on counts\nMIN_WORDS = 50          # minimum words to keep a doc\nN_OUT     = 50000       # how many ranked ids to emit (>> enough for 12M tokens)\n\n# prose-quality gates -- reject non-prose (number/name lists, nav menus, spam).\n# The domain score alone rewards low-diversity docs saturated with a few\n# target-ish tokens (e.g. pages that are ALL numbers), so we first require the\n# document to LOOK like fluent English prose before scoring its domain match.\nSTOP_MIN, STOP_MAX = 0.20, 0.60   # fraction of tokens that are common stopwords\nDIGIT_MAX          = 0.15         # fraction of tokens that are purely numeric\nALPHA_MIN          = 0.60         # fraction of tokens that are purely alphabetic\nTTR_MAX            = 0.85         # type/token ratio cap (kills list spam that never repeats)\n\nSTOPWORDS = set((\"the of and to a in that is was for it with as his he on be at by \"\n    \"i this had not are but from or have an they which one you were her all she there \"\n    \"would their we him been has when who will more no if out so said what up its about \"\n    \"into than them can only other new some could time these two may then do first any \"\n    \"my now such like our over man me even most made after also did many before must \"\n    \"through back years where much your way well down should because each just those \"\n    \"people how too little state good very make world still see own work men day get here \"\n    \"between both under never same another know while last might us great old year off \"\n    \"come since against go came right used take three states himself few house use during \"\n    \"without again place around however home small found thought went say part once \"\n    \"general high upon school every don does got united left number course war until \"\n    \"always away something fact water though less public put think almost hand enough far \"\n    \"took head yet government system set told nothing end why called didn eyes find going \"\n    \"look asked later knew point next city).\").split())\n\nWORD = re.compile(r\"[a-z0-9']+\")", "originalFile": "\"\"\"Curate the raw web pool for training a small LM whose held-out target is a\nBROAD high-quality English mix (Wikipedia + high-quality web prose + news +\ntechnical Q&A).\n\nCriterion (stated, reproducible):\n  Rank every pool document by how much its word-ngram distribution looks like the\n  DISCLOSED target domain rather than the raw web background -- i.e. a DSIR-style\n  (Data Selection via Importance Resampling) importance weight\n\n      score(doc) = mean over the doc's unigrams+bigrams of\n                     [ log P_target(ngram) - log P_raw(ngram) ]\n\n  P_target is estimated from the disclosed dev target (multi_dev.npy, decoded back\n  to text with the GPT-2 tokenizer -- it IS a sample of the scoring domain).\n  P_raw is estimated from the whole pool (the background web distribution).\n  A high score means the document uses the vocabulary/phrasing of clean\n  encyclopedic / news / prose / technical-QA English far more than generic web\n  spam does, so training on it should lower held-out perplexity on that domain.\n\nLight quality gates on top of the domain score:\n  - drop documents shorter than MIN_WORDS (too little signal, noisy per-token score)\n  - drop exact-duplicate texts (dedup by normalized-text hash) so the 12M-token\n    budget is not wasted re-reading the same document.\n\nOutput: /workspace/submission/selection.json -- pool ids in priority order\n(best-first), enough to comfortably exceed the 12M-token training budget.\n\"\"\"\nimport json, re, zlib, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\n\nB         = 1 << 20     # hashed n-gram buckets\nPRIME     = 1000003     # bigram mixing prime\nSMOOTH    = 1.0         # additive smoothing on counts\nMIN_WORDS = 50          # minimum words to keep a doc\nN_OUT     = 50000       # how many ranked ids to emit (>> enough for 12M tokens)\n\nWORD = re.compile(r\"[a-z0-9']+\")\n\ndef words(text):\n    return WORD.findall(text.lower())\n\n# ---- stable per-word hash (deterministic across processes) ----------------\n_wid_cache = {}\ndef wid(w):\n    v = _wid_cache.get(w)\n    if v is None:\n        v = zlib.crc32(w.encode(\"utf-8\")) & 0xFFFFFFFF\n        _wid_cache[w] = v\n    return v\n\ndef doc_buckets(ws):\n    \"\"\"unigram + bigram bucket ids for a list of words, as a uint32 array.\"\"\"\n    if not ws:\n        return np.empty(0, np.uint32)\n    ids = np.fromiter((wid(w) for w in ws), dtype=np.uint64, count=len(ws))\n    uni = (ids % B).astype(np.uint32)\n    if len(ids) > 1:\n        big = (((ids[:-1] * PRIME) + ids[1:]) % B).astype(np.uint32)\n        return np.concatenate([uni, big])\n    return uni\n\n# ---- 1. target distribution from the disclosed dev sample -----------------\nprint(\"decoding dev target ...\", flush=True)\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV)\ndev_text = tok.decode(dev.tolist())\ntgt_counts = np.bincount(doc_buckets(words(dev_text)), minlength=B).astype(np.float64)\n\n# ---- 2. read pool, hash every doc, accumulate raw distribution ------------\nprint(\"reading + hashing pool ...\", flush=True)\nids, offsets, all_b, wlens = [], [0], [], []\nseen = set()\nfor line in open(POOL):\n    r = json.loads(line)\n    ws = words(r[\"text\"])\n    # exact-duplicate dedup on normalized text\n    h = zlib.crc32(\" \".join(ws).encode(\"utf-8\"))\n    if h in seen:\n        continue\n    seen.add(h)\n    b = doc_buckets(ws)\n    ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))\n    wlens.append(len(ws))\nids = np.array(ids); wlens = np.array(wlens)\nbig_b = np.concatenate(all_b) if all_b else np.empty(0, np.uint32)\noffsets = np.array(offsets)\nraw_counts = np.bincount(big_b, minlength=B).astype(np.float64)  # single global count\nprint(f\"  kept {len(ids)} docs after dedup\", flush=True)\n\n# ---- 3. log importance-weight lookup table --------------------------------\ntgt_p = (tgt_counts + SMOOTH) / (tgt_counts.sum() + SMOOTH * B)\nraw_p = (raw_counts + SMOOTH) / (raw_counts.sum() + SMOOTH * B)\nlogr  = np.log(tgt_p) - np.log(raw_p)           # per-bucket log importance weight\n\n# ---- 4. per-doc mean log importance weight --------------------------------\nprint(\"scoring docs ...\", flush=True)\nper_bucket = logr[big_b]                         # value for every ngram occurrence\ncsum = np.concatenate([[0.0], np.cumsum(per_bucket)])\ndoc_sum = csum[offsets[1:]] - csum[offsets[:-1]]\nnb = (offsets[1:] - offsets[:-1]).astype(np.float64)\nscores = np.where(nb > 0, doc_sum / nb, -1e9)\n\n# ---- 5. filter + rank -----------------------------------------------------\nkeep = wlens >= MIN_WORDS\norder = np.argsort(-scores)\norder = order[keep[order]]\nselection = ids[order][:N_OUT].tolist()\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids -> {OUT}\")\nprint(\"score range kept: %.3f .. %.3f\" % (scores[order][0], scores[order][min(len(order)-1, N_OUT-1)]))\n", "structuredPatch": [{"oldStart": 38, "oldLines": 6, "newStart": 38, "newLines": 30, "lines": [" MIN_WORDS = 50          # minimum words to keep a doc", " N_OUT     = 50000       # how many ranked ids to emit (>> enough for 12M tokens)", " ", "+# prose-quality gates -- reject non-prose (number/name lists, nav menus, spam).", "+# The domain score alone rewards low-diversity docs saturated with a few", "+# target-ish tokens (e.g. pages that are ALL numbers), so we first require the", "+# document to LOOK like fluent English prose before scoring its domain match.", "+STOP_MIN, STOP_MAX = 0.20, 0.60   # fraction of tokens that are common stopwords", "+DIGIT_MAX          = 0.15         # fraction of tokens that are purely numeric", "+ALPHA_MIN          = 0.60         # fraction of tokens that are purely alphabetic", "+TTR_MAX            = 0.85         # type/token ratio cap (kills list spam that never repeats)", "+", "+STOPWORDS = set((\"the of and to a in that is was for it with as his he on be at by \"", "+    \"i this had not are but from or have an they which one you were her all she there \"", "+    \"would their we him been has when who will more no if out so said what up its about \"", "+    \"into than them can only other new some could time these two may then do first any \"", "+    \"my now such like our over man me even most made after also did many before must \"", "+    \"through back years where much your way well down should because each just those \"", "+    \"people how too little state good very make world still see own work men day get here \"", "+    \"between both under never same another know while last might us great old year off \"", "+    \"come since against go came right used take three states himself few house use during \"", "+    \"without again place around however home small found thought went say part once \"", "+    \"general high upon school every don does got united left number course war until \"", "+    \"always away something fact water though less public put think almost hand enough far \"", "+    \"took head yet government system set told nothing end why called didn eyes find going \"", "+    \"look asked later knew point next city).\").split())", "+", " WORD = re.compile(r\"[a-z0-9']+\")", " ", " def words(text):"]}], "userModified": false, "replaceAll": false}

The file /workspace/submission/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "ids, offsets, all_b, wlens = [], [0], [], []\nseen = set()\nfor line in open(POOL):\n    r = json.loads(line)\n    ws = words(r[\"text\"])\n    # exact-duplicate dedup on normalized text\n    h = zlib.crc32(\" \".join(ws).encode(\"utf-8\"))\n    if h in seen:\n        continue\n    seen.add(h)\n    b = doc_buckets(ws)\n    ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))\n    wlens.append(len(ws))\nids = np.array(ids); wlens = np.array(wlens)", "newString": "def prose_quality(ws):\n    \"\"\"Cheap prose-ness gate. Returns True if the doc looks like English prose.\"\"\"\n    n = len(ws)\n    if n < MIN_WORDS:\n        return False\n    nstop = nalpha = ndigit = 0\n    for w in ws:\n        if w in STOPWORDS: nstop += 1\n        if w.isalpha():    nalpha += 1\n        elif w.isdigit():  ndigit += 1\n    if not (STOP_MIN <= nstop / n <= STOP_MAX): return False\n    if ndigit / n > DIGIT_MAX:                  return False\n    if nalpha / n < ALPHA_MIN:                  return False\n    if len(set(ws)) / n > TTR_MAX:              return False   # too little repetition\n    return True\n\nids, offsets, all_b, wlens = [], [0], [], []\nseen = set()\nfor line in open(POOL):\n    r = json.loads(line)\n    ws = words(r[\"text\"])\n    # exact-duplicate dedup on normalized text\n    h = zlib.crc32(\" \".join(ws).encode(\"utf-8\"))\n    if h in seen:\n        continue\n    seen.add(h)\n    if not prose_quality(ws):\n        continue\n    b = doc_buckets(ws)\n    ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))\n    wlens.append(len(ws))\nids = np.array(ids); wlens = np.array(wlens)", "originalFile": "\"\"\"Curate the raw web pool for training a small LM whose held-out target is a\nBROAD high-quality English mix (Wikipedia + high-quality web prose + news +\ntechnical Q&A).\n\nCriterion (stated, reproducible):\n  Rank every pool document by how much its word-ngram distribution looks like the\n  DISCLOSED target domain rather than the raw web background -- i.e. a DSIR-style\n  (Data Selection via Importance Resampling) importance weight\n\n      score(doc) = mean over the doc's unigrams+bigrams of\n                     [ log P_target(ngram) - log P_raw(ngram) ]\n\n  P_target is estimated from the disclosed dev target (multi_dev.npy, decoded back\n  to text with the GPT-2 tokenizer -- it IS a sample of the scoring domain).\n  P_raw is estimated from the whole pool (the background web distribution).\n  A high score means the document uses the vocabulary/phrasing of clean\n  encyclopedic / news / prose / technical-QA English far more than generic web\n  spam does, so training on it should lower held-out perplexity on that domain.\n\nLight quality gates on top of the domain score:\n  - drop documents shorter than MIN_WORDS (too little signal, noisy per-token score)\n  - drop exact-duplicate texts (dedup by normalized-text hash) so the 12M-token\n    budget is not wasted re-reading the same document.\n\nOutput: /workspace/submission/selection.json -- pool ids in priority order\n(best-first), enough to comfortably exceed the 12M-token training budget.\n\"\"\"\nimport json, re, zlib, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\n\nB         = 1 << 20     # hashed n-gram buckets\nPRIME     = 1000003     # bigram mixing prime\nSMOOTH    = 1.0         # additive smoothing on counts\nMIN_WORDS = 50          # minimum words to keep a doc\nN_OUT     = 50000       # how many ranked ids to emit (>> enough for 12M tokens)\n\n# prose-quality gates -- reject non-prose (number/name lists, nav menus, spam).\n# The domain score alone rewards low-diversity docs saturated with a few\n# target-ish tokens (e.g. pages that are ALL numbers), so we first require the\n# document to LOOK like fluent English prose before scoring its domain match.\nSTOP_MIN, STOP_MAX = 0.20, 0.60   # fraction of tokens that are common stopwords\nDIGIT_MAX          = 0.15         # fraction of tokens that are purely numeric\nALPHA_MIN          = 0.60         # fraction of tokens that are purely alphabetic\nTTR_MAX            = 0.85         # type/token ratio cap (kills list spam that never repeats)\n\nSTOPWORDS = set((\"the of and to a in that is was for it with as his he on be at by \"\n    \"i this had not are but from or have an they which one you were her all she there \"\n    \"would their we him been has when who will more no if out so said what up its about \"\n    \"into than them can only other new some could time these two may then do first any \"\n    \"my now such like our over man me even most made after also did many before must \"\n    \"through back years where much your way well down should because each just those \"\n    \"people how too little state good very make world still see own work men day get here \"\n    \"between both under never same another know while last might us great old year off \"\n    \"come since against go came right used take three states himself few house use during \"\n    \"without again place around however home small found thought went say part once \"\n    \"general high upon school every don does got united left number course war until \"\n    \"always away something fact water though less public put think almost hand enough far \"\n    \"took head yet government system set told nothing end why called didn eyes find going \"\n    \"look asked later knew point next city).\").split())\n\nWORD = re.compile(r\"[a-z0-9']+\")\n\ndef words(text):\n    return WORD.findall(text.lower())\n\n# ---- stable per-word hash (deterministic across processes) ----------------\n_wid_cache = {}\ndef wid(w):\n    v = _wid_cache.get(w)\n    if v is None:\n        v = zlib.crc32(w.encode(\"utf-8\")) & 0xFFFFFFFF\n        _wid_cache[w] = v\n    return v\n\ndef doc_buckets(ws):\n    \"\"\"unigram + bigram bucket ids for a list of words, as a uint32 array.\"\"\"\n    if not ws:\n        return np.empty(0, np.uint32)\n    ids = np.fromiter((wid(w) for w in ws), dtype=np.uint64, count=len(ws))\n    uni = (ids % B).astype(np.uint32)\n    if len(ids) > 1:\n        big = (((ids[:-1] * PRIME) + ids[1:]) % B).astype(np.uint32)\n        return np.concatenate([uni, big])\n    return uni\n\n# ---- 1. target distribution from the disclosed dev sample -----------------\nprint(\"decoding dev target ...\", flush=True)\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV)\ndev_text = tok.decode(dev.tolist())\ntgt_counts = np.bincount(doc_buckets(words(dev_text)), minlength=B).astype(np.float64)\n\n# ---- 2. read pool, hash every doc, accumulate raw distribution ------------\nprint(\"reading + hashing pool ...\", flush=True)\nids, offsets, all_b, wlens = [], [0], [], []\nseen = set()\nfor line in open(POOL):\n    r = json.loads(line)\n    ws = words(r[\"text\"])\n    # exact-duplicate dedup on normalized text\n    h = zlib.crc32(\" \".join(ws).encode(\"utf-8\"))\n    if h in seen:\n        continue\n    seen.add(h)\n    b = doc_buckets(ws)\n    ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))\n    wlens.append(len(ws))\nids = np.array(ids); wlens = np.array(wlens)\nbig_b = np.concatenate(all_b) if all_b else np.empty(0, np.uint32)\noffsets = np.array(offsets)\nraw_counts = np.bincount(big_b, minlength=B).astype(np.float64)  # single global count\nprint(f\"  kept {len(ids)} docs after dedup\", flush=True)\n\n# ---- 3. log importance-weight lookup table --------------------------------\ntgt_p = (tgt_counts + SMOOTH) / (tgt_counts.sum() + SMOOTH * B)\nraw_p = (raw_counts + SMOOTH) / (raw_counts.sum() + SMOOTH * B)\nlogr  = np.log(tgt_p) - np.log(raw_p)           # per-bucket log importance weight\n\n# ---- 4. per-doc mean log importance weight --------------------------------\nprint(\"scoring docs ...\", flush=True)\nper_bucket = logr[big_b]                         # value for every ngram occurrence\ncsum = np.concatenate([[0.0], np.cumsum(per_bucket)])\ndoc_sum = csum[offsets[1:]] - csum[offsets[:-1]]\nnb = (offsets[1:] - offsets[:-1]).astype(np.float64)\nscores = np.where(nb > 0, doc_sum / nb, -1e9)\n\n# ---- 5. filter + rank -----------------------------------------------------\nkeep = wlens >= MIN_WORDS\norder = np.argsort(-scores)\norder = order[keep[order]]\nselection = ids[order][:N_OUT].tolist()\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids -> {OUT}\")\nprint(\"score range kept: %.3f .. %.3f\" % (scores[order][0], scores[order][min(len(order)-1, N_OUT-1)]))\n", "structuredPatch": [{"oldStart": 96, "oldLines": 6, "newStart": 96, "newLines": 22, "lines": [" ", " # ---- 2. read pool, hash every doc, accumulate raw distribution ------------", " print(\"reading + hashing pool ...\", flush=True)", "+def prose_quality(ws):", "+    \"\"\"Cheap prose-ness gate. Returns True if the doc looks like English prose.\"\"\"", "+    n = len(ws)", "+    if n < MIN_WORDS:", "+        return False", "+    nstop = nalpha = ndigit = 0", "+    for w in ws:", "+        if w in STOPWORDS: nstop += 1", "+        if w.isalpha():    nalpha += 1", "+        elif w.isdigit():  ndigit += 1", "+    if not (STOP_MIN <= nstop / n <= STOP_MAX): return False", "+    if ndigit / n > DIGIT_MAX:                  return False", "+    if nalpha / n < ALPHA_MIN:                  return False", "+    if len(set(ws)) / n > TTR_MAX:              return False   # too little repetition", "+    return True", "+", " ids, offsets, all_b, wlens = [], [0], [], []", " seen = set()", " for line in open(POOL):"]}, {"oldStart": 106, "oldLines": 6, "newStart": 122, "newLines": 8, "lines": ["     if h in seen:", "         continue", "     seen.add(h)", "+    if not prose_quality(ws):", "+        continue", "     b = doc_buckets(ws)", "     ids.append(r[\"id\"]); all_b.append(b); offsets.append(offsets[-1] + len(b))", "     wlens.append(len(ws))"]}], "userModified": false, "replaceAll": false}

decoding dev target ...
reading + hashing pool ...
  kept 138388 docs after dedup
scoring docs ...
wrote 50000 ids -> /workspace/submission/selection.json
score range kept: 0.385 .. -0.344

real	1m17.551s
user	1m12.900s
sys	0m1.500s

[stdout]
decoding dev target ...
reading + hashing pool ...
  kept 138388 docs after dedup
scoring docs ...
wrote 50000 ids -> /workspace/submission/selection.json
score range kept: 0.385 .. -0.344

real	1m17.551s
user	1m12.900s
sys	0m1.500s
[metadata] {"noOutputExpected": false}

=== TOP1 id 114704 ===
'Attendees | All Canada Games\nRegister Here\nAccommodations\nSelect Page\nRecruits Attending\n<br /><br />\nRecruits will receive a link after registration to get listed as an attendee. Recruits with an existing ConnectLAX p' 

=== TOP2 id 27085 ===
'<|endoftext|>News on : Jagan Mohan\nThe Atmakur Civil Judge on Tuesday sent TDP MLA Erra Shekhar to 14-day judicial custody in connection\nTDP MLA from Jadcherla Erra Shekhar surrendered before the Mahbunagar District SP here on Mon' 

=== TOP3 id 78628 ===
'In the Name of Allah, The Most Gracious, Ever Merciful.\nLove for All, Hatred for None.\nHuzoor Anwar at Nikah Ceremony\nHazoor at Nikah ceremony\nHuzoor at Nikah Ceremony\nNawab Mansoor Ahmed khan sb, Naib Ameer Ghana and Mukhtar Ahme' 

=== rank2000 id 113230 ===
'<|endoftext|>RAJA RAM MOHAN ROY\n- REFORMER PAR EXCELLENCE\nRaja Ram Mohan Roy, one of the great\nreformers of renaissance India has commanded respect to the point\nof veneration and has been acclaimed as a versatile presence on\nthe I' 

=== rank8000 id 93252 ===
'You are here\nTrobadors: A Symposium on Occitan Poetry\nwith Jakes Aymonino, Ariane Daguin, Pierre Joris, Deborah Kapchan, Sarah Kay, Domenja Lekuona, Nicole Peyrafitte, Richard Sieburth, Alem Surre-Garcia, and Joan Francés Tisnèr\nD' 

=== rank11000 id 109681 ===
'otton Eyelet Fabric, Woven Solid Cropped Top And Smocked On The Front And Back With Puff Sleeve\nFabric 100% Cotton\nDreamy boho eyelet fabric makes our cotton Eyelet Crop Top a whimsical wonder. Cropped smocked blouse in light blue'

[stdout]
=== TOP1 id 114704 ===
'Attendees | All Canada Games\nRegister Here\nAccommodations\nSelect Page\nRecruits Attending\n&lt;br /&gt;&lt;br /&gt;\nRecruits will receive a link after registration to get listed as an attendee. Recruits with an existing ConnectLAX p' 

=== TOP2 id 27085 ===
'<|endoftext|>News on : Jagan Mohan\nThe Atmakur Civil Judge on Tuesday sent TDP MLA Erra Shekhar to 14-day judicial custody in connection\nTDP MLA from Jadcherla Erra Shekhar surrendered before the Mahbunagar District SP here on Mon' 

=== TOP3 id 78628 ===
'In the Name of Allah, The Most Gracious, Ever Merciful.\nLove for All, Hatred for None.\nHuzoor Anwar at Nikah Ceremony\nHazoor at Nikah ceremony\nHuzoor at Nikah Ceremony\nNawab Mansoor Ahmed khan sb, Naib Ameer Ghana and Mukhtar Ahme' 

=== rank2000 id 113230 ===
'<|endoftext|>RAJA RAM MOHAN ROY\n- REFORMER PAR EXCELLENCE\nRaja Ram Mohan Roy, one of the great\nreformers of renaissance India has commanded respect to the point\nof veneration and has been acclaimed as a versatile presence on\nthe I' 

=== rank8000 id 93252 ===
'You are here\nTrobadors: A Symposium on Occitan Poetry\nwith Jakes Aymonino, Ariane Daguin, Pierre Joris, Deborah Kapchan, Sarah Kay, Domenja Lekuona, Nicole Peyrafitte, Richard Sieburth, Alem Surre-Garcia, and Joan Francés Tisnèr\nD' 

=== rank11000 id 109681 ===
'otton Eyelet Fabric, Woven Solid Cropped Top And Smocked On The Front And Back With Puff Sleeve\nFabric 100% Cotton\nDreamy boho eyelet fabric makes our cotton Eyelet Crop Top a whimsical wonder. Cropped smocked blouse in light blue'
[metadata] {"noOutputExpected": false}

random baseline selection written 40000

[stdout]
random baseline selection written 40000
[metadata] {"noOutputExpected": false}

=== MY SELECTION ===
Token indices sequence length is longer than the specified maximum sequence length for this model (2170 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 50000 ids -> /tmp/dev_train.npy
HELDOUT_PPL 315.382  loss 5.7538  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)

real	1m57.587s
user	1m51.650s
sys	0m3.140s

[stdout]
=== MY SELECTION ===
Token indices sequence length is longer than the specified maximum sequence length for this model (2170 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 50000 ids -> /tmp/dev_train.npy
HELDOUT_PPL 315.382  loss 5.7538  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)

real	1m57.587s
user	1m51.650s
sys	0m3.140s
[metadata] {"noOutputExpected": false}

=== RANDOM BASELINE ===
packed 12000000 tokens from 40000 ids -> /tmp/dev_train.npy
HELDOUT_PPL 457.994  loss 6.1269  train_s 56  /tmp/dev_train.npy

[stdout]
=== RANDOM BASELINE ===
packed 12000000 tokens from 40000 ids -> /tmp/dev_train.npy
HELDOUT_PPL 457.994  loss 6.1269  train_s 56  /tmp/dev_train.npy
[metadata] {"noOutputExpected": false}

top15k with html/QA markers: 1141  code-ish: 629 of 15000
median words top docs 276.0

[stdout]
top15k with html/QA markers: 1141  code-ish: 629 of 15000
median words top docs 276.0
[metadata] {"noOutputExpected": false}

pool docs with QA/code markers: 3604 code: 2948 of 182016

[stdout]
pool docs with QA/code markers: 3604 code: 2948 of 182016
[metadata] {"noOutputExpected": false}

patched
  kept 152608 docs after dedup
scoring docs ...
wrote 50000 ids -> /tmp/sel2.json
score range kept: 0.377 .. -0.326

real	1m22.563s
user	1m17.240s
sys	0m1.790s

[stdout]
patched
  kept 152608 docs after dedup
scoring docs ...
wrote 50000 ids -> /tmp/sel2.json
score range kept: 0.377 .. -0.326

real	1m22.563s
user	1m17.240s
sys	0m1.790s
[metadata] {"noOutputExpected": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bik854v5v). Output is being written to: /tmp/claude-0/-workspace/0f98a815-3e2d-46b4-89e2-accd4d6daa8e/tasks/bik854v5v.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bik854v5v", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

variant top15k QA/code markers: 301 of 15000
packed 12000000 tokens from 50000 ids -> /tmp/dev_train.npy
HELDOUT_PPL 320.528  loss 5.7700  train_s 56  /tmp/dev_train.npy

[stdout]
variant top15k QA/code markers: 301 of 15000
packed 12000000 tokens from 50000 ids -> /tmp/dev_train.npy
HELDOUT_PPL 320.528  loss 5.7700  train_s 56  /tmp/dev_train.npy
[metadata] {"noOutputExpected": false}