claude-opus-4-8

5390
---
total 14
drwxr-xr-x 1 root root  100 Aug  4 04:13 .
drwxr-xr-x 1 root root  160 Aug  4 04:13 ..
drwxr-xr-x 1 root root   60 Aug  4 04:13 .live
drwxr-xr-x 1 root root  100 Aug  4 04:13 .metrics
drwxr-xr-x 1 root root  140 Aug  4 04:13 .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 04:13 ..
-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 04:13 ..

[stdout]
5390
---
total 14
drwxr-xr-x 1 root root  100 Aug  4 04:13 .
drwxr-xr-x 1 root root  160 Aug  4 04:13 ..
drwxr-xr-x 1 root root   60 Aug  4 04:13 .live
drwxr-xr-x 1 root root  100 Aug  4 04:13 .metrics
drwxr-xr-x 1 root root  140 Aug  4 04:13 .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 04:13 ..
-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 04:13 ..
[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}

=== dev npy header ===
shape (1000000,) dtype uint16 min 0 max 50256
=== pool line count ===
182016 data/pool.jsonl
=== first 2 docs (truncated) ===
--- id 0 len_chars 414
'|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 · DAYS: News, Spoilers & Discussion|'
--- id 1 len_chars 3018
'*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'

[stdout]
=== dev npy header ===
shape (1000000,) dtype uint16 min 0 max 50256
=== pool line count ===
182016 data/pool.jsonl
=== first 2 docs (truncated) ===
--- id 0 len_chars 414
'|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 · DAYS: News, Spoilers & Discussion|'
--- id 1 len_chars 3018
'*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'
[metadata] {"noOutputExpected": false}

=== dev @ 0.0 ===
" 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 Ly"

=== dev @ 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 system. Tunnel systems would be shared by oil, water, and gas pipelines; channels for laser and microwave waveguides; electric power lines including superconducting ones; and freight systems. Environmental and economic benefits are substantial, and the technology for building and operating the system exists.\n\nThis report is part of the RAND Corporation paper series. The paper was a product of the RAND Corporation from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of"

=== dev @ 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 does this s**t."In the clip, she was sporting a top which had the word \'Sunday\' emblazoned across the front as she sat in the passenger seat. With his music blaring out, she filmed herself dancing and bopping her head to the music. Then all of a sudden, he made Cyrus jump as he suddenly screamed at her, causing to drop her phone in panic.(With IANS inputs)<|endoftext|>Uttar Pradesh chief minister Yogi Adityanath has maintained that 63 children in government-run Gor'

=== dev @ 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 will break when the next version of windows comes out.</p>\n<hr />\n<p>Note: The answers claiming this is a duplicate question do not actually answer the question <code>is_windows</code>. They answer the question &quot;what platform&quot;. Since many flavors of windows exist none of them comprehensively describe how to get an answer of <code>isWindows</code>.</p>\n\n<p>Python <a href="http://docs.python.org/library/'

[stdout]
=== dev @ 0.0 ===
" 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 Ly"

=== dev @ 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 system. Tunnel systems would be shared by oil, water, and gas pipelines; channels for laser and microwave waveguides; electric power lines including superconducting ones; and freight systems. Environmental and economic benefits are substantial, and the technology for building and operating the system exists.\n\nThis report is part of the RAND Corporation paper series. The paper was a product of the RAND Corporation from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of"

=== dev @ 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 does this s**t."In the clip, she was sporting a top which had the word \'Sunday\' emblazoned across the front as she sat in the passenger seat. With his music blaring out, she filmed herself dancing and bopping her head to the music. Then all of a sudden, he made Cyrus jump as he suddenly screamed at her, causing to drop her phone in panic.(With IANS inputs)<|endoftext|>Uttar Pradesh chief minister Yogi Adityanath has maintained that 63 children in government-run Gor'

=== dev @ 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 will break when the next version of windows comes out.</p>\n<hr />\n<p>Note: The answers claiming this is a duplicate question do not actually answer the question <code>is_windows</code>. They answer the question &quot;what platform&quot;. Since many flavors of windows exist none of them comprehensively describe how to get an answer of <code>isWindows</code>.</p>\n\n<p>Python <a href="http://docs.python.org/library/'
[metadata] {"noOutputExpected": false}

16
               total        used        free      shared  buff/cache   available
Mem:            1024           0        1023           0           0        1023
NVIDIA H100 80GB HBM3, 81559 MiB
4000 docs, 2768019 tokens in 4.61s -> 0.60M tok/s, 867 docs/s
est full pool tokenize: 209.8685484008789 s

[stdout]
16
               total        used        free      shared  buff/cache   available
Mem:            1024           0        1023           0           0        1023
NVIDIA H100 80GB HBM3, 81559 MiB
4000 docs, 2768019 tokens in 4.61s -> 0.60M tok/s, 867 docs/s
est full pool tokenize: 209.8685484008789 s
[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, multi-domain high-quality English mix (Wikipedia + high-quality web prose\n+ news + technical Q&A).\n\nCriterion (stated, reproducible): rank every pool document by a DSIR-style\nunigram log-likelihood ratio in GPT-2 token space:\n\n    score(doc) = (1/n) * sum_i [ log p_target(t_i) - log p_pool(t_i) ]\n\nwhere p_target is the smoothed unigram distribution of the disclosed target\nsample (data/multi_dev.npy) and p_pool is the smoothed unigram distribution of\nthe whole raw pool. Documents whose vocabulary looks MORE like the target than\nlike the average raw-web document score high. This is exactly importance\nresampling toward the disclosed domain: it pulls in clean encyclopedic / news /\ntechnical-prose English and pushes down forum chatter, boilerplate, spam,\nnon-English and symbol/gibberish text (all of which are common in the raw pool\nbut rare in the target).\n\nLight junk/length gates remove degenerate documents that a pure unigram score\ncan be fooled by (too short to carry signal, or highly repetitive).\n\nOutput: submission/selection.json = pool ids in priority order (best first).\n\"\"\"\nimport json, math, sys, time\nimport numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nTARGET_NPY = \"/workspace/data/multi_dev.npy\"\nOUT = \"/workspace/submission/selection.json\"\nCACHE = \"/tmp/pool_tokens.npz\"\n\nVOCAB = 50257\nEOS = 50256          # GPT-2 <|endoftext|> — a separator, excluded from unigram stats\nALPHA = 1.0          # Laplace smoothing on both distributions\nMIN_TOK = 64         # drop documents too short to carry a stable domain signal\nMAX_TOP1_FRAC = 0.35 # drop documents dominated by a single repeated token\nMIN_DISTINCT = 0.18  # drop documents with too few distinct tokens (repetitive)\nBUDGET = 12_000_000  # official training budget; we emit >> this many tokens\n\n\ndef tokenize_pool(tok):\n    \"\"\"Tokenize the whole pool once; cache concatenated ids + offsets to /tmp.\"\"\"\n    try:\n        z = np.load(CACHE)\n        print(\"loaded token cache\", CACHE, flush=True)\n        return z[\"ids\"], z[\"off\"], z[\"pid\"]\n    except Exception:\n        pass\n    ids_parts, lengths, pids = [], [], []\n    buf_txt, buf_id = [], []\n    t0 = time.time()\n\n    def flush():\n        if not buf_txt:\n            return\n        enc = tok(buf_txt, add_special_tokens=False).input_ids\n        for e in enc:\n            ids_parts.append(np.asarray(e, dtype=np.uint16))\n            lengths.append(len(e))\n        pids.extend(buf_id)\n        buf_txt.clear(); buf_id.clear()\n\n    with open(POOL) as f:\n        for n, line in enumerate(f):\n            r = json.loads(line)\n            buf_txt.append(r[\"text\"]); buf_id.append(r[\"id\"])\n            if len(buf_txt) >= 2000:\n                flush()\n                if (n + 1) % 20000 == 0:\n                    print(f\"  tokenized {n+1} docs  {time.time()-t0:.0f}s\", flush=True)\n    flush()\n    ids = np.concatenate(ids_parts) if ids_parts else np.zeros(0, np.uint16)\n    off = np.zeros(len(lengths) + 1, dtype=np.int64)\n    off[1:] = np.cumsum(lengths)\n    pid = np.asarray(pids, dtype=np.int64)\n    np.savez(CACHE, ids=ids, off=off, pid=pid)\n    print(f\"tokenized {len(pid)} docs, {len(ids)} tokens in {time.time()-t0:.0f}s\", flush=True)\n    return ids, off, pid\n\n\ndef main():\n    tok = AutoTokenizer.from_pretrained(\"gpt2\")\n\n    # target unigram distribution (drop EOS separators)\n    tgt = np.load(TARGET_NPY).astype(np.int64)\n    tgt = tgt[tgt != EOS]\n    tc = np.bincount(tgt, minlength=VOCAB).astype(np.float64)\n\n    ids, off, pid = tokenize_pool(tok)\n\n    # pool background unigram distribution (drop EOS)\n    pc = np.bincount(ids.astype(np.int64), minlength=VOCAB).astype(np.float64)\n    pc[EOS] = 0.0\n\n    logp_tgt = np.log((tc + ALPHA) / (tc.sum() + ALPHA * VOCAB))\n    logp_pool = np.log((pc + ALPHA) / (pc.sum() + ALPHA * VOCAB))\n    ratio = (logp_tgt - logp_pool).astype(np.float64)  # per-token log importance weight\n\n    ndoc = len(pid)\n    score = np.full(ndoc, -1e9, dtype=np.float64)\n    ntok = np.zeros(ndoc, dtype=np.int64)\n    t0 = time.time()\n    for i in range(ndoc):\n        a, b = off[i], off[i + 1]\n        d = ids[a:b].astype(np.int64)\n        n = len(d)\n        ntok[i] = n\n        if n < MIN_TOK:\n            continue\n        cnt = np.bincount(d, minlength=1)\n        top1 = cnt.max() / n\n        distinct = (cnt > 0).sum() / n\n        if top1 > MAX_TOP1_FRAC or distinct < MIN_DISTINCT:\n            continue\n        score[i] = ratio[d].mean()\n        if (i + 1) % 40000 == 0:\n            print(f\"  scored {i+1}/{ndoc}  {time.time()-t0:.0f}s\", flush=True)\n\n    order = np.argsort(-score)  # descending\n    order = order[score[order] > -1e8]  # keep only docs that passed gates\n\n    # emit ids in priority order, enough to comfortably exceed the budget\n    sel, cum = [], 0\n    for i in order:\n        sel.append(int(pid[i]))\n        cum += int(ntok[i]) + 1  # +1 for the EOS the packer appends\n        if cum >= 3 * BUDGET and len(sel) >= 4000:\n            break\n    json.dump(sel, open(OUT, \"w\"))\n    kept = int((score > -1e8).sum())\n    print(f\"kept {kept}/{ndoc} docs after gates; emitted {len(sel)} ids \"\n          f\"(~{cum/1e6:.1f}M tokens) -> {OUT}\", flush=True)\n    print(f\"score stats over kept: min {score[score>-1e8].min():.3f} \"\n          f\"max {score.max():.3f} mean {score[score>-1e8].mean():.3f}\", flush=True)\n\n\nif __name__ == \"__main__\":\n    main()\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Token indices sequence length is longer than the specified maximum sequence length for this model (1048 > 1024). Running this sequence through the model will result in indexing errors
  tokenized 20000 docs  7s
  tokenized 40000 docs  14s
  tokenized 60000 docs  21s
  tokenized 80000 docs  28s
  tokenized 100000 docs  35s
  tokenized 120000 docs  46s
  tokenized 140000 docs  62s
  tokenized 160000 docs  78s
  tokenized 180000 docs  94s
tokenized 182016 docs, 199911772 tokens in 96s
  scored 40000/182016  4s
  scored 80000/182016  7s
  scored 120000/182016  10s
  scored 160000/182016  13s
kept 176223/182016 docs after gates; emitted 43680 ids (~36.0M tokens) -> /workspace/submission/selection.json
score stats over kept: min -3.014 max 0.711 mean -0.327

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (1048 > 1024). Running this sequence through the model will result in indexing errors
  tokenized 20000 docs  7s
  tokenized 40000 docs  14s
  tokenized 60000 docs  21s
  tokenized 80000 docs  28s
  tokenized 100000 docs  35s
  tokenized 120000 docs  46s
  tokenized 140000 docs  62s
  tokenized 160000 docs  78s
  tokenized 180000 docs  94s
tokenized 182016 docs, 199911772 tokens in 96s
  scored 40000/182016  4s
  scored 80000/182016  7s
  scored 120000/182016  10s
  scored 160000/182016  13s
kept 176223/182016 docs after gates; emitted 43680 ids (~36.0M tokens) -> /workspace/submission/selection.json
score stats over kept: min -3.014 max 0.711 mean -0.327
[metadata] {"noOutputExpected": false}

=== TOP 3 (highest priority) ===
--- id 105413 len 591
' 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 at Inkster High School . . . Coached by Greg Carter . . . Netted 115 total tackles with 85'

--- id 118102 len 188
'<|endoftext|>Index of /buildlogs/build-3-9-x86_64/main/wget/\nIndex of /buildlogs/build-3-9-x86_64/main/wget/\n../\nwget-1.20.3-r0.log                                 08-Apr-2019 10:10     49'

--- id 140758 len 188
'<|endoftext|>Index of /buildlogs/build-3-9-x86_64/main/wget/\nIndex of /buildlogs/build-3-9-x86_64/main/wget/\n../\nwget-1.20.3-r0.log                                 08-Apr-2019 10:10     49'

=== AROUND RANK 20000 ===
--- id 46992
" two ladies I drew for the latest design assignment. I took my time and used a 0.03 fineliner for the face details and outlines and pencils and markers for the rest. It's funny because when I started the classes I told myself I couldn't draw and especially not faces. The markers made a world of difference. I think I was scared to use them previousl"

=== LAST emitted (near budget tail) ===
--- id 81507
' Noble<|endoftext|>Builder Lead Converter: How A Home Builder Can Grow Revenue Without Ads\nPrior Lake, MN based Builder Lead Converter is reaching out to show their community how home builders can reach more customers without traditional advertising. As the internet continues its rapid expansion, mo'

--- id 9342
'MP Bank has taken the unusual step of offering to help its customers find other banks if they choose to take their business elsewhere.\nMP Bank has decided to start charging its lowest earning customers ISK 5,000 a month (EUR 30/USD 44) to operate their accounts, in what the company heralds as a chan'

[stdout]
=== TOP 3 (highest priority) ===
--- id 105413 len 591
' 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 at Inkster High School . . . Coached by Greg Carter . . . Netted 115 total tackles with 85'

--- id 118102 len 188
'<|endoftext|>Index of /buildlogs/build-3-9-x86_64/main/wget/\nIndex of /buildlogs/build-3-9-x86_64/main/wget/\n../\nwget-1.20.3-r0.log                                 08-Apr-2019 10:10     49'

--- id 140758 len 188
'<|endoftext|>Index of /buildlogs/build-3-9-x86_64/main/wget/\nIndex of /buildlogs/build-3-9-x86_64/main/wget/\n../\nwget-1.20.3-r0.log                                 08-Apr-2019 10:10     49'

=== AROUND RANK 20000 ===
--- id 46992
" two ladies I drew for the latest design assignment. I took my time and used a 0.03 fineliner for the face details and outlines and pencils and markers for the rest. It's funny because when I started the classes I told myself I couldn't draw and especially not faces. The markers made a world of difference. I think I was scared to use them previousl"

=== LAST emitted (near budget tail) ===
--- id 81507
' Noble<|endoftext|>Builder Lead Converter: How A Home Builder Can Grow Revenue Without Ads\nPrior Lake, MN based Builder Lead Converter is reaching out to show their community how home builders can reach more customers without traditional advertising. As the internet continues its rapid expansion, mo'

--- id 9342
'MP Bank has taken the unusual step of offering to help its customers find other banks if they choose to take their business elsewhere.\nMP Bank has decided to start charging its lowest earning customers ISK 5,000 a month (EUR 30/USD 44) to operate their accounts, in what the company heralds as a chan'
[metadata] {"noOutputExpected": false}

4980

[stdout]
4980
[metadata] {"noOutputExpected": false}

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

[metadata] {"type": "create", "filePath": "/workspace/exp.py", "content": "\"\"\"Experiment harness: build several candidate selections from the cached pool\ntokens and target unigram stats, write them to /tmp/sel_<mode>.json for dev_eval.\"\"\"\nimport json, sys, numpy as np\n\nVOCAB=50257; EOS=50256; ALPHA=1.0; BUDGET=12_000_000\nmode = sys.argv[1]\nz=np.load(\"/tmp/pool_tokens.npz\"); ids=z[\"ids\"]; off=z[\"off\"]; pid=z[\"pid\"]\ntgt=np.load(\"/workspace/data/multi_dev.npy\").astype(np.int64); tgt=tgt[tgt!=EOS]\ntc=np.bincount(tgt,minlength=VOCAB).astype(np.float64)\npc=np.bincount(ids.astype(np.int64),minlength=VOCAB).astype(np.float64); pc[EOS]=0.0\nlogp_tgt=np.log((tc+ALPHA)/(tc.sum()+ALPHA*VOCAB))\nlogp_pool=np.log((pc+ALPHA)/(pc.sum()+ALPHA*VOCAB))\nratio=(logp_tgt-logp_pool)\nndoc=len(pid)\n\ndef gates(d):\n    n=len(d)\n    if n<MIN_TOK: return False\n    cnt=np.bincount(d,minlength=1)\n    if cnt.max()/n>0.35: return False\n    if (cnt>0).sum()/n<0.18: return False\n    return True\n\nMIN_TOK=128\nif mode==\"random\":\n    rng=np.random.default_rng(0)\n    order=rng.permutation(ndoc)\n    sel=[]; cum=0\n    for i in order:\n        n=off[i+1]-off[i]\n        sel.append(int(pid[i])); cum+=int(n)+1\n        if cum>=3*BUDGET and len(sel)>=4000: break\n    json.dump(sel,open(\"/tmp/sel_random.json\",\"w\")); print(\"random\",len(sel),cum); sys.exit()\n\n# scored modes with exact dedup\nscore=np.full(ndoc,-1e9); ntok=np.zeros(ndoc,np.int64); seen=set()\nfor i in range(ndoc):\n    a,b=off[i],off[i+1]; d=ids[a:b].astype(np.int64); n=len(d); ntok[i]=n\n    if not gates(d): continue\n    h=hash(ids[a:b].tobytes())\n    if h in seen: continue\n    seen.add(h)\n    if mode==\"ratio\":\n        score[i]=ratio[d].mean()\n    elif mode==\"tgtlike\":\n        score[i]=logp_tgt[d].mean()\n    elif mode==\"blend\":\n        score[i]=logp_tgt[d].mean()+0.5*np.clip(ratio[d],-4,4).mean()\norder=np.argsort(-score); order=order[score[order]>-1e8]\nsel=[]; cum=0\nfor i in order:\n    sel.append(int(pid[i])); cum+=int(ntok[i])+1\n    if cum>=3*BUDGET and len(sel)>=4000: break\njson.dump(sel,open(f\"/tmp/sel_{mode}.json\",\"w\"))\nprint(mode,\"kept\",len(order),\"emitted\",len(sel),\"~%.1fM tok\"%(cum/1e6))\n", "structuredPatch": [], "originalFile": null, "userModified": false}

random 32448 36001163
ratio kept 151775 emitted 41610 ~36.0M tok
blend kept 151775 emitted 39721 ~36.0M tok
tgtlike kept 151775 emitted 39453 ~36.0M tok

[stdout]
random 32448 36001163
ratio kept 151775 emitted 41610 ~36.0M tok
blend kept 151775 emitted 39721 ~36.0M tok
tgtlike kept 151775 emitted 39453 ~36.0M tok
[metadata] {"noOutputExpected": false}

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

[metadata] {"type": "create", "filePath": "/workspace/fastpack.py", "content": "\"\"\"Fast dev packer: build the 12M-token train array from cached pool tokens,\nreplicating pack_selection.py exactly (doc ids + EOS per doc, truncate to BUDGET).\"\"\"\nimport json, sys, numpy as np\nBUDGET=12_000_000; EOS=50256\nsel=json.load(open(sys.argv[1])); out=sys.argv[2]\nz=np.load(\"/tmp/pool_tokens.npz\"); ids=z[\"ids\"]; off=z[\"off\"]; pid=z[\"pid\"]\npos={int(p):i for i,p in enumerate(pid)}\nparts=[]; tot=0\nfor s in sel:\n    i=pos[int(s)]; d=ids[off[i]:off[i+1]]\n    parts.append(d); parts.append(np.array([EOS],dtype=np.uint16)); tot+=len(d)+1\n    if tot>=BUDGET: break\narr=np.concatenate(parts)[:BUDGET].astype(np.uint16)\nnp.save(out,arr); print(\"packed\",len(arr),\"tokens from\",len(sel),\"ids ->\",out)\n", "structuredPatch": [], "originalFile": null, "userModified": false}

packed 12000000 tokens from 41610 ids -> /tmp/tr_ratio.npy
ratio: HELDOUT_PPL 406.595  loss 6.0078  train_s 54  /tmp/tr_ratio.npy
packed 12000000 tokens from 39721 ids -> /tmp/tr_blend.npy
blend: HELDOUT_PPL 567.434  loss 6.3411  train_s 54  /tmp/tr_blend.npy
packed 12000000 tokens from 39453 ids -> /tmp/tr_tgtlike.npy
tgtlike: HELDOUT_PPL 618.265  loss 6.4269  train_s 54  /tmp/tr_tgtlike.npy

[stdout]
packed 12000000 tokens from 41610 ids -> /tmp/tr_ratio.npy
ratio: HELDOUT_PPL 406.595  loss 6.0078  train_s 54  /tmp/tr_ratio.npy
packed 12000000 tokens from 39721 ids -> /tmp/tr_blend.npy
blend: HELDOUT_PPL 567.434  loss 6.3411  train_s 54  /tmp/tr_blend.npy
packed 12000000 tokens from 39453 ids -> /tmp/tr_tgtlike.npy
tgtlike: HELDOUT_PPL 618.265  loss 6.4269  train_s 54  /tmp/tr_tgtlike.npy
[metadata] {"noOutputExpected": false}

File created successfully at: /workspace/exp2.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/exp2.py", "content": "import json, sys, time, numpy as np\nVOCAB=50257; EOS=50256; ALPHA=1.0; BUDGET=12_000_000; MIN_TOK=128\nmode=sys.argv[1]\nz=np.load(\"/tmp/pool_tokens.npz\"); ids=z[\"ids\"]; off=z[\"off\"]; pid=z[\"pid\"]\ntgt=np.load(\"/workspace/data/multi_dev.npy\").astype(np.int64)\ntgtu=tgt[tgt!=EOS]\ntc=np.bincount(tgtu,minlength=VOCAB).astype(np.float64)\npc=np.bincount(ids.astype(np.int64),minlength=VOCAB).astype(np.float64); pc[EOS]=0.0\nlogp_tgt=np.log((tc+ALPHA)/(tc.sum()+ALPHA*VOCAB))\nlogp_pool=np.log((pc+ALPHA)/(pc.sum()+ALPHA*VOCAB))\nratio=(logp_tgt-logp_pool)\nndoc=len(pid)\ndef gate_ok(d):\n    n=len(d)\n    if n<MIN_TOK: return False\n    cnt=np.bincount(d,minlength=1)\n    if cnt.max()/n>0.35: return False\n    if (cnt>0).sum()/n<0.18: return False\n    return True\n\nscore=np.full(ndoc,-1e9); ntok=np.zeros(ndoc,np.int64)\n\nif mode==\"bigram\":\n    t0=time.time()\n    # target bigram joint counts (drop bigrams touching EOS)\n    a=tgt[:-1]; b=tgt[1:]; m=(a!=EOS)&(b!=EOS)\n    tb=(a[m].astype(np.int64)*VOCAB+b[m].astype(np.int64))\n    ut,ct=np.unique(tb,return_counts=True); Nt=ct.sum()\n    # pool bigram joint counts (concat cache; negligible cross-doc boundary noise)\n    pa=ids[:-1].astype(np.int64); pb=ids[1:].astype(np.int64)\n    pbi=pa*VOCAB+pb\n    up,cp=np.unique(pbi,return_counts=True); Np=cp.sum()\n    print(\"bigram tables built\",time.time()-t0,\"Nt\",Nt,\"Np\",Np,\"uniq_t\",len(ut),\"uniq_p\",len(up),flush=True)\n    floor_t=0.1/Nt; floor_p=0.1/Np\n    lt_floor=np.log(floor_t); lp_floor=np.log(floor_p)\n    for i in range(ndoc):\n        s,e=off[i],off[i+1]; d=ids[s:e].astype(np.int64); ntok[i]=len(d)\n        if not gate_ok(d): continue\n        db=d[:-1]*VOCAB+d[1:]\n        # target counts via searchsorted\n        j=np.searchsorted(ut,db); j=np.clip(j,0,len(ut)-1)\n        hit=ut[j]==db; ptc=np.where(hit,ct[j],0)\n        ltp=np.where(ptc>0,np.log(np.maximum(ptc,1)/Nt),lt_floor)\n        # pool counts\n        k=np.searchsorted(up,db); k=np.clip(k,0,len(up)-1)\n        hitp=up[k]==db; ppc=np.where(hitp,cp[k],0)\n        lpp=np.where(ppc>0,np.log(np.maximum(ppc,1)/Np),lp_floor)\n        score[i]=(ltp-lpp).mean()\n        if (i+1)%40000==0: print(\"  scored\",i+1,time.time()-t0,flush=True)\nelse:\n    for i in range(ndoc):\n        s,e=off[i],off[i+1]; d=ids[s:e].astype(np.int64); ntok[i]=len(d)\n        if not gate_ok(d): continue\n        if mode==\"ratio_clip\":\n            score[i]=np.clip(ratio[d],-3,3).mean()\n\norder=np.argsort(-score); order=order[score[order]>-1e8]\nsel=[]; cum=0\nfor i in order:\n    sel.append(int(pid[i])); cum+=int(ntok[i])+1\n    if cum>=3*BUDGET and len(sel)>=4000: break\njson.dump(sel,open(f\"/tmp/sel_{mode}.json\",\"w\"))\nprint(mode,\"emitted\",len(sel),\"~%.1fM tok\"%(cum/1e6),flush=True)\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bua5zc111). Output is being written to: /tmp/claude-0/-workspace/f24e2183-2de4-4e23-9c97-3b30167f33a7/tasks/bua5zc111.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": "bua5zc111", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

ratio_clip emitted 40879 ~36.0M tok
bigram tables built 8.128766059875488 Nt 994591 Np 199911771 uniq_t 377314 uniq_p 15549223
  scored 40000 35.57090759277344
  scored 80000 60.10585427284241
  scored 120000 87.35616278648376
---time---
4309

[stdout]
ratio_clip emitted 40879 ~36.0M tok
bigram tables built 8.128766059875488 Nt 994591 Np 199911771 uniq_t 377314 uniq_p 15549223
  scored 40000 35.57090759277344
  scored 80000 60.10585427284241
  scored 120000 87.35616278648376
---time---
4309
[metadata] {"noOutputExpected": false}

ratio_clip emitted 40879 ~36.0M tok
  scored 160000 125.70444679260254
bigram emitted 41628 ~36.0M tok

[stdout]
ratio_clip emitted 40879 ~36.0M tok
  scored 160000 125.70444679260254
bigram emitted 41628 ~36.0M tok
[metadata] {"noOutputExpected": false}

ratio_clip: HELDOUT_PPL 407.042  loss 6.0089  train_s 54  /tmp/tr_ratio_clip.npy
bigram: HELDOUT_PPL 339.799  loss 5.8284  train_s 54  /tmp/tr_bigram.npy

[stdout]
ratio_clip: HELDOUT_PPL 407.042  loss 6.0089  train_s 54  /tmp/tr_ratio_clip.npy
bigram: HELDOUT_PPL 339.799  loss 5.8284  train_s 54  /tmp/tr_bigram.npy
[metadata] {"noOutputExpected": false}

total emitted 41628
--- rank 0 id 102159 chars 1273
"1 page matching The electrical engineer 1888 by two experimenters, Nikola Tesla and Galileo Ferraris in this book\nWhat people are saying - Write a review\nWe haven't found any reviews in the usual places.\nOther editions - View all\nalternating current amperes apparatus application "
--- rank 1 id 97830 chars 1325
" in this book\nResults 1-3 of 27\nWhat people are saying - Write a review\nWe haven't found any reviews in the usual places.\nFOREWORD by Sir Herbert Read\n10 other sections not shown\nOther editions - View all\nabsolute absurdist accept action affirmation artist aspect atheism Bakunin "
--- rank 2 id 167992 chars 69952
'\nScroll back to top<|endoftext|>What Was Pebble Mill? | Touch of Eastern Promise\nWhat Was Pebble Mill?The stories from Pebble Mill, the BBC studios in the West Midlands\nSearch:\nHome\nBlog\nVideos\nPhotos\nAbout\nBuilding\nMemories\nPhotographs\nVideo\nTelevision\nTransmission Lists\nDrama\nF'
--- rank 3 id 36051 chars 1633
'<|endoftext|>monsters inc t shirt as skirt of generation that eider down takes) of the leg ministry is gotten , O word model , the freeboard skill business can appear do not the stripe that benefit other coloring of Hermes(love horse) series : The water to is the place and tiger '
--- rank 50 id 6827 chars 8026
'Slippers were hurled at RJD leader Tejashwi Yadav during a poll rally in Bihar’s Aurangabad. The leader was sitting with party workers when the incident happened. Two slippers were hurled at the leader. One of the slipper missed Yadav while the second one landed in his lap. Yadav'
--- rank 500 id 92766 chars 4024
'<|endoftext|>While working with Silverlight applications, we may face some scenarios where we may need to embed Silverlight as a component, like for e.g in Sharepoint Webpars or simple we can have the same with ASP.NET. The biggest challenge comes when we have to pass the paramet'
--- rank 5000 id 53980 chars 6005
'Three hundred million yen and Yakuza boss Anjo have disappeared without a trace. Anjo’s most deadly and most sadomasochistic enforcer, Kakihara, and the rest of the Yakuza gang embark on a torture-riddle search and rescue to find their missing boss. After unrightfully torturing a'

[stdout]
total emitted 41628
--- rank 0 id 102159 chars 1273
"1 page matching The electrical engineer 1888 by two experimenters, Nikola Tesla and Galileo Ferraris in this book\nWhat people are saying - Write a review\nWe haven't found any reviews in the usual places.\nOther editions - View all\nalternating current amperes apparatus application "
--- rank 1 id 97830 chars 1325
" in this book\nResults 1-3 of 27\nWhat people are saying - Write a review\nWe haven't found any reviews in the usual places.\nFOREWORD by Sir Herbert Read\n10 other sections not shown\nOther editions - View all\nabsolute absurdist accept action affirmation artist aspect atheism Bakunin "
--- rank 2 id 167992 chars 69952
'\nScroll back to top<|endoftext|>What Was Pebble Mill? | Touch of Eastern Promise\nWhat Was Pebble Mill?The stories from Pebble Mill, the BBC studios in the West Midlands\nSearch:\nHome\nBlog\nVideos\nPhotos\nAbout\nBuilding\nMemories\nPhotographs\nVideo\nTelevision\nTransmission Lists\nDrama\nF'
--- rank 3 id 36051 chars 1633
'<|endoftext|>monsters inc t shirt as skirt of generation that eider down takes) of the leg ministry is gotten , O word model , the freeboard skill business can appear do not the stripe that benefit other coloring of Hermes(love horse) series : The water to is the place and tiger '
--- rank 50 id 6827 chars 8026
'Slippers were hurled at RJD leader Tejashwi Yadav during a poll rally in Bihar’s Aurangabad. The leader was sitting with party workers when the incident happened. Two slippers were hurled at the leader. One of the slipper missed Yadav while the second one landed in his lap. Yadav'
--- rank 500 id 92766 chars 4024
'<|endoftext|>While working with Silverlight applications, we may face some scenarios where we may need to embed Silverlight as a component, like for e.g in Sharepoint Webpars or simple we can have the same with ASP.NET. The biggest challenge comes when we have to pass the paramet'
--- rank 5000 id 53980 chars 6005
'Three hundred million yen and Yakuza boss Anjo have disappeared without a trace. Anjo’s most deadly and most sadomasochistic enforcer, Kakihara, and the rest of the Yakuza gang embark on a torture-riddle search and rescue to find their missing boss. After unrightfully torturing a'
[metadata] {"noOutputExpected": false}

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

[metadata] {"type": "create", "filePath": "/workspace/scorer.py", "content": "\"\"\"Parametrized selector: scorer.py <target_npy> <mode> <out_json>\nmode in {ratio, bigram}. Builds the domain model from <target_npy> only.\"\"\"\nimport json, sys, numpy as np\nVOCAB=50257; EOS=50256; ALPHA=1.0; BUDGET=12_000_000; MIN_TOK=128\ntarget_npy, mode, out = sys.argv[1], sys.argv[2], sys.argv[3]\nz=np.load(\"/tmp/pool_tokens.npz\"); ids=z[\"ids\"]; off=z[\"off\"]; pid=z[\"pid\"]\ntgt=np.load(target_npy).astype(np.int64)\ntgtu=tgt[tgt!=EOS]\ntc=np.bincount(tgtu,minlength=VOCAB).astype(np.float64)\npc=np.bincount(ids.astype(np.int64),minlength=VOCAB).astype(np.float64); pc[EOS]=0.0\nlogp_tgt=np.log((tc+ALPHA)/(tc.sum()+ALPHA*VOCAB))\nlogp_pool=np.log((pc+ALPHA)/(pc.sum()+ALPHA*VOCAB))\nratio=(logp_tgt-logp_pool)\nndoc=len(pid)\ndef gate_ok(d):\n    n=len(d)\n    if n<MIN_TOK: return False\n    cnt=np.bincount(d,minlength=1)\n    if cnt.max()/n>0.35: return False\n    if (cnt>0).sum()/n<0.18: return False\n    return True\nscore=np.full(ndoc,-1e9); ntok=np.zeros(ndoc,np.int64)\nif mode==\"bigram\":\n    a=tgt[:-1]; b=tgt[1:]; m=(a!=EOS)&(b!=EOS)\n    tb=(a[m].astype(np.int64)*VOCAB+b[m].astype(np.int64))\n    ut,ct=np.unique(tb,return_counts=True); Nt=ct.sum()\n    pa=ids[:-1].astype(np.int64); pb=ids[1:].astype(np.int64)\n    up,cp=np.unique(pa*VOCAB+pb,return_counts=True); Np=cp.sum()\n    lt_floor=np.log(0.1/Nt); lp_floor=np.log(0.1/Np)\n    for i in range(ndoc):\n        s,e=off[i],off[i+1]; d=ids[s:e].astype(np.int64); ntok[i]=len(d)\n        if not gate_ok(d): continue\n        db=d[:-1]*VOCAB+d[1:]\n        j=np.clip(np.searchsorted(ut,db),0,len(ut)-1); ptc=np.where(ut[j]==db,ct[j],0)\n        ltp=np.where(ptc>0,np.log(np.maximum(ptc,1)/Nt),lt_floor)\n        k=np.clip(np.searchsorted(up,db),0,len(up)-1); ppc=np.where(up[k]==db,cp[k],0)\n        lpp=np.where(ppc>0,np.log(np.maximum(ppc,1)/Np),lp_floor)\n        score[i]=(ltp-lpp).mean()\nelse:\n    for i in range(ndoc):\n        s,e=off[i],off[i+1]; d=ids[s:e].astype(np.int64); ntok[i]=len(d)\n        if not gate_ok(d): continue\n        score[i]=ratio[d].mean()\norder=np.argsort(-score); order=order[score[order]>-1e8]\nsel=[]; cum=0\nfor i in order:\n    sel.append(int(pid[i])); cum+=int(ntok[i])+1\n    if cum>=3*BUDGET and len(sel)>=4000: break\njson.dump(sel,open(out,\"w\")); print(mode,target_npy,\"emitted\",len(sel))\n", "structuredPatch": [], "originalFile": null, "userModified": false}

started pid 2504

[stdout]
started pid 2504
[metadata] {"noOutputExpected": false}

uniA->B: HELDOUT_PPL 939.814  loss 6.8457  train_s 54  /tmp/trA_ratio.npy
random->B: HELDOUT_PPL 403.053  loss 5.9991  train_s 54  /tmp/tr_rnd.npy

[stdout]
uniA->B: HELDOUT_PPL 939.814  loss 6.8457  train_s 54  /tmp/trA_ratio.npy
random->B: HELDOUT_PPL 403.053  loss 5.9991  train_s 54  /tmp/tr_rnd.npy
[metadata] {"noOutputExpected": false}

ilA 500000 ilB 500000
time:
3758

[stdout]
ilA 500000 ilB 500000
time:
3758
[metadata] {"noOutputExpected": false}