secemp9

#!/usr/bin/env python3
"""
43-Parameter MLP for Multiplication — PyTorch Port

Original solution by Stefan Mesken (Code Golf, 2019)
Ported from TensorFlow to PyTorch with identical weights.

A standard neural network architecture with weights that would emerge
from actual training — not hand-crafted mathematical tricks.

secemp9

#!/usr/bin/env python3
"""
3-Parameter MLP for Addition

A minimal neural network with weights that would emerge from actual training.
Empirically verified: all random initializations converge to these exact weights.

Architecture: Input(2) → Dense(1, linear)
Parameters:  3 (weights [1, 1], bias 0)
Max error:   0.0 (exact)

secemp9

#!/usr/bin/env python3
"""
Deep AST-based static analysis of the dockercraft package.

Designed to catch artifacts from parallel agent edits:
- Duplicate/conflicting definitions across files
- Broken import chains after renames
- Orphaned references to moved/deleted symbols
- Inconsistent class hierarchies
- self.attr usage for attrs never set in __init__

secemp9

#!/usr/bin/env python3
"""
General-purpose AST-based Python module splitter.

Analyzes any monolithic .py file, builds a full dependency graph with structural
fingerprints, auto-discovers natural clusters, and produces split module files.

Capabilities:
  - AST parsing with full symbol table (ast + inspect-style signatures)
  - Structural hashing (hashlib) to fingerprint each symbol for drift detection

secemp9

<LLM_JUDGE_SPEC version="1.0" name="AntiLLMY" schema="1">
  <mission>Score a passage for LLM-y speak (“slop”), using only the given text. Return a compact diagnosis plus concrete fixes.</mission>

  <!-- ===== Regex library (mechanically checkable signs) ===== -->
  <regex_library flags="i">
    <!-- Tone / puffery / editorializing -->
    <pattern id="puffery_words">\b(stunning|breathtaking|must[- ]?(see|visit)|rich (?:cultural )?heritage|enduring(?:\s+legacy)?|nestled|in the heart of|watershed moment|stands as|serves as|is a testament|plays a (?:vital|significant) role|continues to captivate|solidifies)\b</pattern>
    <pattern id="editorialize">\b(it'?s (?:important|worth) (?:to note|noting)|no discussion would be complete|this (?:article|section) (?:wouldn'?t|would not) exist without)\b</pattern>
    <pattern id="weasel">\b(some (?:critics|observers|commentators) (?:argue|say|believe)|many (?:believe|say)|industry (?:reports|analysts) (?:suggest|say))\b</pattern>
    <pattern id="superficial_ing">\b(?:ens

secemp9

import requests
import json
import time
import copy
from enum import Enum
from typing import Dict, List, Any, Optional, Union, Iterator
from dataclasses import dataclass, field

class ModelErrorType(Enum):
    MAX_LENGTH = "1024"

secemp9

import requests
import json
import os
import subprocess
import time
from typing import Dict, List, Any, Optional
import uuid
import shutil

class ToolCallingTester:

secemp9

<output_sequence function="x(i)=2(i)+2" i_start="0" i_end="10000" count="10001">
2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 372, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 396, 398, 4

secemp9

import torch, torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
from PIL import Image

REVISION = "2025-04-14"              # lock to a known good tag
MODEL_ID = "vikhyatk/moondream2"
device   = "cuda"                    # or "cpu" / bitsandbytes / GGUF, etc.

# 1️⃣  load model + text tokenizer
model = AutoModelForCausalLM.from_pretrained(

secemp9

import torch, torch.nn.functional as F
from transformers import AutoProcessor, LlavaForConditionalGeneration
from PIL import Image

MODEL_ID = "llava-hf/llava-1.5-7b-hf"
device   = "cuda"

model = LlavaForConditionalGeneration.from_pretrained(
            MODEL_ID, torch_dtype=torch.float16, device_map="auto")
processor = AutoProcessor.from_pretrained(MODEL_ID)