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going forward!

Nordine Lotfi secemp9

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going forward!
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secemp9 / anti_slop_rubric.xml
Created September 15, 2025 21:25
<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
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secemp9 / openai_toolcall.py
Created July 23, 2025 00:17
openai toolcalling test
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"
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secemp9 / openrouter_toolcall.py
Created July 22, 2025 02:29
openrouter tool calling verify
import requests
import json
import os
import subprocess
import time
from typing import Dict, List, Any, Optional
import uuid
import shutil
class ToolCallingTester:
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secemp9 / gist:837164556b0f0631297ea1a554755a86
Created June 10, 2025 07:49
<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
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secemp9 / vlm_token_vis_moondream.py
Created May 31, 2025 20:53
attempt at reproducing https://www.groundlight.ai/blog/how-vlm-works-tokens 's interactive vlm demo to visualize tokens on their patch/predicted region on an image
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(
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secemp9 / vlm_token_vis_llava.py
Created May 31, 2025 20:52
attempt at reproducing https://www.groundlight.ai/blog/how-vlm-works-tokens 's interactive vlm demo to visualize tokens on their patch/predicted region on an image
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)
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secemp9 / rotation_invariant_cnn.py
Created May 31, 2025 04:21
rotation invariant CNN implementation
# -*- coding: utf-8 -*-
"""
CyCNN implementation for rotation invariant image classification.
Based on "CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers"
"""
# Core Libraries
import os
import random
import time
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secemp9 / testchoice.html
Created May 20, 2025 01:16
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Chips Filter Demo</title>
<style>
body {
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secemp9 / autocolor_ps.py
Created May 6, 2025 23:54
autocolor (photoshop) inspired algorithm
def improved_auto_color(image: Image.Image) -> Image.Image:
"""
Improved version of Photoshop-like Auto Color.
It works by:
1. Converting to float and normalizing.
2. Computing per-channel percentiles (e.g., 0.5% and 99.5%).
3. Stretching values between those percentiles to [0, 255].
4. This avoids over-amplifying noise or outliers.
"""
img_array = np.array(image).astype(np.float32)
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secemp9 / gemini_cli.py
Created May 4, 2025 22:03
woa gemini
#!/usr/bin/env python3
import sys
import os
from google import genai
from google.genai import types
from PIL import Image
import readline # For command history
def print_usage():
"""Print usage instructions."""