sugatoray

## install:
## > pip install schedule watchfiles

import os
import time
from datetime import datetime
import schedule
import stat
from watchfiles import watch

sugatoray

Sub InsertAllSlides()
'  Insert all slides from all presentations in the same folder as this one
'  INTO this one; do not attempt to insert THIS file into itself, though.

    Dim vArray() As String
    Dim x As Long

    ' Change "*.PPT" to "*.PPTX" or whatever if necessary:
    EnumerateFiles ActivePresentation.Path & "\", "*.PPTX", vArray

sugatoray

/* Custom CSS for Marimo - Font customization */

/* Import Press Start 2P font from Google Fonts */
@import url('https://fonts.googleapis.com/css2?family=Press+Start+2P&display=swap');

:root {
    --marimo-monospace-font: 'Press Start 2P', 'Courier New', monospace;
    --marimo-text-font: 'Press Start 2P', 'Courier New', monospace;
    --marimo-heading-font: 'Press Start 2P', 'Courier New', monospace;
}

sugatoray

# --- Configuration ---
$sourcePath = "C:\Path\To\Your\SourcePpts" # Folder containing the PPTs to merge
$outputPath = "C:\Path\To\Your\MergedPresentation.pptx" # Full path for the merged output file

# Get a list of PowerPoint files to merge (e.g., all .pptx files)
$pptFiles = Get-ChildItem -Path $sourcePath -Filter "*.pptx" | Sort-Object Name

# --- Check if there are files to merge ---
if ($pptFiles.Count -eq 0) {
    Write-Host "No PowerPoint files found in: $sourcePath"

sugatoray

import os
import torch
import psutil
import datasets
import glob

from transformers import (
    AutoTokenizer, LlamaConfig, LlamaForCausalLM, Trainer, TrainingArguments,
    DataCollatorForLanguageModeling
)

sugatoray

# /// script
# requires-python = ">=3.11,<3.12"
# dependencies = [
#     "distilabel[mlx]",
# ]
# ///
from distilabel.models import MlxLLM
from distilabel.pipeline import InstructionResponsePipeline

llm = MlxLLM(

sugatoray

# /// script
# requires-python = ">=3.11,<3.12"
# dependencies = [
#     "distilabel[hf-transformers, hf-inference-endpoints]",
# ]
# ///
from distilabel.models import InferenceEndpointsLLM
from distilabel.pipeline import InstructionResponsePipeline

repo_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"

sugatoray

HF Model License Top20 Dist

• source: HF Model Metadata
• author: Sugato Ray @sugatoray

sugatoray

# requires sentence_transformers>=3.2.0
from sentence_transformers import SentenceTransformer, export_optimized_onnx_model, export_dynamic_quantized_onnx_model

# The model to export to ONNX (+ optimize, quantize), OpenVINO
model_id = "mixedbread-ai/mxbai-embed-large-v1"
# Where to save the exported models locally
output_dir = model_id.replace("/", "-")

onnx_model = SentenceTransformer(model_id, backend="onnx", model_kwargs={"export": True})
onnx_model.save_pretrained(output_dir)

sugatoray

Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach