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Browsing: coding
A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning
import subprocess, sys, os, shutil, glob def pip_install(args): subprocess.run([sys.executable, “-m”, “pip”, “install”, “-q”, *args], check=True) pip_install([“huggingface_hub>=0.26,<1.0”]) pip_install([ “-U”, “transformers>=4.49,<4.57”, “accelerate>=0.33.0”, “bitsandbytes>=0.43.0”, “peft>=0.11.0”, “datasets>=2.20.0,<3.0”, “sentence-transformers>=3.0.0,<4.0”, “faiss-cpu”, ])…
A Coding Implementation to Build an AI-Powered File Type Detection and Security Analysis Pipeline with Magika and OpenAI
!pip install magika openai -q import os, io, json, zipfile, textwrap, hashlib, tempfile, getpass from pathlib import Path from collections import Counter from magika import Magika…
A Coding Tutorial for Running PrismML Bonsai 1-Bit LLM on CUDA with GGUF, Benchmarking, Chat, JSON, and RAG
section(“7 · Q1_0_g128 Quantization — What’s Happening Under the Hood”) print(textwrap.dedent(“”” ╔══════════════════════════════════════════════════════════════╗ ║ Bonsai Q1_0_g128 Weight Representation ║ ╠══════════════════════════════════════════════════════════════╣ ║ Each weight = 1 bit: 0…
Anthropic Releases Claude Opus 4.7: A Major Upgrade for Agentic Coding, High-Resolution Vision, and Long-Horizon Autonomous Tasks
Anthropic has launched Claude Opus 4.7, it’s latest frontier model and a direct successor to Claude Opus 4.6. The release is positioned as a focused improvement…
A End-to-End Coding Guide to Running OpenAI GPT-OSS Open-Weight Models with Advanced Inference Workflows
In this tutorial, we explore how to run OpenAI’s open-weight GPT-OSS models in Google Colab with a strong focus on their technical behavior, deployment requirements, and…
A Coding Guide to Build a Production-Grade Background Task Processing System Using Huey with SQLite, Scheduling, Retries, Pipelines, and Concurrency Control
consumer = huey.create_consumer( workers=4, worker_type=WORKER_THREAD, periodic=True, initial_delay=0.1, backoff=1.15, max_delay=2.0, scheduler_interval=1, check_worker_health=True, health_check_interval=10, flush_locks=False, ) consumer_thread = threading.Thread(target=consumer.run, daemon=True) consumer_thread.start() print(“Consumer started (threaded).”) print(“\nEnqueue basics…”) r1 =…
Qwen Team Open-Sources Qwen3.6-35B-A3B: A Sparse MoE Vision-Language Model with 3B Active Parameters and Agentic Coding Capabilities
The open-source AI landscape has a new entry worth paying attention to. The Qwen team at Alibaba has released Qwen3.6-35B-A3B, the first open-weight model from the…
A Coding Implementation to Build Multi-Agent AI Systems with SmolAgents Using Code Execution, Tool Calling, and Dynamic Orchestration
In this tutorial, we build an advanced, production-ready agentic system using SmolAgents and demonstrate how modern, lightweight AI agents can reason, execute code, dynamically manage tools,…
A Coding Implementation of Crawl4AI for Web Crawling, Markdown Generation, JavaScript Execution, and LLM-Based Structured Extraction
import subprocess import sys print(“📦 Installing system dependencies…”) subprocess.run([‘apt-get’, ‘update’, ‘-qq’], capture_output=True) subprocess.run([‘apt-get’, ‘install’, ‘-y’, ‘-qq’, ‘libnss3’, ‘libnspr4’, ‘libatk1.0-0’, ‘libatk-bridge2.0-0’, ‘libcups2’, ‘libdrm2’, ‘libxkbcommon0’, ‘libxcomposite1’, ‘libxdamage1’, ‘libxfixes3’,…
A Step-by-Step Coding Tutorial on NVIDIA PhysicsNeMo: Darcy Flow, FNOs, PINNs, Surrogate Models, and Inference Benchmarking
print(“\n” + “=”*80) print(“SECTION 4: DATA VISUALIZATION”) print(“=”*80) def visualize_darcy_samples( permeability: np.ndarray, pressure: np.ndarray, n_samples: int = 3 ): “””Visualize Darcy flow samples.””” fig, axes =…
