Browsing: Implementation

import subprocess, sys def pip(*pkgs): subprocess.check_call([sys.executable, “-m”, “pip”, “install”, “-q”, *pkgs]) pip(“llmcompressor”, “compressed-tensors”, “transformers>=4.45”, “accelerate”, “datasets”) import os, gc, time, json, math from pathlib import Path…

header(“6. RAW CUDA KERNEL — MANDELBROT”) mandel = cp.RawKernel(r”’ extern “C” __global__ void mandel(float xmin, float xmax, float ymin, float ymax, int W, int H, int…

factor_prices = load_factors_dataset() X_full, F_full = prices_to_returns(prices, factor_prices) X_tr, X_te, F_tr, F_te = train_test_split( X_full, F_full, test_size=0.33, shuffle=False ) fm = MeanRisk( objective_function=ObjectiveFunction.MAXIMIZE_RATIO, risk_measure=RiskMeasure.VARIANCE, prior_estimator=FactorModel(), )…

banner(“Part 5 — Streaming”) mem.attribution(entity_id=”[email protected]”, process_id=”personal-assistant”) stream = client.chat.completions.create( model=MODEL, messages=[{“role”: “user”, “content”: “In two sentences, what do you remember about me?”}], stream=True, ) print(“[stream] “,…

banner(“STEP 6 — IOC hunting in the deobfuscated strings”) PATTERNS = [ (“URL”, re.compile(r”https?://[^\s\”<>]+”)), (“IP”, re.compile(r”\b(?:\d{1,3}\.){3}\d{1,3}\b”)), (“PE/script”, re.compile(r”[A-Za-z0-9_]+\.(?:exe|dll|sys|ps1|bat)\b”, re.I)), (“Win32 API”, re.compile(r”\b(?:Reg(?:Open|Set|Create|Delete)Key(?:Ex)?A?|VirtualAlloc(?:Ex)?|CreateRemoteThread|WinExec|LoadLibraryA?|GetProcAddress|InternetOpenA?)\b”)), (“Registry”, re.compile(r”SOFTWARE\\\\?[A-Za-z0-9_\\\\]+”, re.I)), (“Base64-like”,…

filename_counter: Counter = Counter() all_json_keys: Counter = Counter() samples_for_show: List = [] for i, row in enumerate(tqdm(ds_test, desc=”inspecting structure”, total=200)): if i >= 200: break p…

In this tutorial, we explore the lambda/hermes-agent-reasoning-traces dataset to understand how agent-based models think, use tools, and generate responses across multi-turn conversations. We start by loading…

EPOCHS = 15 opt = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4) sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=EPOCHS) loss_fn = nn.MSELoss() hist = {“tr”: [], “va”: [], “r”: []} def pearson(a, b):…

print(“=” * 62) print(“SECTION 9 · pyrightconfig.json”) print(“=” * 62) config = { “include”: [“src”], “exclude”: [“**/__pycache__”], “pythonVersion”: “3.11”, “typeCheckingMode”: “strict”, “reportMissingImports”: “error”, “reportMissingTypeStubs”: “warning”, “reportUnknownVariableType”:…

In this tutorial, we build an advanced hands-on workflow with the Deepgram Python SDK and explore how modern voice AI capabilities come together in a single…