Browsing: Implementation

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):…

In this tutorial, we work with Microsoft’s OpenMementos dataset and explore how reasoning traces are structured through blocks and mementos in a practical, Colab-ready workflow. We…

import numpy as np import matplotlib.pyplot as plt fig, axes = plt.subplots(1, 2, figsize=(14, 4.5)) tk, mk = zip(*mem_kvc); tb, mb = zip(*mem_base) axes[0].plot(tk, mk, label=”with…

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…

BATCH = 128 EPOCHS = 30 steps_per_epoch = len(X_train) // BATCH train_losses, val_losses = [], [] t0 = time.time() for epoch in range(EPOCHS): key, sk =…

In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional search space…

class QwenChat: def __init__(self, model, processor, system=None, tools=None): self.model, self.processor = model, processor self.tokenizer = processor.tokenizer self.history: list[dict] = [] if system: self.history.append({“role”: “system”, “content”: system})…