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In this tutorial, we implement how to run the Bonsai 1-bit large language model efficiently using GPU acceleration and PrismML’s optimized GGUF deployment stack. We set up the environment, install the required dependencies, and download the prebuilt llama.cpp binaries, and load the Bonsai-1.7B model for fast inference on CUDA. As we progress, we examine how 1-bit quantization works under the hood, why the Q1_0_g128 format is so memory-efficient, and how this makes Bonsai practical for lightweight yet capable language model deployment. We also test core inference, benchmarking, multi-turn chat, structured JSON generation, code generation, OpenAI-compatible server mode, and a small retrieval-augmented generation workflow, giving us a complete, hands-on view of how Bonsai operates in real-world use. Copy CodeCopiedUse a different Browser import os, sys, subprocess, time, json, urllib.request, tarfile, textwrap try: import google.colab IN_COLAB = True except ImportError: IN_COLAB = False def section(title): bar = “═” * 60 print(f”n{bar}n {title}n{bar}”) section(“1 · Environment & GPU Check”) def run(cmd, capture=False, check=True, **kw): return subprocess.run( cmd, shell=True, capture_output=capture, text=True, check=check, **kw ) gpu_info = run(“nvidia-smi –query-gpu=name,memory.total,driver_version –format=csv,noheader”, capture=True, check=False) if gpu_info.returncode == 0: print(” GPU detected:”, gpu_info.stdout.strip()) else: print(” No GPU found — inference will run on CPU (much slower).”) cuda_check = run(“nvcc –version”, capture=True, check=False) if cuda_check.returncode == 0: for line in cuda_check.stdout.splitlines(): if “release” in line: print(” CUDA:”, line.strip()) break print(f” Python {sys.version.split()[0]} | Platform: Linux (Colab)”) section(“2 · Installing Python Dependencies”) run(“pip install -q huggingface_hub requests tqdm openai”) print(” huggingface_hub, requests, tqdm, openai installed”) from huggingface_hub import hf_hub_download We begin by importing the core Python modules that we need for system operations, downloads, timing, and JSON handling. We check whether we are running inside Google Colab, define a reusable section printer, and create a helper function to run shell commands cleanly from Python. We then verify the GPU and CUDA environment, print the Python runtime details, install the required Python dependencies, and prepare the Hugging Face download utility for the next stages. Copy CodeCopiedUse a different Browser section(“3 · Downloading PrismML llama.cpp Prebuilt Binaries”) RELEASE_TAG = “prism-b8194-1179bfc” BASE_URL = f”https://github.com/PrismML-Eng/llama.cpp/releases/download/{RELEASE_TAG}” BIN_DIR = “/content/bonsai_bin” os.makedirs(BIN_DIR, exist_ok=True) def detect_cuda_build(): r = run(“nvcc –version”, capture=True, check=False) for line in r.stdout.splitlines(): if “release” in line: try: ver = float(line.split(“release”)[-1].strip().split(“,”)[0].strip()) if ver >= 13.0: return “13.1” if ver >= 12.6: return “12.8” return “12.4” except ValueError: pass return “12.4” cuda_build = detect_cuda_build() print(f” Detected CUDA build slot: {cuda_build}”) TAR_NAME = f”llama-{RELEASE_TAG}-bin-linux-cuda-{cuda_build}-x64.tar.gz” TAR_URL = f”{BASE_URL}/{TAR_NAME}” tar_path = f”/tmp/{TAR_NAME}” if not os.path.exists(f”{BIN_DIR}/llama-cli”): print(f” Downloading: {TAR_URL}”) urllib.request.urlretrieve(TAR_URL, tar_path) print(” Extracting …”) with tarfile.open(tar_path, “r:gz”) as t: t.extractall(BIN_DIR) for fname in os.listdir(BIN_DIR): fp = os.path.join(BIN_DIR, fname) if os.path.isfile(fp): os.chmod(fp, 0o755) print(f” Binaries extracted to {BIN_DIR}”) bins = sorted(f for f in os.listdir(BIN_DIR) if os.path.isfile(os.path.join(BIN_DIR, f))) print(” Available:”, “, “.join(bins)) else: print(f” Binaries already present at {BIN_DIR}”) LLAMA_CLI = f”{BIN_DIR}/llama-cli” LLAMA_SERVER = f”{BIN_DIR}/llama-server” test = run(f”{LLAMA_CLI} –version”, capture=True, check=False) if test.returncode == 0: print(f” llama-cli version: {test.stdout.strip()[:80]}”) else: print(f” llama-cli test failed: {test.stderr.strip()[:200]}”) section(“4 · Downloading Bonsai-1.7B GGUF Model”) MODEL_REPO = “prism-ml/Bonsai-1.7B-gguf” MODEL_DIR = “/content/bonsai_models” GGUF_FILENAME = “Bonsai-1.7B.gguf” os.makedirs(MODEL_DIR, exist_ok=True) MODEL_PATH = os.path.join(MODEL_DIR, GGUF_FILENAME) if not os.path.exists(MODEL_PATH): print(f” Downloading {GGUF_FILENAME} (~248 MB) from HuggingFace …”) MODEL_PATH = hf_hub_download( repo_id=MODEL_REPO, filename=GGUF_FILENAME, local_dir=MODEL_DIR, ) print(f” Model saved to: {MODEL_PATH}”) else: print(f” Model already cached: {MODEL_PATH}”) size_mb = os.path.getsize(MODEL_PATH) / 1e6 print(f” File size on disk: {size_mb:.1f} MB”) section(“5 · Core Inference Helpers”) DEFAULT_GEN_ARGS = dict( temp=0.5, top_p=0.85, top_k=20, repeat_penalty=1.0, n_predict=256, n_gpu_layers=99, ctx_size=4096, ) def build_llama_cmd(prompt, system_prompt=”You are a helpful assistant.”, **overrides): args = {**DEFAULT_GEN_ARGS, **overrides} formatted = ( f”<|im_start|>systemn{system_prompt}<|im_end|>n” f”<|im_start|>usern{prompt}<|im_end|>n” f”<|im_start|>assistantn” ) safe_prompt = formatted.replace(‘”‘, ‘\”‘) return ( f'{LLAMA_CLI} -m “{MODEL_PATH}”‘ f’ -p “{safe_prompt}”‘ f’ -n {args[“n_predict”]}’ f’ –temp {args[“temp”]}’ f’ –top-p {args[“top_p”]}’ f’ –top-k {args[“top_k”]}’ f’ –repeat-penalty {args[“repeat_penalty”]}’ f’ -ngl {args[“n_gpu_layers”]}’ f’ -c {args[“ctx_size”]}’ f’ –no-display-prompt’ f’ -e’ ) def infer(prompt, system_prompt=”You are a helpful assistant.”, verbose=True, **overrides): cmd = build_llama_cmd(prompt, system_prompt, **overrides) t0 = time.time() result = run(cmd, capture=True, check=False) elapsed = time.time() – t0 output = result.stdout.strip() if verbose: print(f”n{‘─’*50}”) print(f”Prompt : {prompt[:100]}{‘…’ if len(prompt) > 100 else ”}”) print(f”{‘─’*50}”) print(output) print(f”{‘─’*50}”) print(f” {elapsed:.2f}s | ~{len(output.split())} words”) return output, elapsed print(” Inference helpers ready.”) section(“6 · Basic Inference — Hello, Bonsai!”) infer(“What makes 1-bit language models special compared to standard models?”) We download and prepare the PrismML prebuilt llama.cpp CUDA binaries that power local inference for the Bonsai model. We detect the available CUDA version, choose the matching binary build, extract the downloaded archive, make the files executable, and verify that the llama-cli binary works correctly. After that, we download the Bonsai-1.7B GGUF model from Hugging Face, set up the model path, define the default generation settings, and build the core helper functions that format prompts and run inference. Copy CodeCopiedUse a different Browser 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 → −scale ║ ║ 1 → +scale ║ ║ Every 128 weights share one FP16 scale factor. ║ ║ ║ ║ Effective bits per weight: ║ ║ 1 bit (sign) + 16/128 bits (shared scale) = 1.125 bpw ║ ║ ║ ║ Memory comparison for Bonsai-1.7B: ║ ║ FP16: 3.44 GB (1.0× baseline) ║ ║ Q1_0_g128: 0.24 GB (14.2× smaller!) ║ ║ MLX 1-bit g128: 0.27 GB (12.8× smaller) ║ ╚══════════════════════════════════════════════════════════════╝ “””)) print(” Python demo of Q1_0_g128 quantization logic:n”) import random random.seed(42) GROUP_SIZE = 128 weights_fp16 = [random.gauss(0, 0.1) for _ in range(GROUP_SIZE)] scale = max(abs(w) for w in weights_fp16) quantized = [1 if w >= 0 else 0 for w in weights_fp16] dequantized = [scale if b == 1 else -scale for b in quantized] mse = sum((a – b) ** 2 for a, b in zip(weights_fp16, dequantized)) / GROUP_SIZE print(f” FP16 weights (first 8): {[f'{w:.4f}’ for w in weights_fp16[:8]]}”) print(f” 1-bit repr (first 8): {quantized[:8]}”) print(f” Shared scale: {scale:.4f}”) print(f” Dequantized (first 8): {[f'{w:.4f}’ for w in dequantized[:8]]}”) print(f” MSE of reconstruction: {mse:.6f}”) memory_fp16 = GROUP_SIZE * 2 memory_1bit = GROUP_SIZE / 8 + 2 print(f”n Memory: FP16={memory_fp16}B vs Q1_0_g128={memory_1bit:.1f}B ” f”({memory_fp16/memory_1bit:.1f}× reduction)”) section(“8 ·

In this tutorial, we explore property-based testing using Hypothesis and build a rigorous testing pipeline that goes far beyond traditional unit testing. We implement invariants, differential testing, metamorphic testing, targeted exploration, and stateful testing to validate both functional correctness and behavioral guarantees of our systems. Instead of manually crafting edge cases, we let Hypothesis generate structured inputs, shrink failures to minimal counterexamples, and systematically uncover hidden bugs. Also, we demonstrate how modern testing practices can be integrated directly into experimental and research-driven workflows. Copy CodeCopiedUse a different Browser import sys, textwrap, subprocess, os, re, math !{sys.executable} -m pip -q install hypothesis pytest test_code = r”’ import re, math import pytest from hypothesis import ( given, assume, example, settings, note, target, HealthCheck, Phase ) from hypothesis import strategies as st from hypothesis.stateful import RuleBasedStateMachine, rule, invariant, initialize, precondition def clamp(x: int, lo: int, hi: int) -> int: if x < lo: return lo if x > hi: return hi return x def normalize_whitespace(s: str) -> str: return ” “.join(s.split()) def is_sorted_non_decreasing(xs): return all(xs[i] <= xs[i+1] for i in range(len(xs)-1)) def merge_sorted(a, b): i = j = 0 out = [] while i < len(a) and j < len(b): if a[i] <= b[j]: out.append(a[i]); i += 1 else: out.append(b[j]); j += 1 out.extend(a[i:]) out.extend(b[j:]) return out def merge_sorted_reference(a, b): return sorted(list(a) + list(b)) We set up the environment by installing Hypothesis and pytest and importing all required modules. We begin constructing the full test suite by defining core utility functions such as clamp, normalize_whitespace, and merge_sorted. We establish the functional foundation that our property-based tests will rigorously validate in later snippets. Copy CodeCopiedUse a different Browser def safe_parse_int(s: str): t = s.strip() if re.fullmatch(r”[+-]?d+”, t) is None: return (False, “not_an_int”) if len(t.lstrip(“+-“)) > 2000: return (False, “too_big”) try: return (True, int(t)) except Exception: return (False, “parse_error”) def safe_parse_int_alt(s: str): t = s.strip() if not t: return (False, “not_an_int”) sign = 1 if t[0] == “+”: t = t[1:] elif t[0] == “-“: sign = -1 t = t[1:] if not t or any(ch < “0” or ch > “9” for ch in t): return (False, “not_an_int”) if len(t) > 2000: return (False, “too_big”) val = 0 for ch in t: val = val * 10 + (ord(ch) – 48) return (True, sign * val) bounds = st.tuples(st.integers(-10_000, 10_000), st.integers(-10_000, 10_000)).map( lambda t: (t[0], t[1]) if t[0] <= t[1] else (t[1], t[0]) ) @st.composite def int_like_strings(draw): sign = draw(st.sampled_from([“”, “+”, “-“])) digits = draw(st.text(alphabet=st.characters(min_codepoint=48, max_codepoint=57), min_size=1, max_size=300)) left_ws = draw(st.text(alphabet=[” “, “t”, “n”], min_size=0, max_size=5)) right_ws = draw(st.text(alphabet=[” “, “t”, “n”], min_size=0, max_size=5)) return f”{left_ws}{sign}{digits}{right_ws}” sorted_lists = st.lists(st.integers(-10_000, 10_000), min_size=0, max_size=200).map(sorted) We implement parsing logic and define structured strategies that generate constrained, meaningful test inputs. We create composite strategies such as int_like_strings to precisely control the input space for property validation. We prepare sorted list generators and bounds strategies that enable differential and invariant-based testing. Copy CodeCopiedUse a different Browser @settings(max_examples=300, suppress_health_check=[HealthCheck.too_slow]) @given(x=st.integers(-50_000, 50_000), b=bounds) def test_clamp_within_bounds(x, b): lo, hi = b y = clamp(x, lo, hi) assert lo <= y <= hi @settings(max_examples=300, suppress_health_check=[HealthCheck.too_slow]) @given(x=st.integers(-50_000, 50_000), b=bounds) def test_clamp_idempotent(x, b): lo, hi = b y = clamp(x, lo, hi) assert clamp(y, lo, hi) == y @settings(max_examples=250) @given(s=st.text()) @example(” attb n c “) def test_normalize_whitespace_is_idempotent(s): t = normalize_whitespace(s) assert normalize_whitespace(t) == t assert normalize_whitespace(” nt ” + s + ” t”) == normalize_whitespace(s) @settings(max_examples=250, suppress_health_check=[HealthCheck.too_slow]) @given(a=sorted_lists, b=sorted_lists) def test_merge_sorted_matches_reference(a, b): out = merge_sorted(a, b) ref = merge_sorted_reference(a, b) assert out == ref assert is_sorted_non_decreasing(out) We define core property tests that validate correctness and idempotence across multiple functions. We use Hypothesis decorators to automatically explore edge cases and verify behavioral guarantees such as boundary constraints and deterministic normalization. We also implement differential testing to ensure our merge implementation matches a trusted reference. Copy CodeCopiedUse a different Browser @settings(max_examples=250, deadline=200, suppress_health_check=[HealthCheck.too_slow]) @given(s=int_like_strings()) def test_two_parsers_agree_on_int_like_strings(s): ok1, v1 = safe_parse_int(s) ok2, v2 = safe_parse_int_alt(s) assert ok1 and ok2 assert v1 == v2 @settings(max_examples=250) @given(s=st.text(min_size=0, max_size=200)) def test_safe_parse_int_rejects_non_ints(s): t = s.strip() m = re.fullmatch(r”[+-]?d+”, t) ok, val = safe_parse_int(s) if m is None: assert ok is False else: if len(t.lstrip(“+-“)) > 2000: assert ok is False and val == “too_big” else: assert ok is True and isinstance(val, int) def variance(xs): if len(xs) < 2: return 0.0 mu = sum(xs) / len(xs) return sum((x – mu) ** 2 for x in xs) / (len(xs) – 1) @settings(max_examples=250, phases=[Phase.generate, Phase.shrink]) @given(xs=st.lists(st.integers(-1000, 1000), min_size=0, max_size=80)) def test_statistics_sanity(xs): target(variance(xs)) if len(xs) == 0: assert variance(xs) == 0.0 elif len(xs) == 1: assert variance(xs) == 0.0 else: v = variance(xs) assert v >= 0.0 k = 7 assert math.isclose(variance([x + k for x in xs]), v, rel_tol=1e-12, abs_tol=1e-12) We extend our validation to parsing robustness and statistical correctness using targeted exploration. We verify that two independent integer parsers agree on structured inputs and enforce rejection rules on invalid strings. We further implement metamorphic testing by validating invariants of variance under transformation. Copy CodeCopiedUse a different Browser class Bank: def __init__(self): self.balance = 0 self.ledger = [] def deposit(self, amt: int): if amt <= 0: raise ValueError(“deposit must be positive”) self.balance += amt self.ledger.append((“dep”, amt)) def withdraw(self, amt: int): if amt <= 0: raise ValueError(“withdraw must be positive”) if amt > self.balance: raise ValueError(“insufficient funds”) self.balance -= amt self.ledger.append((“wd”, amt)) def replay_balance(self): bal = 0 for typ, amt in self.ledger: bal += amt if typ == “dep” else -amt return bal class BankMachine(RuleBasedStateMachine): def __init__(self): super().__init__() self.bank = Bank() @initialize() def init(self): assert self.bank.balance == 0 assert self.bank.replay_balance() == 0 @rule(amt=st.integers(min_value=1, max_value=10_000)) def deposit(self, amt): self.bank.deposit(amt) @precondition(lambda self: self.bank.balance > 0) @rule(amt=st.integers(min_value=1, max_value=10_000)) def withdraw(self, amt): assume(amt <= self.bank.balance) self.bank.withdraw(amt) @invariant() def balance_never_negative(self): assert self.bank.balance >= 0 @invariant() def ledger_replay_matches_balance(self): assert self.bank.replay_balance() == self.bank.balance TestBankMachine = BankMachine.TestCase ”’ path = “/tmp/test_hypothesis_advanced.py” with open(path, “w”, encoding=”utf-8″) as f: f.write(test_code) print(“Hypothesis version:”, __import__(“hypothesis”).__version__) print(“nRunning pytest on:”, path, “n”) res = subprocess.run([sys.executable, “-m”, “pytest”, “-q”, path], capture_output=True, text=True) print(res.stdout) if res.returncode != 0: print(res.stderr) if res.returncode == 0: print(“nAll Hypothesis tests passed.”) elif res.returncode == 5:

Quantum computing has spent years living in the future tense. Hardware has improved, research has compounded, and venture dollars have followed — but the gap between a quantum processor running in a lab and one running a real-world application remains stubbornly wide. NVIDIA moved to close that gap with the launch of NVIDIA Ising, the world’s first family of open quantum AI models specifically designed to help researchers and enterprises build quantum processors capable of running useful applications. Here’s the core problem Ising is designed to solve: quantum computers are extraordinarily sensitive. Their fundamental unit of computation, the qubit, is so easily disturbed by environmental noise that errors accumulate rapidly during computation. Before you can run anything meaningful on a quantum processor, two things have to work well — calibration (making sure the hardware is tuned and operating correctly) and error correction (detecting and fixing errors as they occur in real time). Both of these have historically been manual, slow, and difficult to scale. NVIDIA is betting that AI can automate both. What the Ising Model Family Actually Includes NVIDIA Ising includes two distinct components: Ising Calibration and Ising Decoding. Ising Calibration is a vision language model — a model architecture familiar to anyone who has worked with multimodal AI — that is designed to rapidly interpret and react to measurements from quantum processors. Think of it as an AI agent that continuously watches diagnostic readouts from quantum hardware and autonomously adjusts the system to keep it running optimally. This enables AI agents to automate continuous calibration, reducing the time needed from days to hours. That’s not a minor speedup — in quantum hardware development, days of calibration time between experiments is a major bottleneck. Ising Decoding comes in two variants of a 3D convolutional neural network (3D CNN) model, each optimized for different trade-offs: one tuned for speed and the other tuned for accuracy. These models perform real-time decoding for quantum error correction. If you’ve worked with signal processing or sequence modeling, error correction decoding is conceptually similar — you’re trying to infer what the ‘correct’ state of the system should be, given noisy observations. Ising Decoding models are up to 2.5x faster and 3x more accurate than pyMatching, the current open-source industry standard. The Ecosystem Is Already Moving Ising Calibration is already in use by Atom Computing, Academia Sinica, EeroQ, Conductor Quantum, Fermi National Accelerator Laboratory, Harvard John A. Paulson School of Engineering and Applied Sciences, Infleqtion, IonQ, IQM Quantum Computers, Lawrence Berkeley National Laboratory’s Advanced Quantum Testbed, Q-CTRL, and the U.K. National Physical Laboratory. Ising Decoding is being deployed by Cornell University, EdenCode, Infleqtion, IQM Quantum Computers, Quantum Elements, Sandia National Laboratories, SEEQC, University of California San Diego, UC Santa Barbara, University of Chicago, University of Southern California, and Yonsei University. That’s a remarkably broad day-one adoption spanning national labs, Ivy League institutions, and commercial quantum hardware companies across multiple qubit modalities. How It Fits Into NVIDIA’s Quantum Stack NVIDIA Ising complements the NVIDIA CUDA-Q software platform for hybrid quantum-classical computing and integrates with the NVIDIA NVQLink QPU-GPU hardware interconnect for real-time control and quantum error correction. CUDA-Q is NVIDIA’s broader programming model for hybrid quantum-classical workflows — if you’ve written CUDA kernels for GPU acceleration, CUDA-Q follows a similar philosophy of tightly coupling classical and accelerated compute. NVQLink is the hardware bridge that lets GPUs communicate with quantum processing units (QPUs) at the latency required for real-time error correction. Key Takeaways NVIDIA Ising is the world’s first family of open quantum AI models, purpose-built to solve the two hardest engineering problems blocking practical quantum computing — calibration and error correction — using AI instead of slow, manual processes. Ising Calibration uses a vision language model to autonomously tune quantum processors, reducing the time required for continuous calibration from days to hours by enabling AI agents to interpret and react to hardware measurements in real time. Ising Decoding uses a 3D convolutional neural network (3D CNN) to perform real-time quantum error correction, delivering up to 2.5x faster performance and 3x higher accuracy compared to pyMatching. Adoption is already broad and diverse on day one, with leading institutions including Fermi National Accelerator Laboratory, Harvard, Lawrence Berkeley National Laboratory’s Advanced Quantum Testbed, IQM Quantum Computers, Sandia National Laboratories, and over a dozen universities and enterprises deploying Ising Calibration and Ising Decoding across multiple qubit modalities. Ising integrates directly into NVIDIA’s full quantum-classical software and hardware stack, complementing the NVIDIA CUDA-Q platform for hybrid quantum-classical computing and the NVIDIA NVQLink QPU-GPU hardware interconnect, with models available on GitHub, Hugging Face, and build.nvidia.com and fine-tunable via NVIDIA NIM microservices. Check out the Technical details and Product Page here. Also, feel free to follow us on Twitter and don’t forget to join our 130k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post NVIDIA Releases Ising: the First Open Quantum AI Model Family for Hybrid Quantum-Classical Systems appeared first on MarkTechPost.

Table of contents What Is AI Red Teaming? Top 19 AI Red Teaming Tools (2026) Conclusion What Is AI Red Teaming? AI Red Teaming is the process of systematically testing artificial intelligence systems—especially generative AI and machine learning models—against adversarial attacks and security stress scenarios. Red teaming goes beyond classic penetration testing; while penetration testing targets known software flaws, red teaming probes for unknown AI-specific vulnerabilities, unforeseen risks, and emergent behaviors. The process adopts the mindset of a malicious adversary, simulating attacks such as prompt injection, data poisoning, jailbreaking, model evasion, bias exploitation, and data leakage. This ensures AI models are not only robust against traditional threats, but also resilient to novel misuse scenarios unique to current AI systems. Key Features & Benefits Threat Modeling: Identify and simulate all potential attack scenarios—from prompt injection to adversarial manipulation and data exfiltration. Realistic Adversarial Behavior: Emulates actual attacker techniques using both manual and automated tools, beyond what is covered in penetration testing. Vulnerability Discovery: Uncovers risks such as bias, fairness gaps, privacy exposure, and reliability failures that may not emerge in pre-release testing. Regulatory Compliance: Supports compliance requirements (EU AI Act, NIST RMF, US Executive Orders) increasingly mandating red teaming for high-risk AI deployments. Continuous Security Validation: Integrates into CI/CD pipelines, enabling ongoing risk assessment and resilience improvement. Red teaming can be carried out by internal security teams, specialized third parties, or platforms built solely for adversarial testing of AI systems. Top 19 AI Red Teaming Tools (2026) Below is a rigorously researched list of the latest and most reputable AI red teaming tools, frameworks, and platforms—spanning open-source, commercial, and industry-leading solutions for both generic and AI-specific attacks: Mindgard – Automated AI red teaming and model vulnerability assessment. MIND.io – Data security platform providing autonomous DLP and data detection and response (DDR) for Agentic AI. Garak – Open-source LLM adversarial testing toolkit. HiddenLayer– A comprehensive AI security platform that provides automated model scanning and red teaming. AIF360 (IBM) – AI Fairness 360 toolkit for bias and fairness assessment. Foolbox – Library for adversarial attacks on AI models. Penligent– An AI-powered penetration testing tool that requires no expert knowledge Giskard– Comprehensive testing for traditional Machine Learning models and Agentic AI Adversarial Robustness Toolbox (ART) – IBM’s open-source toolkit for ML model security. FuzzyAI– A powerful tool for automated LLM fuzzing DeepTeam– An AI framework to red team LLMs and LLM systems SPLX– A unified platform to test, protect & govern AI at scale Pentera– A Platform that executes AI-driven adversarial testing in production to validate exploitability, prioritize remediation. Dreadnode – ML/AI vulnerability detection and red team toolkit. Galah – AI honeypot framework supporting LLM use cases. Meerkat – Data visualization and adversarial testing for ML. Ghidra/GPT-WPRE – Code reverse engineering platform with LLM analysis plugins. Guardrails – Application security for LLMs, prompt injection defense. Snyk – Developer-focused LLM red teaming tool simulating prompt injection and adversarial attacks. Conclusion In the era of generative AI and Large Language Models, AI Red Teaming has become foundational to responsible and resilient AI deployment. Organizations must embrace adversarial testing to uncover hidden vulnerabilities and adapt their defenses to new threat vectors—including attacks driven by prompt engineering, data leakage, bias exploitation, and emergent model behaviors. The best practice is to combine manual expertise with automated platforms utilizing the top red teaming tools listed above for a comprehensive, proactive security posture in AI systems. Check out our Twitter page and don’t forget to join our 130k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Top 19 AI Red Teaming Tools (2026): Secure Your ML Models appeared first on MarkTechPost.

In this tutorial, we explore how to build a fully functional background task processing system using Huey directly, without relying on Redis. We configure a SQLite-backed Huey instance, start a real consumer in the notebook, and implement advanced task patterns, including retries, priorities, scheduling, pipelines, locking, and monitoring via signals. As we move step by step, we demonstrate how we can simulate production-grade asynchronous job handling while keeping everything self-contained and easy to run in a cloud notebook environment. Copy CodeCopiedUse a different Browser !pip -q install -U huey import os import time import json import random import threading from datetime import datetime from huey import SqliteHuey, crontab from huey.constants import WORKER_THREAD DB_PATH = “/content/huey_demo.db” if os.path.exists(DB_PATH): os.remove(DB_PATH) huey = SqliteHuey( name=”colab-huey”, filename=DB_PATH, results=True, store_none=False, utc=True, ) print(“Huey backend:”, type(huey).__name__) print(“SQLite DB at:”, DB_PATH) We install Huey and configure a SQLite-backed instance. We initialize the database file and ensure a clean environment before starting execution. By doing this, we establish a lightweight yet production-style task queue setup without external dependencies. Copy CodeCopiedUse a different Browser EVENT_LOG = [] @huey.signal() def _log_all_signals(signal, task, exc=None): EVENT_LOG.append({ “ts”: datetime.utcnow().isoformat() + “Z”, “signal”: str(signal), “task”: getattr(task, “name”, None), “id”: getattr(task, “id”, None), “args”: getattr(task, “args”, None), “kwargs”: getattr(task, “kwargs”, None), “exc”: repr(exc) if exc else None, }) def print_latest_events(n=10): print(“n— Latest Huey events —“) for row in EVENT_LOG[-n:]: print(json.dumps(row, indent=2)) We implement a signal handler to capture and store task lifecycle events in a structured log. We track execution details, including task IDs, arguments, and exceptions, to improve observability. Through this mechanism, we build real-time monitoring into our asynchronous system. Copy CodeCopiedUse a different Browser @huey.task(priority=50) def quick_add(a, b): return a + b @huey.task(priority=10) def slow_io(seconds=1.0): time.sleep(seconds) return f”slept={seconds}” @huey.task(retries=3, retry_delay=1, priority=100) def flaky_network_call(p_fail=0.6): if random.random() < p_fail: raise RuntimeError(“Transient failure (simulated)”) return “OK” @huey.task(context=True, priority=60) def cpu_pi_estimate(samples=200_000, task=None): inside = 0 rnd = random.random for _ in range(samples): x, y = rnd(), rnd() if x*x + y*y <= 1.0: inside += 1 est = 4.0 * inside / samples return {“task_id”: task.id if task else None, “pi_estimate”: est, “samples”: samples} We define multiple tasks with priorities, retry configurations, and contextual awareness. We simulate different workloads, including simple arithmetic, I/O delay, transient failures, and CPU-bound computation. By doing this, we demonstrate how Huey handles reliability, execution order, and task metadata. Copy CodeCopiedUse a different Browser @huey.lock_task(“demo:daily-sync”) @huey.task() def locked_sync_job(tag=”sync”): time.sleep(1.0) return f”locked-job-done:{tag}:{datetime.utcnow().isoformat()}Z” @huey.task() def fetch_number(seed=7): random.seed(seed) return random.randint(1, 100) @huey.task() def transform_number(x, scale=3): return x * scale @huey.task() def store_result(x): return {“stored_value”: x, “stored_at”: datetime.utcnow().isoformat() + “Z”} We introduce locking to prevent concurrent execution of critical jobs. We also define tasks that will later be chained together using pipelines to form structured workflows. Through this design, we model realistic background processing patterns that require sequencing and concurrency control. Copy CodeCopiedUse a different Browser TICK = {“count”: 0} @huey.task() def heartbeat(): TICK[“count”] += 1 print(f”[heartbeat] tick={TICK[‘count’]} utc={datetime.utcnow().isoformat()}Z”) @huey.periodic_task(crontab(minute=”*”)) def heartbeat_minutely(): heartbeat() _TIMER_STATE = {“running”: False, “timer”: None} def start_seconds_heartbeat(interval_sec=15): _TIMER_STATE[“running”] = True def _tick(): if not _TIMER_STATE[“running”]: return huey.enqueue(heartbeat.s()) t = threading.Timer(interval_sec, _tick) _TIMER_STATE[“timer”] = t t.start() _tick() def stop_seconds_heartbeat(): _TIMER_STATE[“running”] = False t = _TIMER_STATE.get(“timer”) if t is not None: try: t.cancel() except Exception: pass _TIMER_STATE[“timer”] = None We define heartbeat behavior and configure minute-level periodic execution using Huey’s crontab scheduling. We also implement a timer-based mechanism to simulate sub-minute execution intervals for demonstration purposes. With this setup, we create visible recurring background activity within the notebook. Copy CodeCopiedUse a different Browser 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 = quick_add(10, 32) r2 = slow_io(0.75) print(“quick_add result:”, r1(blocking=True, timeout=5)) print(“slow_io result:”, r2(blocking=True, timeout=5)) print(“nRetries + priority demo (flaky task)…”) rf = flaky_network_call(p_fail=0.7) try: print(“flaky_network_call result:”, rf(blocking=True, timeout=10)) except Exception as e: print(“flaky_network_call failed even after retries:”, repr(e)) print(“nContext task (task id inside payload)…”) rp = cpu_pi_estimate(samples=150_000) print(“pi payload:”, rp(blocking=True, timeout=20)) print(“nLocks demo: enqueue multiple locked jobs quickly (should serialize)…”) locked_results = [locked_sync_job(tag=f”run{i}”) for i in range(3)] print([res(blocking=True, timeout=10) for res in locked_results]) print(“nScheduling demo: run slow_io in ~3 seconds…”) rs = slow_io.schedule(args=(0.25,), delay=3) print(“scheduled handle:”, rs) print(“scheduled slow_io result:”, rs(blocking=True, timeout=10)) print(“nRevoke demo: schedule a task in 5s then revoke before it runs…”) rv = slow_io.schedule(args=(0.1,), delay=5) rv.revoke() time.sleep(6) try: out = rv(blocking=False) print(“revoked task output:”, out) except Exception as e: print(“revoked task did not produce result (expected):”, type(e).__name__, str(e)[:120]) print(“nPipeline demo…”) pipeline = ( fetch_number.s(123) .then(transform_number, 5) .then(store_result) ) pipe_res = huey.enqueue(pipeline) print(“pipeline final result:”, pipe_res(blocking=True, timeout=10)) print(“nStarting 15-second heartbeat demo for ~40 seconds…”) start_seconds_heartbeat(interval_sec=15) time.sleep(40) stop_seconds_heartbeat() print(“Stopped 15-second heartbeat demo.”) print_latest_events(12) print(“nStopping consumer gracefully…”) consumer.stop(graceful=True) consumer_thread.join(timeout=5) print(“Consumer stopped.”) We start a threaded consumer inside the notebook to process tasks asynchronously. We enqueue tasks, test retries, demonstrate scheduling and revocation, execute pipelines, and observe logged signals. Finally, we gracefully shut down the consumer to ensure clean resource management and controlled system termination. In conclusion, we designed and executed an advanced asynchronous task system using Huey with a SQLite backend and an in-notebook consumer. We implemented retries, task prioritization, future scheduling, revocation, locking mechanisms, task chaining through pipelines, and periodic behavior simulation, all within a Colab-friendly setup. Through this approach, we gained a clear understanding of how to use Huey to manage background workloads efficiently and extend this architecture to real-world production deployments. Check out the Full Coding Notebook/Implementation here. Also, feel free to follow us on Twitter and don’t forget to join our 130k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post A Coding Guide to Build a Production-Grade Background Task Processing System Using Huey with SQLite, Scheduling, Retries, Pipelines, and Concurrency Control appeared first on MarkTechPost.

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 practical inference workflows. We begin by setting up the exact dependencies needed for Transformers-based execution, verifying GPU availability, and loading openai/gpt-oss-20b with the correct configuration using native MXFP4 quantization, torch.bfloat16 activations. As we move through the tutorial, we work directly with core capabilities such as structured generation, streaming, multi-turn dialogue handling, tool execution patterns, and batch inference, while keeping in mind how open-weight models differ from closed-hosted APIs in terms of transparency, controllability, memory constraints, and local execution trade-offs. Also, we treat GPT-OSS not just as a chatbot, but as a technically inspectable open-weight LLM stack that we can configure, prompt, and extend inside a reproducible workflow. Copy CodeCopiedUse a different Browser print(” Step 1: Installing required packages…”) print(“=” * 70) !pip install -q –upgrade pip !pip install -q transformers>=4.51.0 accelerate sentencepiece protobuf !pip install -q huggingface_hub gradio ipywidgets !pip install -q openai-harmony import transformers print(f” Transformers version: {transformers.__version__}”) import torch print(f”n System Information:”) print(f” PyTorch version: {torch.__version__}”) print(f” CUDA available: {torch.cuda.is_available()}”) if torch.cuda.is_available(): gpu_name = torch.cuda.get_device_name(0) gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1e9 print(f” GPU: {gpu_name}”) print(f” GPU Memory: {gpu_memory:.2f} GB”) if gpu_memory < 15: print(f”n WARNING: gpt-oss-20b requires ~16GB VRAM.”) print(f” Your GPU has {gpu_memory:.1f}GB. Consider using Colab Pro for T4/A100.”) else: print(f”n GPU memory sufficient for gpt-oss-20b”) else: print(“n No GPU detected!”) print(” Go to: Runtime → Change runtime type → Select ‘T4 GPU'”) raise RuntimeError(“GPU required for this tutorial”) print(“n” + “=” * 70) print(” PART 2: Loading GPT-OSS Model (Correct Method)”) print(“=” * 70) from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import torch MODEL_ID = “openai/gpt-oss-20b” print(f”n Loading model: {MODEL_ID}”) print(” This may take several minutes on first run…”) print(” (Model size: ~40GB download, uses native MXFP4 quantization)”) tokenizer = AutoTokenizer.from_pretrained( MODEL_ID, trust_remote_code=True ) model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, device_map=”auto”, trust_remote_code=True, ) pipe = pipeline( “text-generation”, model=model, tokenizer=tokenizer, ) print(” Model loaded successfully!”) print(f” Model dtype: {model.dtype}”) print(f” Device: {model.device}”) if torch.cuda.is_available(): allocated = torch.cuda.memory_allocated() / 1e9 reserved = torch.cuda.memory_reserved() / 1e9 print(f” GPU Memory Allocated: {allocated:.2f} GB”) print(f” GPU Memory Reserved: {reserved:.2f} GB”) print(“n” + “=” * 70) print(” PART 3: Basic Inference Examples”) print(“=” * 70) def generate_response(messages, max_new_tokens=256, temperature=0.8, top_p=1.0): “”” Generate a response using gpt-oss with recommended parameters. OpenAI recommends: temperature=1.0, top_p=1.0 for gpt-oss “”” output = pipe( messages, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, top_p=top_p, pad_token_id=tokenizer.eos_token_id, ) return output[0][“generated_text”][-1][“content”] print(“n Example 1: Simple Question Answering”) print(“-” * 50) messages = [ {“role”: “user”, “content”: “What is the Pythagorean theorem? Explain briefly.”} ] response = generate_response(messages, max_new_tokens=150) print(f”User: {messages[0][‘content’]}”) print(f”nAssistant: {response}”) print(“nn Example 2: Code Generation”) print(“-” * 50) messages = [ ] response = generate_response(messages, max_new_tokens=300) print(f”User: {messages[0][‘content’]}”) print(f”nAssistant: {response}”) print(“nn Example 3: Creative Writing”) print(“-” * 50) messages = [ {“role”: “user”, “content”: “Write a haiku about artificial intelligence.”} ] response = generate_response(messages, max_new_tokens=100, temperature=1.0) print(f”User: {messages[0][‘content’]}”) print(f”nAssistant: {response}”) We set up the full Colab environment required to run GPT-OSS properly and verify that the system has a compatible GPU with enough VRAM. We install the core libraries, check the PyTorch and Transformers versions, and confirm that the runtime is suitable for loading an open-weight model like gpt-oss-20b. We then load the tokenizer, initialize the model with the correct technical configuration, and run a few basic inference examples to confirm that the open-weight pipeline is working end to end. Copy CodeCopiedUse a different Browser print(“n” + “=” * 70) print(” PART 4: Configurable Reasoning Effort”) print(“=” * 70) print(“”” GPT-OSS supports different reasoning effort levels: • LOW – Quick, concise answers (fewer tokens, faster) • MEDIUM – Balanced reasoning and response • HIGH – Deep thinking with full chain-of-thought The reasoning effort is controlled through system prompts and generation parameters. “””) class ReasoningEffortController: “”” Controls reasoning effort levels for gpt-oss generations. “”” EFFORT_CONFIGS = { “low”: { “system_prompt”: “You are a helpful assistant. Be concise and direct.”, “max_tokens”: 200, “temperature”: 0.7, “description”: “Quick, concise answers” }, “medium”: { “system_prompt”: “You are a helpful assistant. Think through problems step by step and provide clear, well-reasoned answers.”, “max_tokens”: 400, “temperature”: 0.8, “description”: “Balanced reasoning” }, “high”: { “system_prompt”: “””You are a helpful assistant with advanced reasoning capabilities. For complex problems: 1. First, analyze the problem thoroughly 2. Consider multiple approaches 3. Show your complete chain of thought 4. Provide a comprehensive, well-reasoned answer Take your time to think deeply before responding.”””, “max_tokens”: 800, “temperature”: 1.0, “description”: “Deep chain-of-thought reasoning” } } def __init__(self, pipeline, tokenizer): self.pipe = pipeline self.tokenizer = tokenizer def generate(self, user_message: str, effort: str = “medium”) -> dict: “””Generate response with specified reasoning effort.””” if effort not in self.EFFORT_CONFIGS: raise ValueError(f”Effort must be one of: {list(self.EFFORT_CONFIGS.keys())}”) config = self.EFFORT_CONFIGS[effort] messages = [ {“role”: “system”, “content”: config[“system_prompt”]}, {“role”: “user”, “content”: user_message} ] output = self.pipe( messages, max_new_tokens=config[“max_tokens”], do_sample=True, temperature=config[“temperature”], top_p=1.0, pad_token_id=self.tokenizer.eos_token_id, ) return { “effort”: effort, “description”: config[“description”], “response”: output[0][“generated_text”][-1][“content”], “max_tokens_used”: config[“max_tokens”] } reasoning_controller = ReasoningEffortController(pipe, tokenizer) print(f”n Logic Puzzle: {test_question}n”) for effort in [“low”, “medium”, “high”]: result = reasoning_controller.generate(test_question, effort) print(f”━━━ {effort.upper()} ({result[‘description’]}) ━━━”) print(f”{result[‘response’][:500]}…”) print() print(“n” + “=” * 70) print(” PART 5: Structured Output Generation (JSON Mode)”) print(“=” * 70) import json import re class StructuredOutputGenerator: “”” Generate structured JSON outputs with schema validation. “”” def __init__(self, pipeline, tokenizer): self.pipe = pipeline self.tokenizer = tokenizer def generate_json(self, prompt: str, schema: dict, max_retries: int = 2) -> dict: “”” Generate JSON output in accordance with a specified schema. Args: prompt: The user’s request schema: JSON schema description max_retries: Number of retries on parse failure “”” schema_str = json.dumps(schema, indent=2) system_prompt = f”””You are a helpful assistant that ONLY outputs valid JSON. Your response must exactly match this JSON schema: {schema_str} RULES: – Output ONLY the JSON object, nothing else – No markdown code blocks (no “`) – No explanations before or after – Ensure all required fields are present – Use correct data types as specified””” messages = [ {“role”: “system”, “content”: system_prompt}, {“role”: “user”, “content”: