World Wire

Posts

How to Align Large Language Models with Human Preferences Using Direct Preference Optimization, QLoRA, and Ultra-Feedback

By Tokenboy February 12, 2026

In this tutorial, we implement an end-to-end Direct Preference Optimization workflow to align a large language model with human preferences without using a reward model. We combine TRL’s DPOTrainer with QLoRA and PEFT to make preference-based alignment feasible on a single Colab GPU. We train directly on the UltraFeedback binarized dataset, where each prompt has a chosen and a rejected response, allowing us to shape model behavior and style rather than just factual recall. Copy Code Copied Use a different Browser import os import math import random import torch !pip -q install -U "transformers>=4.45.0" "datasets>=2.19.0" "accelerate>=0.33.0" "trl>=0.27.0" "peft>=0.12.0" "bitsandbytes>=0.43.0" "sentencepiece" "evaluate" SEED = 42 random.seed(SEED) torch.manual_seed(SEED) torch.cuda.manual_seed_all(SEED) MODEL_NAME = os.environ.get("MODEL_NAME", "Qwen/Qwen2-0....

OpenAI Releases a Research Preview of GPT‑5.3-Codex-Spark: A 15x Faster AI Coding Model Delivering Over 1000 Tokens Per Second on Cerebras Hardware

By Tokenboy February 12, 2026

OpenAI just launched a new research preview called GPT-5.3 Codex-Spark . This model is built for 1 thing: extreme speed. While the standard GPT-5.3 Codex focuses on deep reasoning, Spark is designed for near-instant response times. It is the result of a deep hardware-software integration between OpenAI and Cerebras. The results are game-changing. Spark is 15x faster than the flagship GPT-5.3 Codex. It consistently delivers over 1000 tokens per second . This speed effectively removes the delay between a developer’s thought and the model’s code output. The Hardware: Wafer-Scale Engineering The massive performance jump is powered by the Cerebras Wafer-Scale Engine 3 (WSE-3) . Traditional AI models run on clusters of small GPUs. These GPUs must communicate to each other over cables, which creates a ‘bottleneck.’ This bottleneck slows down the speed of the model. The WSE-3 is different. It is a single, giant chip the size of a whole silicon wafer. Because the entire model lives on 1 p...

Is This AGI? Google’s Gemini 3 Deep Think Shatters Humanity’s Last Exam And Hits 84.6% On ARC-AGI-2 Performance Today

By Tokenboy February 12, 2026

Google announced a major update to Gemini 3 Deep Think today. This update is specifically built to accelerate modern science, research, and engineering. This seems to be more than just another model release. It represents a pivot toward a ‘reasoning mode’ that uses internal verification to solve problems that previously required human expert intervention. The updated model is hitting benchmarks that redefine the frontier of intelligence. By focusing on test-time compute —the ability of a model to ‘think’ longer before generating a response—Google is moving beyond simple pattern matching. https://ift.tt/hGSs67F Redefining AGI with 84.6% on ARC-AGI-2 The ARC-AGI benchmark is an ultimate test of intelligence. Unlike traditional benchmarks that test memorization, ARC-AGI measures a model’s ability to learn new skills and generalize to novel tasks it has never seen. Google team reported that Gemini 3 Deep Think achieved 84.6% on ARC-AGI-2 , a result verified by the ARC Prize Fou...

How to Build a Matryoshka-Optimized Sentence Embedding Model for Ultra-Fast Retrieval with 64-Dimension Truncation

By Tokenboy February 11, 2026

In this tutorial, we fine-tune a Sentence-Transformers embedding model using Matryoshka Representation Learning so that the earliest dimensions of the vector carry the most useful semantic signal. We train with MatryoshkaLoss on triplet data and then validate the key promise of MRL by benchmarking retrieval quality after truncating embeddings to 64, 128, and 256 dimensions. At the end, we save the tuned model and demonstrate how to load it with a small truncate_dim setting for fast and memory-efficient vector search. Check out the FULL CODES here . Copy Code Copied Use a different Browser !pip -q install -U sentence-transformers datasets accelerate import math import random import numpy as np import torch from datasets import load_dataset from torch.utils.data import DataLoader from sentence_transformers import SentenceTransformer, InputExample from sentence_transformers import losses from sentence_transformers.util import cos_sim def set_seed(seed=42): ran...

How to Build an Atomic-Agents RAG Pipeline with Typed Schemas, Dynamic Context Injection, and Agent Chaining

By Tokenboy February 11, 2026

In this tutorial, we build an advanced, end-to-end learning pipeline around Atomic-Agents by wiring together typed agent interfaces, structured prompting, and a compact retrieval layer that grounds outputs in real project documentation. Also, we demonstrate how to plan retrieval, retrieve relevant context, inject it dynamically into an answering agent, and run an interactive loop that turns the setup into a reusable research assistant for any new Atomic Agents question. Check out the FULL CODES here . Copy Code Copied Use a different Browser import os, sys, textwrap, time, json, re from typing import List, Optional, Dict, Tuple from dataclasses import dataclass import subprocess subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "atomic-agents", "instructor", "openai", "pydantic", "requests", "beautifulsoup4", ...

NVIDIA Researchers Introduce KVTC Transform Coding Pipeline to Compress Key-Value Caches by 20x for Efficient LLM Serving

By Tokenboy February 10, 2026

Serving Large Language Models (LLMs) at scale is a massive engineering challenge because of Key-Value (KV) cache management. As models grow in size and reasoning capability, the KV cache footprint increases and becomes a major bottleneck for throughput and latency. For modern Transformers, this cache can occupy multiple gigabytes. NVIDIA researchers have introduced KVTC (KV Cache Transform Coding). This lightweight transform coder compresses KV caches for compact on-GPU and off-GPU storage. It achieves up to 20x compression while maintaining reasoning and long-context accuracy. For specific use cases, it can reach 40x or higher. https://ift.tt/5XGQN03 The Memory Dilemma in LLM Inference In production, inference frameworks treat local KV caches like databases. Strategies like prefix sharing promote the reuse of caches to speed up responses. However, stale caches consume scarce GPU memory. Developers currently face a difficult choice: Keep the cache: Occupies memory needed ...