AIOZ AI

Baseline source code for Smoker Classification Challenge

Model for License Plate Recognition Challenge

QwQ-32B is a 32.5B-parameter causal reasoning model from the Qwen series, post-trained with supervised fine-tuning and reinforcement learning to think explicitly before answering. Despite its mid-range size, it delivers performance competitive with leading reasoning systems such as DeepSeek-R1 and o1-mini, particularly on hard math, coding, and multi-step problems. It supports a native 131,072-token context (with YaRN scaling for inputs beyond 8,192 tokens) and is best driven with non-greedy sampling (Temperature 0.6, TopP 0.95, TopK 20–40).

Model for Spaceship Titanic Prediction Challenge

Model for Melanoma Skin Cancer Classification Challenge

Qwen3-Coder-Next is an open-weight coding model from the Qwen team that activates just 3B of its 80B parameters per token, matching the performance of models 10–20× larger at a fraction of the inference cost. It pairs a hybrid Gated DeltaNet + Gated Attention MoE architecture with a native 262,144-token context, and is purpose-built for agentic coding tasks such as long-horizon reasoning, tool use, and failure recovery, with out-of-the-box support for Claude Code, Qwen Code, Qoder, Kilo, Trae, and Cline.

An agentic coding model that explores codebases, edits across multiple files, and drives software-engineering agents — light enough to run on a single GPU, with a 256k context window and vision support.

Z-Image-Turbo is a 6B-parameter text-to-image diffusion model distilled from the Z-Image foundation model, producing high-fidelity images in only 8 NFEs (Number of Function Evaluations). It delivers sub-second inference latency on H800-class GPUs and fits within 16 GB of VRAM on consumer hardware, while preserving strong photorealism, bilingual (English/Chinese) text rendering, and reliable instruction following.

Phi-4 is Microsoft's 14B-parameter dense decoder-only language model, trained on ~9.8T tokens of synthetic, textbook-quality, and curated web data with a 16K-token context window. It is engineered for reasoning and logic in memory- or latency-constrained deployments, matching or surpassing far larger models on math (MATH: 80.4) and science (GPQA: 56.1) benchmarks. Released under the permissive MIT license with SFT + DPO alignment for instruction following and safety.

Qwen2.5-Omni-7B is an end-to-end multimodal foundation model that perceives text, images, audio, and video while streaming both text and natural speech responses in real time. Built on the novel Thinker–Talker architecture with TMRoPE time-aligned multimodal position embeddings, it delivers state-of-the-art results on OmniBench and matches or surpasses similarly sized single-modality models across speech, vision, and audio reasoning benchmarks. Its end-to-end speech-instruction-following ability rivals its text-input performance on standards such as MMLU and GSM8K.

Dataset for Smoker Classification Challenge

Dataset for License Plate Recognition Challenge

Dataset for Spaceship Titanic Prediction Challenge

Dataset for Melanoma Skin Cancer Classification Challenge

Dataset for Iris Flower Classification Challenge