All Models

Qwen2.5-72B-Instruct is a flagship 72.7-billion parameter, open-weight model engineered for high-stakes AI production, advanced reasoning, and complex conversational tasks. Optimised with an 80-layer architecture using Grouped Query Attention (GQA), it delivers exceptional performance in coding, mathematics, and structured data tasks like JSON and table processing.

Qwen3-0.6B is a compact 0.6B-parameter causal language model from Alibaba Cloud’s Qwen team. Featuring a hybrid Thinking/Non-Thinking mode, it balances deep reasoning with fast, efficient responses. With support for a 32K token context window, over 100 languages, and native tool-calling via the Qwen-Agent framework, it is well-suited for lightweight chatbots, math/code reasoning, and agentic applications.

Baseline source code for Handwritten Digit Recognition Challenge

Ministral 3 3B Base 2512 is a compact multimodal foundation model from Mistral AI. It combines a 3.4B-parameter language decoder with a 0.4B frozen vision encoder for native image understanding. Distilled from the 24B Mistral Small 3.1 through an iterative “Cascade Distillation” process, it preserves much of its teacher model’s capability while supporting a 256K token context window via YaRN RoPE scaling.

Model for Wine Quality Classification Challenge

A 7B-parameter decoder-only language model developed by Xiaomi. Trained from scratch for mathematical and coding reasoning, it serves as the foundational checkpoint of the MiMo-7B series, providing a strong base for fine-tuning and reinforcement learning.

Model for Wine Quality Classification Challenge

A 3B-parameter long-context instruct model from IBM, finetuned for enhanced instruction following and tool-calling. Supports 12 languages including English, Chinese, Arabic, and Japanese. Built on a dense Transformer with GQA, RoPE, SwiGLU, and 128K context length. Trained using SFT, RL alignment, and model merging techniques for enterprise applications.

extremelypowerfulfromdistrict2

Qwen-Image-2512 is a 20B-parameter text-to-image generation model built on the Multimodal Diffusion Transformer (MMDiT) architecture with a frozen Qwen2.5-VL semantic encoder. It delivers photorealistic image generation, with improved human rendering, natural textures, and accurate text rendering in both English and Chinese for posters, infographics, and slides.

Baseline source code for Wine Quality Classification Challenge

A character animation and replacement video model: give it one character image and a driving video, and it transfers full-body motion and facial expression onto your character in either animation or replacement mode.

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spaceship model 2

A large agentic model built for coding, tool use, search, and office work, reporting strong results on SWE-Bench Verified and other agentic benchmarks.

A 32B instruction-tuned model from Ai2, built for reasoning, coding, and instruction-following, and released as a fully open model with public code, checkpoints, and training data.

A compact 3B model that punches above its size: dual-mode reasoning, six languages, and up to 128k-token context, released fully open with weights, data mixture, and training details.

Baseline source code for Smoker Classification Challenge

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.