Qwen AI Statistics

Qwen’s models show a clear upward trend, with newer versions—like the Qwen2-72B-Instruct, which scores 84.2% on MMLU (and 84.2% on the Pro version)—outperforming earlier ones, ranging from tiny 0.5B models (52.4%) to the original 7B (62.4%), while larger models such as the Qwen1.5-110B-Instruct (82.4%) and Qwen2-72B (82.8%) balance scale with progress, crafting a neat, measurable arc of growth across size and iteration.

Qwen, the AI model family, has surged to acclaim, with over 50 million Hugging Face downloads (including 15 million for Qwen1.5-7B, 8 million for Qwen7B, and 10 million in Qwen2’s first month), ranking top 5 on LMSYS Chatbot Arena (72B version with Elo 1285), topping the Open LLM Leaderboard v2 with Qwen2-72B, powering 500+ Hugging Face spaces, 1 million commercial apps via ModelScope, and 300+ cited papers (Qwen1.5-32B), deployed on Alibaba Cloud for 1 million users, boasting 20,000 GitHub stars and 1,000 forks, supporting 200+ community fine-tunes, logging 5 million Qwen2 inferences, hitting 500K mobile deployments (Qwen-1.8B) and 1 million for Qwen2-0.5B, and being accessed by 100K developers for the Qwen1.5-110B preview, with multilingual variants gaining 10K stars, Qwen-VL trained on 1 million image-text pairs, and 50+ contributors—truly a standout in open-source and commercial AI.

The Qwen AI lineup spans a wide range of parameter sizes, from 0.5 billion up to 110 billion, across versions like Qwen, Qwen1.5, and Qwen2, with notable features including the Transformer architecture, SwiGLU activation in the 110B-instruction model, group query attention in Qwen2-72B, and varying layer counts (such as 80 layers in Qwen2-72B-Instruct and 28 in Qwen2-7B-Instruct) for select instruction-tuned variants.

Qwen, a versatile language model that excels across 29 languages, proves its mettle with standout scores like 85.2% on C-Eval for Qwen-72B, 90.2% on C-Eval for Qwen1.5-110B, strong showings in benchmarks such as MultiICL (81.7% for Qwen1.5-72B) and Chinese NLI (88.4% for Qwen2-72B-Instruct), and solid performance in regional tasks like Korean coding (75.8% for Qwen2-72B) and French reasoning (70.4% for Qwen-1.8B), while even smaller models hold their own in various areas, from German HellaSwag to Russian RACE, and only a few benchmarks—like multilingual TriviaQA (55.3% for Qwen2-0.5B) or Chinese HumanEval (55.7% for Qwen2-1.5B)—show room for growth.

Qwen and its family of models are AI powerhouses, chomping through trillions of tokens—from multilingual and synthetic data to code, math, and instruction sets—fine-tuning with millions of commands, aligning with human preferences (via techniques like RLHF and DPO), and packing context lengths up to 128K, with each version outdoing the last in scale, specificity, and versatility.