For edge-side models, we compare Qwen2.5-0.5B, 1.5B, and 3B against established baselines: Qwen2-0.5B/1.5B and Gemma2-2.6B. Qwen2.5-0.5B, 1.5B, and 3B continue to maintain strong performance across nearly all benchmarks. Notably, the Qwen2.5-0.5B model outperforms the Gemma2-2.6B on various math and coding tasks. Subsection 5.2 Instruction-tuned Model  To critically evaluate instruction-tuned models, we adopt a multifaceted approach. Foundational skills and human preferences are assessed using open datasets and benchmarks. Additionally, our detailed in-house evaluations delve deeper into the models'competencies in key areas and multilingualism. A particular focus is placed on assessing long-context capability. The subsequent sections outline the evaluation methods and present the results. Subsubsection 5.2.1 Open Benchmark Evaluation  To comprehensively evaluate the quality of instruction-tuned models, we compile automatic and human evaluation to assess the capabilities and human preference. For the evaluation of basic capabilities, we apply similar datasets in the pre-trained model evaluation, which target on natural language understanding, coding, mathematics, and reasoning. Specifically, we evaluate on MMLU-Pro, MMLU-redux and LiveBench 0831 for general evaluation, GPQA, GSM8K and MATH for science and mathematics, HumanEval, MBPP, MultiPL-E and LiveCodeBench 2305-2409 for coding, IFEval for instruction following. Additionally, we assess the performance of human preference alignment and instruction following by evaluating on benchmarks including MT-Bench and Arena-Hard. Qwen2.5-72B-Instruct & Qwen2.5-Plus We compare Qwen2.5-72B-Instruct and Qwen2.5-Plus to other leading open-weight instrution-tuned models: Llama3.1-70B-Instruct, Llama3.1-405B-Instruct, and our previous 72B version, Qwen2-72B-Instruct. The Qwen2.5-72B-Instruct model delivers exceptional performance, even surpassing the larger Llama-3.1-405B-Instruct in several critical benchmarks including MMLU-redux, MATH, MBPP, MultiPL-E, LiveCodeBench, Arena-Hard and MTBench. Moreover, Qwen2.5-Plus outperforms Qwen2.5-72B-Instruct on 9 out of 13 benchmarks. Qwen2.5-14B/32B-Instruct & Qwen2.5-Turbo The performance of the Qwen2.5-Turbo, Qwen2.5-14B-Instruct, and Qwen2.5-32B-Instruct models is evaluated and compared against baselines of similar sizes. The baselines include GPT4o-mini, Gemma2-27B-IT, and Qwen2-57BA14B-Instruct. The Qwen2.5-32B-Instruct model exhibits superior performance across most tasks when compared to other models of similar size. Notably, our open-weight Qwen2.5-14B-Instruct model delivers competitive results across all benchmarks, rivaling those of GPT-4o-mini. Despite its significantly lower training and inference costs, the Qwen2.5-Turbo model outperforms Qwen2.5-14B-Instruct on eight out of ten benchmarks. This demonstrates that Qwen2.5-Turbo achieves remarkable efficiency and effectiveness, making it a compelling choice for resource-constrained environments. Other Instruction-tuned Models The Qwen2.5-7B-Instruct model significantly outperforms its competitors, Gemma2-9B-IT and Llama3.1-8B-Instruct, across all tasks except IFEval. Notably, Qwen2.5-7B-Instruct exhibits clear advantages in mathematics (MATH: 75.5) and coding (HumanEval: 84.8). For the edge-side instruction models, the Qwen2.5-3B-Instruct model, despite having fewer parameters than both the Phi3.5-mini-instruct and MiniCPM3-4B-Instruct models, surpasses them in mathematics and coding tasks. Additionally, it delivers competitive results in language understanding. The Qwen2.5-1.5B-Instruct and Qwen2.5-0.5B-Instruct models have also seen substantial performance improvements over their previous versions. These enhancements make them particularly well-suited for edge-side applications in highly resource-constrained environments. Subsubsection 5.2.2 In-house Automatic Evaluation  Despite the availability of several open benchmark datasets for evaluation, we believe that these are insufficient to fully capture the capabilities of LLMs. To address this, we have developed a series of in-house datasets designed to assess various aspects of model performance, including knowledge understanding, text generation, coding, and more. These evaluations are conducted in both Chinese and English. In addition, we have specifically evaluated the multilingual performance of instruction-tuned models. English & Chinese Evaluation