The Qwen2.5-14B model is explicitly documented as a dense, decoder-only transformer. Detailed architectural specifications are provided in the official technical report and Hugging Face model cards, including the use of Rotary Position Embeddings (RoPE), SwiGLU activation, RMSNorm, and Grouped Query Attention (GQA) with 40 query heads and 8 KV heads. The model consists of 48 layers with a hidden size of 5120. While the pretraining methodology is described as a multi-stage process involving context length scaling (from 4k to 32k/128k), the specific hyperparameters for every training stage are not fully disclosed in a single reproducible document.

Alibaba discloses that the model was trained on 18 trillion tokens, a significant increase from previous versions. However, the exact composition breakdown (e.g., specific percentages of web, code, and academic data) is only provided in general terms. While they mention sourcing from 'high-quality' web data, code (5.5T for specialized variants), and math data, they do not provide a public list of sources or a detailed data-cleaning pipeline for the general 14B variant. The use of synthetic data is acknowledged but not quantified for the base model.

The tokenizer is publicly available via the Hugging Face repository and is well-documented. It uses Byte-level Byte Pair Encoding (BBPE) with a large vocabulary size of 151,646 tokens, which is optimized for multilingual support across 29+ languages. The technical report provides compression rate comparisons against other tokenizers (like Llama), and the vocabulary files are directly inspectable in the 'tokenizer.json' and 'vocab.json' files on the official repo.

The model's parameter counts are clearly stated: 14.7 billion total parameters and 13.1 billion non-embedding parameters. As a dense model, all parameters are active during inference, which is explicitly confirmed in the documentation. The architectural breakdown (layers, heads, hidden dimensions) is fully transparent in the config.json file.

Information regarding the specific training compute is extremely limited. While the scale of the data (18T tokens) implies massive compute requirements, Alibaba has not publicly disclosed the total GPU hours, the specific hardware clusters used (e.g., number of H100s), the training duration, or the estimated carbon footprint. This lack of environmental and resource transparency is a significant gap.

While Alibaba provides extensive benchmark results across MMLU, MATH, and HumanEval in their technical reports and blog posts, they do not provide a unified evaluation repository with the exact prompts and few-shot examples used for every score. Third-party researchers have raised concerns about the reliability of some scores due to potential data overlap with common benchmarks, and the lack of a public 'eval' suite makes independent verification difficult.

The model consistently identifies itself as part of the Qwen series in its system prompts and documentation. It maintains clear versioning (2.5) and distinguishes between its base and instruction-tuned variants. There are no widespread reports of the model claiming to be a competitor's product (e.g., GPT-4) in its default configuration.

The Qwen2.5-14B model is released under the Apache 2.0 license, which is a highly permissive, standard open-source license. The terms are clearly stated in the repository, allowing for commercial use, modification, and distribution without the restrictive revenue-based clauses found in some other 'open' models (like the 3B/72B variants of the same family).

VRAM requirements for various precisions (FP16, INT8, INT4) are well-documented by both the official team and the community. The model card provides guidance on context length scaling and the memory impact of the 128K window. Quantization tradeoffs are discussed in the context of GGUF and AWQ versions available on Hugging Face, though official 'accuracy vs. bit-rate' curves are not provided by the primary developer.

The model uses a clear versioning scheme (Qwen2 -> Qwen2.5). However, there is no public changelog for minor weight updates or a transparent policy for documenting silent 'alignment' updates. While previous versions (Qwen1.5, Qwen2) remain accessible, the process for tracking behavioral drift over time is not formalized for the public.