The model's architecture is extensively documented in the Qwen2.5 technical report and official GitHub repository. It is a dense, decoder-only Transformer utilizing Rotary Position Embedding (RoPE), SwiGLU activation, and RMSNorm. Specifically for the 0.5B variant, the Grouped Query Attention (GQA) configuration is detailed with 14 query heads and 2 key-value heads across 24 layers. The transition from the Qwen2 base is clearly explained, and the model weights are publicly accessible on Hugging Face with clear configuration files.

While the total token count is disclosed (expanded from 7 trillion in Qwen2 to 18 trillion in Qwen2.5), the specific composition breakdown (e.g., percentages of web, code, and math data) is not provided for the general 0.5B base model. Documentation mentions 'massive high-quality domain-balanced training sets' and 'expertly curated' data but lacks a granular public breakdown of sources or specific filtering thresholds, relying on high-level descriptions of data types.

The tokenizer is publicly available and fully documented. It uses Byte-level Byte Pair Encoding (BBPE) with a vocabulary size of 151,643 regular tokens and 3 control tokens, ensuring no 'unknown' words. The vocabulary is shared across all Qwen2.5 model sizes, and the compression rates and multilingual efficiency are verified in the technical report. Tokenizer configuration files (tokenizer.json, vocab.json) are accessible in the official repositories.

The parameter count is precisely stated as 0.49 billion total, with a further breakdown of 0.36 billion non-embedding parameters. As a dense model, all parameters are active during inference, which is explicitly confirmed in the technical documentation. The architectural specifications (layers, hidden dimensions, and attention heads) are clearly mapped to the parameter count.

Information regarding training compute is extremely limited. While the hardware type (NVIDIA A100/H100) is implied by the scale of the project and mentioned in inference benchmarks, the specific GPU hours, total compute budget, and carbon footprint for the 0.5B variant are not disclosed. The technical report focuses on performance metrics rather than resource expenditure.

The model provides scores for standard benchmarks (MMLU, MATH, HumanEval) in its technical report. However, while evaluation code is available on GitHub, the exact few-shot prompts and specific versions for all benchmarks are not consistently detailed for the 0.5B variant. Independent researchers have noted significant performance drops on 'clean' versions of benchmarks released after the model's training cutoff, suggesting potential issues with the reported scores' generalizability.

The model consistently identifies itself as part of the Qwen series developed by Alibaba Cloud. It maintains clear versioning (Qwen2.5-0.5B) and distinguishes between its base and instruction-tuned variants. There are no reported instances of the model claiming to be a competitor's product or misrepresenting its foundational architecture.

The model is released under the Apache 2.0 license, which is a standard, permissive open-source license allowing for commercial use, modification, and distribution. The license is clearly stated on the official GitHub, Hugging Face repository, and in the technical report, with no conflicting proprietary terms found for this specific variant.

VRAM requirements are well-documented for various precisions (BF16, INT8, INT4). Official documentation and third-party tools provide specific memory footprints (e.g., ~0.97GB for BF16 at 1k context) and scaling data for context lengths up to 32,768 tokens. Quantization tradeoffs are also addressed with speed and memory benchmarks provided for GPTQ and AWQ formats.

The model uses a clear versioning scheme (2.5), and a changelog is maintained on the official GitHub. However, there is limited documentation regarding long-term drift or specific weight updates within the 2.5 release cycle. While the transition from 2.0 to 2.5 is documented, the granularity of updates for the 0.5B variant specifically is moderate.