The model is explicitly identified as being derived from the Qwen2.5-14B-Instruct base architecture. GreenNode provides documentation on the transition from the base model to a reasoning-focused variant using the Group Relative Policy Optimization (GRPO) methodology. While the fine-tuning methodology is described in technical blog posts and the model card, the full pre-training procedure of the original base is inherited from Qwen, and the specific architectural modifications for the 'R1' reasoning behavior (such as the implementation of Chain-of-Thought) are documented in the context of the fine-tuning pipeline.

GreenNode discloses that the model was trained on a curated Vietnamese instruction dataset of approximately 55,000 samples. This dataset is described as covering cultural, legal, and educational domains. However, a precise percentage breakdown of the data sources (e.g., web vs. proprietary vs. synthetic) is not provided in a granular format. While the filtering methodology (using Sentence Transformers for linguistic verification) is mentioned, the lack of a comprehensive public breakdown or access to the full raw dataset limits the score to the moderate range.

The model utilizes the standard Qwen2.5 tokenizer, which is publicly accessible and well-documented. The vocabulary size (151,936 tokens) and the BPE-based approach are verified through the configuration files on Hugging Face. The tokenizer's support for Vietnamese is explicitly addressed in the training documentation, where specific reward functions were used to ensure the model maintains linguistic integrity and avoids language mixing during long-form reasoning.

The model is clearly defined as a dense, decoder-only transformer with 14.7 billion total parameters. Unlike Mixture-of-Experts (MoE) models, all parameters are active during inference, which is explicitly stated. The architectural configuration (hidden size, number of layers, and attention heads) is fully transparent via the public config.json file on the Hugging Face repository.

GreenNode has publicly stated that the model was fine-tuned using a cluster of 8 NVIDIA H100 Tensor Core GPUs. While the hardware type and the duration of the refinement process (six months of development) are mentioned, the exact total GPU-hours and the specific carbon footprint or detailed cost breakdown are not provided. This represents moderate transparency regarding the compute resources used for the specific 'R1' fine-tuning phase.

The model's performance is cited against benchmarks like VLMU and VLSP, and it is integrated into the NVIDIA NIM framework, which requires technical validation. However, while the model card provides a 'Quickstart' code snippet for inference, a dedicated, public evaluation repository containing the exact prompts and few-shot examples used for all reported benchmarks is not fully detailed. This makes independent third-party reproduction of the exact stated scores more difficult.

The model demonstrates high identity consistency, correctly identifying itself as a Vietnamese reasoning model developed by GreenNode. It does not exhibit confusion with its base architecture (Qwen) in its system prompts or documentation. The versioning (14B-R1) is clearly maintained across the official page, Hugging Face, and NVIDIA NIM documentation.

The model is released under the Apache-2.0 license, which is a standard, highly permissive open-source license. The terms for commercial use, modification, and distribution are clear and well-understood. There are no conflicting proprietary terms found in the official documentation that override this license.

Hardware requirements are documented through its integration with NVIDIA NIM and general 14B parameter model guidelines. VRAM requirements for standard deployment (approx. 28-30GB for FP16) are inferable, and the model supports quantization (e.g., 4-bit) to run on consumer hardware like the RTX 3090/4090. However, a specific 'accuracy vs. quantization' tradeoff table for this specific Vietnamese reasoning variant is not explicitly provided in the primary documentation.

The model uses a clear naming convention (GreenMind-14B-R1), but there is no publicly accessible, detailed changelog or semantic versioning history (e.g., v1.1, v1.2) that tracks specific weight updates or behavior drift over time. While the release date is known, the lack of a formal version tracking system for future iterations limits the transparency of the model's evolution.