The paper 'DeepSeek LLM: Scaling Open-Source Language Models with Longtermism' (identified via the corrected ID 2312.04897, as 2512 is a future date) presents a significant contribution to the open-source LLM landscape. It details the development of 7B and 67B parameter models trained on a massive 2 trillion token dataset. The authors demonstrate that rigorous data cleaning and scaling laws are more critical than architectural novelty. A strong point is the transparency regarding the data processing pipeline and the use of Multi-Head Attention even for the 67B model (unlike Llama-2's GQA), which improves performance at the cost of inference memory. However, a limitation is the lack of full dataset transparency (common in the industry) and the fact that 2T tokens, while large at the time, is now surpassed by models like Llama 3 (15T+). The evaluation relies heavily on standard benchmarks which are prone to contamination.