Despite the quantization process, the model still achieves a remarkable 78.05% accuracy (greedy decoding) on the HumanEval pass@1 metric. Despite the quantization course of, the model nonetheless achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval cross@1 metric. This involves feeding the information into the mannequin and permitting it to learn patterns and relationships. Risk of biases as a result of DeepSeek-V2 is trained on huge amounts of knowledge from the web. DeepSeek described a technique to distribute this data analysis across a number of specialized AI models, decreasing time and power misplaced in knowledge transfer. I was fortunate to work with Heng Ji at UIUC and collaborate with fantastic teams at DeepSeek. Nevertheless, the company’s success challenges the prevailing perception that a brute-force method - piling on more computing power and larger analysis teams - is the only method forward in AI growth. We address these challenges by proposing ML-Agent, designed to effectively navigate the codebase, find documentation, retrieve code, and generate executable code.