Model Hub

Fastest FLUX variant. 4-step generation for near-instant high-quality images. Optimized for speed.

The ability to solve problems is a hallmark of intelligence and has been an enduring goal in AI. AI systems that can create programs as solutions to problems or assist developers in writing programs can increase productivity and make programming more accessible. Recently, pre-trained large language models have shown impressive abilities in generating new codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. However, the evaluation of these models has often been performed in a scattered way on only one or two specific tasks, in a few languages, at a partial granularity (e.g., function) level and in many cases without proper training data. Even more concerning is that in most cases the evaluation of generated codes has been done in terms of mere lexical overlap rather than actual execution whereas semantic similarity (or equivalence) of two code segments depends only on their ``execution similarity'', i.e., being able to get the same output for a given input.

FineFineWeb: A Comprehensive Study on Fine-Grained Domain Web Corpus arXiv: Coming Soon Project Page: Coming Soon Blog: Coming Soon Data Statistics Domain (#tokens/#samples) Iteration 1 Tokens Iteration 2 Tokens Iteration 3 Tokens Total Tokens Iteration 1 Count Iteration 2 Count Iteration 3 Count Total Count aerospace 5.77B 261.63M 309.33M 6.34B 9100000 688505 611034 10399539 agronomy 13.08B 947.41M 229.04M 14.26B 15752828 2711790 649404 19114022 artistic… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/FineFineWeb.