Introducing ππ π’π§πππππ‘: the best public math pre-training dataset with 50B+ tokens! HuggingFaceTB/finemath
Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.
We build the dataset by: π οΈ carefully extracting math data from Common Crawl; π iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.
We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.
We hope this helps advance the performance of LLMs on math and reasoning! π Weβre also releasing all the ablation models as well as the evaluation code.
We applied the same data-driven approach that led to SOTA English performance inπ· FineWeb to thousands of languages.
π₯ FineWeb2 has 8TB of compressed text data and outperforms other multilingual datasets in our experiments.
The dataset is released under the permissive π ODC-By 1.0 license, and the π» code to reproduce it and our evaluations is public.
We will very soon announce a big community project, and are working on a π blogpost walking you through the entire dataset creation process. Stay tuned!
How do I test an LLM for my unique needs? If you work in finance, law, or medicine, generic benchmarks are not enough. This blog post uses Argilla, Distilllabel and π€οΈLighteval to generate evaluation dataset and evaluate models.
[New crazy blog post alert] We are releasing an extensive blog post on the science of creating high quality web-scale datasets, detailing all the steps and learnings that came in our recent 15 trillion tokens π·FineWeb release
Inspired by the distill.pub interactive graphics papers, we settled to write the most extensive, enjoyable and in-depth tech report we could draft on so prepare for a 45-mmin read with interactive graphics and all.
And it's not all, in this article we also introduce πFineWeb-Edu a filtered subset of Common Crawl with 1.3T tokens containing only web pages with very high educational content. Up to our knowledge, FineWeb-Edu out-performs all openly release web-scale datasets by a significant margin on knowledge- and reasoning-intensive benchmarks like MMLU, ARC, and OpenBookQA
We also make a number of surprising observations on the "quality" of the internet it-self which may challenge some of the general assumptions on web data (not saying more, I'll let you draw your conclusions ;)
It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.
This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.
Contamination free code evaluations with LiveCodeBench! π₯οΈ
LiveCodeBench is a new leaderboard, which contains: - complete code evaluations (on code generation, self repair, code execution, tests) - my favorite feature: problem selection by publication date π
This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! π
Is is time for the open-source AI robots revolution π?
With @haixuantao and @Leyo weβve been playing with a low-cost DJI robot controlled by three local open-source AI models (Whisper, Idefics2, Parler-TTS - all Apache2) and orchestrated by Dora-cs.