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Gabriel Martín Blázquez

gabrielmbmb

https://gabrielmb.com

AI & ML interests

ML Engineer

Recent Activity

reacted to anton-l's post with 🚀 4 days ago

Introducing 📐𝐅𝐢𝐧𝐞𝐌𝐚𝐭𝐡: the best public math pre-training dataset with 50B+ tokens! https://huggingface.co./datasets/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: 🛠️ carefully extracting math data from Common Crawl; 🔎 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! 🚀 We’re also releasing all the ablation models as well as the evaluation code. https://huggingface.co./collections/HuggingFaceTB/finemath-6763fb8f71b6439b653482c2

updated a dataset 5 days ago

gabrielmbmb/gsm8k-reasoning-paths-combined

upvoted a paper 5 days ago

Qwen2.5 Technical Report

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Articles

How we leveraged distilabel to create an Argilla 2.0 Chatbot

Jul 16

• 32

Organizations

Posts 4

Post

1822

Yesterday @mattshumer released mattshumer/Reflection-Llama-3.1-70B, an impressive model that achieved incredible results in benchmarks like MMLU. The model was fine-tuned using Reflection-Tuning and the dataset used wasn't released, but I created a small recipe with distilabel that allows generating a dataset with a similar output format:

1. We use MagPie 🐦 in combination with https://huggingface.co./meta-llama/Meta-Llama-3.1-70B-Instruct to generate reasoning instructions.
2. We generate a response again using https://huggingface.co./meta-llama/Meta-Llama-3.1-70B-Instruct, but we steer the LLM to generate an specific output format using a custom system prompt. In the system prompt, we instruct the LLM that it will have first to think 💭 and have reflections that will help resolving ambiguities. After that, we instruct the LLM to generate an output based on the previous thinking

In this dataset gabrielmbmb/distilabel-reflection-tuning you can found 5 rows that I generated with this recipe. You can also found the code of the pipeline in the file called reflection.py.

Post

2900

distilabel 1.3.0 is out! This release contains many core improvements and new tasks that help us building argilla/magpie-ultra-v0.1!

Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features!

Check the new release in GitHub: https://github.com/argilla-io/distilabel

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