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Gabriel Martín Blázquez

gabrielmbmb
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https://gabrielmb.com

AI & ML interests

ML Engineer

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upvoted a paper 1 day ago
Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP
reacted to Kseniase's post with 👍 1 day ago
**15 Agentic Systems and Frameworks of 2024** This year, we started our “AI Agents and Agentic Workflows” series (https://www.turingpost.com/t/AI-Agents) to explore everything about AI agents step by step: all the vocabulary, how they work, and how to build them. The huge interest in this series and the large number of studies conducted on agents showed that it was one of the most popular and important themes of the year. In 2025, most likely, agents will reach new highs – we will be covering that for you. Now, let’s review the agentic systems that have emerged this year. Here is a list of 15 agentic systems and frameworks of 2024: 1. https://huggingface.co./papers/2412.13501 2. https://huggingface.co./papers/2411.03562 3. https://huggingface.co./papers/2408.06292 4. https://huggingface.co./papers/2412.01928 5. https://huggingface.co./papers/2410.08164 6. https://huggingface.co./papers/2408.08435 7. https://huggingface.co./papers/2407.03502 8. https://huggingface.co./papers/2410.18603 9. https://huggingface.co./papers/2410.07484 10. https://huggingface.co./papers/2411.10109 11. https://huggingface.co./papers/2411.01747 12. https://huggingface.co./papers/2410.12375 13. https://huggingface.co./papers/2411.11844 14. https://huggingface.co./papers/2412.14684 15. https://huggingface.co./papers/2410.20424 Thanks for reading Turing Post! Subscribe to receive new posts straight into your inbox -> https://www.turingpost.com/subscribe
upvoted an article 1 day ago
🌁#81: Key AI Concepts to Follow in 2025
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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.

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2909
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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