SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
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2502.02737
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gabrielmbmb
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10 months ago
gabrielmbmb
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an
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over 1 year ago
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1958
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
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.
gabrielmbmb
posted
an
update
over 1 year ago
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2931
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
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
gabrielmbmb
posted
an
update
over 1 year ago
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3614
Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr 🚀
argilla/magpie-ultra-v0.1
Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models 🤗 We will be improving the dataset in upcoming iterations!
argilla/magpie-ultra-v0.1
Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models 🤗 We will be improving the dataset in upcoming iterations!
gabrielmbmb
posted
an
update
over 1 year ago
Post
2511
⚗️ distilabel 1.2.0 is out and it comes with improved support for structured generation, new tasks for generating datasets for training embedding models, new steps for loading data, MixtureOfAgentsLLM and improved docs.
We would love to see a few new datasets for training embedding models built with distilabel on the Hub! ❤️
We would love to see a few new datasets for training embedding models built with distilabel on the Hub! ❤️