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this project is super cool, i expect this is how structured evals with llms will go. https://t.co/oC7tfn3V6t
Brilliant work from @PyTorch team on releasing torchtune ✨ Running fine-tuning with single command. Single-GPU recipes expose a number of memory optimizations that aren't available in the distributed versions. torchtune is built with extensibility and usability, focussing on… https://t.co/gy6H9AAT1G https://t.co/Uk27ijyOms
Torchtune is shipping with LM Evaluation Harness integration for evals of finetunes! Excited to see lm-eval adopted by the ecosystem—evals are crucial. we (@lintangsutawika and I) are looking forward to collaborating with the torchtune team to build out deeper integration! https://t.co/soWzJkVDoG
The recent alpha release of the torchtune library by PyTorch marks a significant advancement in the field of Large Language Models (LLMs). Torchtune, designed as a PyTorch-native library, facilitates the fine-tuning of LLMs with features that promote memory efficiency and integration with popular tools. The library supports single-command fine-tuning and integrates with the LM Evaluation Harness, enhancing the evaluation of fine-tuned models. This release was accompanied by multiple endorsements from the tech community, highlighting its ease of use and potential impact on LLM development and evaluation.
