7 research outputs found
Zephyr: Direct Distillation of LM Alignment
We aim to produce a smaller language model that is aligned to user intent.
Previous research has shown that applying distilled supervised fine-tuning
(dSFT) on larger models significantly improves task accuracy; however, these
models are unaligned, i.e. they do not respond well to natural prompts. To
distill this property, we experiment with the use of preference data from AI
Feedback (AIF). Starting from a dataset of outputs ranked by a teacher model,
we apply distilled direct preference optimization (dDPO) to learn a chat model
with significantly improved intent alignment. The approach requires only a few
hours of training without any additional sampling during fine-tuning. The final
result, Zephyr-7B, sets the state-of-the-art on chat benchmarks for 7B
parameter models, and requires no human annotation. In particular, results on
MT-Bench show that Zephyr-7B surpasses Llama2-Chat-70B, the best open-access
RLHF-based model. Code, models, data, and tutorials for the system are
available at https://github.com/huggingface/alignment-handbook
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks
based on a few demonstrations or natural language instructions. While these
capabilities have led to widespread adoption, most LLMs are developed by
resource-rich organizations and are frequently kept from the public. As a step
towards democratizing this powerful technology, we present BLOOM, a
176B-parameter open-access language model designed and built thanks to a
collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer
language model that was trained on the ROOTS corpus, a dataset comprising
hundreds of sources in 46 natural and 13 programming languages (59 in total).
We find that BLOOM achieves competitive performance on a wide variety of
benchmarks, with stronger results after undergoing multitask prompted
finetuning. To facilitate future research and applications using LLMs, we
publicly release our models and code under the Responsible AI License
Visualización matemática con realidad aumentada: Cálculo multivariado
En este artículo se muestran las principales características de la visualización matemática y cómo el uso de la tecnología puede ayudar a desarrollar habilidades de visualización en el espacio tridimensional. Específicamente se muestra cómo mejorar el proceso de enseñanza aprendizaje de importantes conceptos de cálculo de varias variables, mediante la visualización 3D de las superficies y sus operaciones en una aplicación móvil y mediante el uso de realidad aumentada. Presentamos una aplicación para dispositivos móviles y web que permite visualizar superficies cuádricas y además cuenta con una serie de actividades de realidad aumentada, cada una con sus objetivos particulares, que pueden usarse para desarrollar actividades dentro y fuera del salón de clase.Iniciativa Novu
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License