1. Exported to hf format using https://github.com/tloen/alpaca-lora(float32, no 8bit) 2. Exported to ONNX format using optimum library(https://github.com/huggingface/optimum/pull/922)(also see fp32 repo) 3. Loaded vanilla fp32 and then exported to ONNX using optimum library(https://github.com/huggingface/optimum/pull/922) with this: Data The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data. Human life The model is not intended to inform decisions about matters central to human life, and should not be used in such a way. Mitigations We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier. Risks and harms Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard. Use cases LLaMA is a foundational…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: nenkoru
Теги: onnx, llama, endpoints_compatible
Лайков: 3 | Загрузок: 12
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
