AWQ quantization: done by stelterlab in INT4 GEMM with AutoAWQ by casper-hansen (https://github.com/casper-hansen/AutoAWQ/) Original Weights by the microsoft. Original Model Card follows: Our training data is an extension of the data used for Phi-3 and includes a wide variety of sources from: 1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code. 2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.). 4. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. Multilingual data constitutes about 8% of our overall data. We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge. We evaluated phi-4 using OpenAI’s SimpleEval and our own internal benchmarks to understand the model’s capabilities, more specifically: MMLU:** Popular…
Модальности:
Генерация текста
Области применения:
Генерация кода Математика Диалог / чат
Задача: Генерация текста
Автор: stelterlab
Теги: phi3, phi, nlp, math, code, chat, conversational, custom_code
Лайков: 4 | Загрузок: 2,641
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
