Chat & support: TheBloke’s Discord server Want to contribute? TheBloke’s Patreon page TheBloke’s LLM work is generously supported by a grant from andreessen horowitz (a16z) — Model creator: Doctor Shotgun — Original model: Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES This repo contains AWQ model files for Doctor Shotgun’s Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES. These files were quantised using hardware kindly provided by Massed Compute. For AutoAWQ inference, please install AutoAWQ 0.1.8 or later. Support via Transformers is also available, but currently requires installing Transformers from Github: pip3 install git+https://github.com/huggingface/transformers.git TGI: I tested version 1.3.3 and it loaded the model fine, but I was not able to get any output back. Further testing/debug is required. (Let me know if you get it working!) AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with…
Модальности:
Генерация текста
Области применения:
Следование инструкциям
Задача: Генерация текста
Автор: TheBloke
Теги: mixtral, mergekit, merge, text-generation-inference, 4-bit, awq
Лайков: 3 | Загрузок: 16
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
