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) This repo contains AWQ model files for mrm8488’s Llama 2 Coder 7B. 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. It is also now supported by continuous batching server vLLM, allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB. AWQ model(s) for GPU inference. GPTQ models for GPU inference, with multiple quantisation parameter options. 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference mrm8488’s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions The creator of the source model…
Модальности:
Генерация текста
Области применения:
Генерация кода
Задача: Генерация текста
Автор: TheBloke
Теги: llama, generated_from_trainer, code, coding, text-generation-inference, 4-bit, awq
Лайков: 3 | Загрузок: 17
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
