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: Upstage — Original model: Llama 2 70B Instruct v2 This repo contains AWQ model files for Upstage’s Llama 2 70B Instruct v2. 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 Upstage’s original unquantised fp16 model in pytorch format, for…
Модальности:
Генерация текста
Области применения:
Следование инструкциям
Задача: Генерация текста
Автор: TheBloke
Теги: llama, upstage, llama-2, instruct, instruction, en, text-generation-inference, 4-bit
Лайков: 3 | Загрузок: 17
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
