By reducing model weights to a lower precision, the GGML and GPTQ models — two well-known quantized models — minimize model size and computational needs. HF models load on the GPU, which performs inference significantly more quickly than the CPU. Generally, the model is huge, and you ...
Over 35,000 variations of Meta’s open-source AI model Llama have been shared on Hugging Face since Meta’s first version a year ago, ranging from “quantized and merged models to specialized models in biology and Mandarin,” according to the company. ...
01-ai/Yi-VL-34B · Hugging Face Yi-VL-34B模型托管在Hugging Face上,是全球首个开源的340亿视觉语言模型,代表了人工智能领域的重大进展。它以其双语多模态能力脱颖而出,可以进行英文和中文的多轮文本-图像对话。该模型在图像理解方面表现出色,并在MMMU和CMMMU等基准测试中... 内容导读 Yi-VL-34B模型托管在...
# MaziyarPanahi/Meta-Llama-3-70B-Instruct-GGUF The GGUF and quantized models here are based on meta-llama/Meta-Llama-3-70B-Instruct model ## How to download You can download only the quants you need instead of cloning the entire repository as follows: huggingface-cli download MaziyarPanahi/...
TheHugging Face models hubshows Phi-3 variants are created on a daily basis. In general, the Phi-3 family of models outperforms others in its size class (and adjacent) making it ideal for use cases targeting edge and mobile devices. We added 7 of these models...
You can convert (change the data type or quantize) models using the `convert.py` script. This script takes a Hugging Face repo as input and outputs a model directory (which you can optionally also upload to Hugging Face). For example, to make 4-bit quantized a model, run: ``` python...
First, [download](https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF/blob/main/DuckDB-NSQL-7B-v0.1-q8_0.gguf) the quantized models version of DuckDB-NSQL-7B-v0.1 Downloading the model Alternatively, you can execute the following code: ``` huggingface-cli download motherduc...
Optimum Intel是一个开源库,其针对英特尔硬件对使用 Hugging Face 库构建的端到端流水线进行加速和优化。Optimum Intel实现了多种模型加速技术,如低比特量化、模型权重修剪、蒸馏以及运行时优化。 Optimum Intel在优化时充分利用了英特尔® 先进矢量扩展 512 (英特尔® AVX-512) 、矢量神经网络指令 (Vector Neural ...
description = "EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite model. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while stil...
Quantized Versions through bitsandbytes Using 8-bit precision (int8) # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gem...