2022 年推出的多个预训练开源模型家族大多遵循这种范例。 BLOOM(BigScience Large Open-science Open-access Multilingual Language Model) BLOOM 是由 BigScience 研究团队推出的一系列模型。BigScience 是一个由 Hugging Face 协调,联合法国的 GENCI 和 IDRIS 组织共同参与的国际合作项目,涵盖了来自 60 个国家、250 ...
Minimalistic large language model 3D-parallelism training Resources Readme License Apache-2.0 license Code of conduct Code of conduct Activity Custom properties Stars 1.9kstars Watchers 47watching Forks 188forks Report repository Releases4 Support Mamba Architecture 🐍Latest ...
large-language-model huggingface semantic-kernel 1个回答 0投票 使用HuggingFace 公共 API 时,您不需要指定端点,它应该与下面的代码一起使用。 仅当您部署 HuggingFace 的 TGI(文本生成推理 API)时才需要 Endpoint IKernelBuilder builder = Kernel.CreateBuilder(); builder.Services.AddHuggingFaceTextGeneration( ...
def tokenize(text, model): words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text) pre_tokenized_text = [word for word, offset in words_with_offsets] encoded_words = [encode_word(word, model)[0] for word in pre_tokenized_text] return sum(encoded_words, []...
Large language model size has been increasing 10x every year for the last few years. This is starting to look like another Moore's Law. We've been there before, and we should know that this road leads to diminishing returns, higher cost, more complexity, and new risks. Expo...
使用 PEFT 库,无需微调模型的全部参数,即可高效地将预训练语言模型 (Pre-trained Language Model,PLM) 适配到各种下游应用。PEFT 目前支持以下几种方法: LoRA: LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS Prefix Tuning: P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across...
使用huggingface的Transformer库进行BERT文本分类代码大语言模型(Large Language Models, LLMs)通常指的是拥有大量参数和训练数据的深度学习模型,它们在处理语言相关的任务时表现出色,大模型也带来了计算资源消耗大、部署成本高等问题,BERT及其变体能够处理更加复杂和
1.A Survey on Integration of Large Language Models with Intelligent Robots: arxiv.org/abs/2404.0922 2.联想阿木:AIPC让AI惠及每一个人: https://mp.weixin.qq.com/s/MK2IRXbNqVytYtQHR8FpEw 3.ICRA 官网: 2024.ieee-icra.org 4.HuggingFace开源 dora 项目: huggingface.co/dora-rs 5.dora-rs 开源...
1.A Survey on Integration of Large Language Models with Intelligent Robots: https://arxiv.org/abs/2404.09228 2.联想阿木:AIPC让AI惠及每一个人: https://mp.weixin.qq.com/s/MK2IRXbNqVytYtQHR8FpEw 3.ICRA 官网: https://2024.ieee-icra.org ...
[2]Jordan Hoffmann, et. al., "Training Compute-Optimal Large Language Models.", https://arxiv.org/abs/2203.15556 [3]Victor Sanh, et. al., "Multitask Prompted Training Enables Zero-Shot Task Generalization", https://arxiv.org/abs...