add_special_tokens=False, return_tensors="pt") prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)) outputs = model.generate(inputs, max_length=250, do_s
Code Repository files navigation README Apache-2.0 license Chat UI Find the docs athf.co/docs/chat-ui. A chat interface using open source models, eg OpenAssistant or Llama. It is a SvelteKit app and it powers theHuggingChat app on hf.co/chat. ...
The code below shows how to generate a video with CogVideoX-5B import torch from diffusers import CogVideoXPipeline from diffusers.utils import export_to_video prompt = "Tracking shot,late afternoon light casting long shadows,a cyclist in athletic gear pedaling down a scenic mountain road,windin...
本章主要介绍Hugging Face下的另外一个重要库:Datasets库,用来处理数据集的一个python库。当微调一个模型时候,需要在以下三个方面使用该库,如下。 从Huggingface Hub上下载和缓冲数据集(也可以本地哟!) 使用Dataset.map()预处理数据 加载和计算指标 Datasets库可以很方便的完成上述三个操作,另外在本章中...
# This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """A simple command-line interactive chat demo.""" import argparse import os import platform import shutil from copy import deepcopy ...
自Transformers 4.0.0 版始,我们有了一个 conda 频道:huggingface。 🤗 Transformers 可以通过 conda 依此安装: conda install -c huggingface transformers 要通过 conda 安装 Flax、PyTorch 或 TensorFlow 其中之一,请参阅它们各自安装页的说明。 模型架构 ...
doc-builder build datasets ~/git/datasets/docs/source--build_dir ~/tmp/test-build This will generate MDX files that you can preview like any Markdown file in your favorite editor. To have a look at the documentation in HTML, you need to install node version 14 or higher. Then you can...
T5模型如何使用forward()方法代替model.generate() 对于我的用例,我需要使用 model.forward() 而不是 model.generate() 方法,即代替下面的代码 outs = model.model.generate(input_ids=batch['source_ids'], attention_mask=batch['source_mask'], output_scores=True, max_length=model.model_arguments.max_out...
RLHF works by using a pre-trained LM to generate text, which i s then evaluated by humans by, for example, ranking two model generations for the same prompt. This data is then collected to learn a reward model that predicts a scalar reward given any generated text. The r eward captures...
BigCode 背后的大规模数据去重 目标受众 本文面向对大规模文档去重感兴趣,且对散列 (hashing) 、图 (graph) 及文本处理有一定了解的读者。 动机 老话说得好: 垃圾进,垃圾出 (garbage in, garbage out),把数据处理干净再输入给模型至关重要,至少对大语言模型如此。虽然现在一些明星大模型 (严格来讲,它们很多是 ...