transformers: https://github.com/huggingface/transformers 的镜像库

代码拉取完成,页面将自动刷新

richgiteeai

Watch

不关注关注所有动态仅关注版本发行动态关注但不提醒动态

1 Star 0 Fork 0

Spark/transformers

代码 Issues 0 Pull Requests 0 Wiki 统计流水线

服务

加入 Gitee

与超过 1400万开发者一起发现、参与优秀开源项目,私有仓库也完全免费 :)

免费加入

已有帐号? 立即登录

文件

新建文件新建 Diagram 文件

main

分支 (954)

标签 (245)

管理

main

v4.57-release

deprecate-warmup-ratio

deprecate-overwrite-output-dir

update-from-pretrained

default-auto

trigger_ci_with_torch_2.9.0.rc_1_on_commit_bb45d36

one_tokenizer

standardize-config

serving-lighteval

rm-bnb-load-in

bump-min-version-of-bitsandbytes

kernel_config

better-check-package

misc-fixes

serve-quantization

bump-accelerate

fix_slow_to_fast

add-deepseek-exp

v4.57.0

v4.56.2

v4.56.1-Vault-Gemma-preview

v4.56.0-Embedding-Gemma-preview

v4.56.1

v4.56.0

v4.55.4

v4.55.3

v4.55.2

v4.55.1

4.55.0-GLM-4.5V-preview

4.55.0-GLM-4.5V-preview-GLM-4.5V-preview

v4.55.0

4.54.1

v4.54-release

v4.54.0

v4.53.3

v4.53.2-Ernie-4.5-preview

v4.53.2-modernbert-decoder-preview

v4.53.2

克隆/下载

HTTPS SSH SVN SVN+SSH 下载ZIP

提示

下载代码请复制以下命令到终端执行

为确保你提交的代码身份被 Gitee 正确识别,请执行以下命令完成配置

git config --global user.name userName 
git config --global user.email userEmail

初次使用 SSH 协议进行代码克隆、推送等操作时,需按下述提示完成 SSH 配置

1 生成 RSA 密钥

2 获取 RSA 公钥内容,并配置到 SSH公钥中

在 Gitee 上使用 SVN,请访问使用指南

使用 HTTPS 协议时,命令行会出现如下账号密码验证步骤。基于安全考虑,Gitee 建议配置并使用私人令牌替代登录密码进行克隆、推送等操作

Username for 'https://gitee.com': userName

Password for 'https://userName@gitee.com': # 私人令牌

新建文件新建 Diagram 文件

分支 954

标签 245

贡献代码

同步代码

创建 Pull Request

了解更多

对比差异通过 Pull Request 同步

同步更新到分支

通过 Pull Request 同步

将会在向当前分支创建一个 Pull
Request,合入后将完成同步

Andrei Panferov FP-Quant NVFP4 and Python 3.9 support (#39... 3256773

20694 次提交

File empty ...

transformers-all-latest-gpu

transformers-doc-builder

transformers-gpu

transformers-intel-cpu

transformers-pytorch-amd-gpu

transformers-pytorch-deepspeed-amd-gpu

transformers-pytorch-deepspeed-latest-gpu

transformers-pytorch-deepspeed-nightly-gpu

transformers-pytorch-gpu

transformers-pytorch-tpu

transformers-pytorch-xpu

transformers-quantization-latest-gpu

README.md

consistency.dockerfile

custom-tokenizers.dockerfile

examples-torch.dockerfile

exotic-models.dockerfile

pipeline-torch.dockerfile

quality.dockerfile

torch-light.dockerfile

README

# Dockers for `transformers`

In this folder you will find various docker files, and some subfolders. 
- dockerfiles (ex: `consistency.dockerfile`) present under `~/docker` are used for our "fast" CIs. You should be able to use them for tasks that only need CPU. For example `torch-light` is a very light weights container (703MiB). 
- subfolders contain dockerfiles used for our `slow` CIs, which *can* be used for GPU tasks, but they are **BIG** as they were not specifically designed for a single model / single task. Thus the `~/docker/transformers-pytorch-gpu` includes additional dependencies to allow us to run ALL model tests (say `librosa` or `tesseract`, which you do not need to run LLMs)

Note that in both case, you need to run `uv pip install -e .`, which should take around 5 seconds. We do it outside the dockerfile for the need of our CI: we checkout a new branch each time, and the `transformers` code is thus updated.

We are open to contribution, and invite the community to create dockerfiles with potential arguments that properly choose extras depending on the model's dependencies! :hugs: