dustynv/transformers

dustynv

HuggingFace Transformers Docker镜像提供便捷API，支持多种NLP和视觉模型，兼容HuggingFace Hub上大量模型，适用于文本生成等LLM任务，支持多种精度和量化选项。

下载次数: 0状态：社区镜像维护者：dustynv仓库类型：镜像最近更新：2 年前

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transformers

容器 镜像 运行 构建

HuggingFace https://huggingface.co/docs/transformers/index库通过便捷的API支持各种NLP和视觉模型，被许多其他LLM包所使用。在https://huggingface.co/models上有大量与其兼容的模型。

[!NOTE]
如果您希望使用Transformer的集成https://huggingface.co/docs/transformers/main_classes/quantization#bitsandbytes-integration量化（`load_in_8bit/load_in_4bit`）或https://huggingface.co/docs/transformers/main_classes/quantization#autogptq-integration量化，请运行以下容器，这些容器在Transformers基础上包含了相应的库：

https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/auto_gptq（依赖于Transformers）

https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/bitsandbytes（依赖于Transformers）

文本生成基准测试

替换您想要运行的https://huggingface.co/models?pipeline_tag=text-generation&sort=trending（应该是像GPT、Llama等CausalLM模型）

bash
./run.sh $(./autotag transformers) \
   huggingface-benchmark.py --model=gpt2

如果模型仓库是私有的或需要身份验证，请添加--env HUGGINGFACE_TOKEN=<您的访问令牌>

默认情况下，性能测量会生成128个新的输出标记（可以使用--tokens=N设置）

可以使用--prompt='your prompt here'更改提示

精度/量化

使用--precision参数启用量化（选项：fp32 fp16 fp4 int8，默认：fp16）

如果您使用fp4或int8，请运行上面提到的https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/bitsandbytes容器，以便安装bitsandbytes包进行量化。预期通过Transformers的4位/8位量化比FP16慢（但消耗更少内存）- 更多信息请参见https://huggingface.co/docs/transformers/main_classes/quantization。

其他库如https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/exllama、https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/awq和https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/auto-gptq有自定义CUDA内核和更高效的量化性能。

Llama2

首先从[***]请求访问权限
然后创建HuggingFace账户，并请求访问其中一个Llama2模型，如https://huggingface.co/meta-llama/Llama-2-7b-hf（这样您将获得所有Llama2模型的访问权限）
从https://huggingface.co/settings/tokens获取用户访问令牌

bash
./run.sh --env HUGGINGFACE_TOKEN=<您的访问令牌> $(./autotag transformers) \
   huggingface-benchmark.py --model=meta-llama/Llama-2-7b-hf

容器

`transformers`
构建状态
要求	`L4T ['>=32.6']`
依赖项	https://github.com/dusty-nv/jetson-containers/tree/master/packages/build/build-essential https://github.com/dusty-nv/jetson-containers/tree/master/packages/cuda/cuda https://github.com/dusty-nv/jetson-containers/tree/master/packages/cuda/cudnn https://github.com/dusty-nv/jetson-containers/tree/master/packages/build/python https://github.com/dusty-nv/jetson-containers/tree/master/packages/numpy https://github.com/dusty-nv/jetson-containers/tree/master/packages/build/cmake/cmake_pip https://github.com/dusty-nv/jetson-containers/tree/master/packages/onnx https://github.com/dusty-nv/jetson-containers/tree/master/packages/pytorch https://github.com/dusty-nv/jetson-containers/tree/master/packages/pytorch/torchvision https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/huggingface_hub https://github.com/dusty-nv/jetson-containers/tree/master/packages/build/rust
被依赖项	https://github.com/dusty-nv/jetson-containers/tree/master/packages/audio/audiocraft https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/auto_awq https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/auto_gptq https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/awq https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/bitsandbytes https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/bitsandbytes https://github.com/dusty-nv/jetson-containers/tree/master/packages/vit/efficientvit https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/gptq-for-llama https://github.com/dusty-nv/jetson-containers/tree/master/packages/l4t/l4t-diffusion https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/llava https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/mlc https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/mlc https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/mlc https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/mlc https://github.com/dusty-nv/jetson-containers/tree/master/packages/vectordb/nanodb https://github.com/dusty-nv/jetson-containers/tree/master/packages/vit/nanoowl https://github.com/dusty-nv/jetson-containers/tree/master/packages/vit/nanosam https://github.com/dusty-nv/jetson-containers/tree/master/packages/nemo https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/optimum https://github.com/dusty-nv/jetson-containers/tree/master/packages/diffusion/stable-diffusion https://github.com/dusty-nv/jetson-containers/tree/master/packages/diffusion/stable-diffusion-webui https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/tensorrt_llm https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/tensorrt_llm https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/tensorrt_llm https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/tensorrt_llm https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/text-generation-inference https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/text-generation-webui https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/text-generation-webui https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/text-generation-webui https://github.com/dusty-nv/jetson-containers/tree/master/packages/audio/voicecraft https://github.com/dusty-nv/jetson-containers/tree/master/packages/audio/whisperx https://github.com/dusty-nv/jetson-containers/tree/master/packages/audio/xtts
Dockerfile	https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/transformers/Dockerfile
镜像	https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-15, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-12, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-11, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-05, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-15, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-14, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-15, 1.5GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-11, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-12, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-15, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-15, 7.6GB)`
说明	在JetPack5上添加了bitsandbytes和auto_gptq依赖项，用于4位/8位量化

`transformers:git`
构建状态
要求	`L4T ['>=32.6']`
依赖项	https://github.com/dusty-nv/jetson-containers/tree/master/packages/build/build-essential https://github.com/dusty-nv/jetson-containers/tree/master/packages/cuda/cuda https://github.com/dusty-nv/jetson-containers/tree/master/packages/cuda/cudnn https://github.com/dusty-nv/jetson-containers/tree/master/packages/build/python https://github.com/dusty-nv/jetson-containers/tree/master/packages/numpy https://github.com/dusty-nv/jetson-containers/tree/master/packages/build/cmake/cmake_pip https://github.com/dusty-nv/jetson-containers/tree/master/packages/onnx https://github.com/dusty-nv/jetson-containers/tree/master/packages/pytorch https://github.com/dusty-nv/jetson-containers/tree/master/packages/pytorch/torchvision https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/huggingface_hub https://github.com/dusty-nv/jetson-containers/tree/master/packages/build/rust
Dockerfile	https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/transformers/Dockerfile
镜像	https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-15, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-12, 5.9GB)` https://hub.docker.com/r/dustynv/transformers/tags `(2023-12-11, 5.9GB)`
说明	在JetPack5上添加了bitsandbytes和auto_gptq依赖项，用于4位/8位量化

`transformers:nvgpt`
构建状态
要求	`L4T ['>=32.6']`
依赖项	[`build-essential`](https://github.com/dusty-nv/jetson-containers/tree/master/packages/build/build-

镜像拉取方式

您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本，请访问标签列表页面。

轩辕镜像加速拉取命令点我查看更多 transformers 镜像标签

docker pull docker.xuanyuan.run/dustynv/transformers:<标签>

使用方法：

DockerHub 原生拉取命令

docker pull dustynv/transformers:<标签>

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