Dayhoff-170M-GRS-SS-122000

microsoft/Dayhoff-170M-GRS-SS-122000

Publisher微软（Microsoft）Verified

Download access requirementsMirror available

Dayhoff 是一个蛋白质序列数据和生成式语言模型的集合体，旨在为蛋白质语言模型研究提供广泛支持。该模型可用于无条件设计蛋白质序列、零样本预测突变对适应度的影响、在序列空间中为结构基序设计支架，以及基于同源序列进行条件生成。 2. 在 ProteinGym 上进行零样本突变效应预测 3. 在 RFDiffusion 和 MotifBench 上为序列空间中的结构基序设计支架

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Model details

模型介绍

中文整理

# Dayhoff 模型卡片

Model use case

Dayhoff 是一个蛋白质序列数据和生成式语言模型的集合体，旨在为蛋白质语言模型研究提供广泛支持。该模型可用于无条件设计蛋白质序列、零样本预测突变对适应度的影响、在序列空间中为结构基序设计支架，以及基于同源序列进行条件生成。

## 主要能力

无条件蛋白质序列设计
在 ProteinGym 上进行零样本突变效应预测
在 RFDiffusion 和 MotifBench 上为序列空间中的结构基序设计支架
使用 Dayhoff-3b-GR-HM 和 Dayhoff-3b-GR-HM-c 进行同源序列条件生成

## 输入输出

*输入：** 单个蛋白质序列或未对齐的多序列比对（MSA）
*输出：** 生成的蛋白质序列或同源蛋白质序列集

运行要求

*硬件要求：**

170M 参数模型：8 张 NVIDIA A100 或 8 张 NVIDIA H100 GPU

3B 参数模型：176 张 NVIDIA H100 GPU

*软件依赖：**

PyTorch 2.7.1

CUDA 12.8 及以上版本

mamba-ssm

causal-conv1d

flash-attn

基本用法

最简单的方式是通过 HuggingFace 接口使用这些模型和数据集。需要安装 PyTorch、mamba-ssm、causal-conv1d 和 flash-attn。

限制

该模型仅应用于生成蛋白质序列或同源蛋白质序列集，不得用于自然语言处理或其他生物序列（如 DNA 序列）。无法保证所有生成的序列都具有真实性。难以生成与任何天然序列没有序列同源性的高质量序列。

License

MIT 许可证

优先采用原作者提供的中文模型卡片；仅有英文说明时由闲社AI翻译整理。重要参数、许可证和商用范围请以原文为准。

你将获得什么

完整仓库方式包含该固定版本中的权重、配置、分词器及说明文件；指定版本方式只下载所选文件或整组分片。多模态模型的投影文件、配置等依赖，请按作者说明配齐。

完整仓库可能包含多个权重格式，因此需要的磁盘空间可能大于单一格式的模型大小。下载文件不等于完成模型部署。

使用前须知

运行环境

运行模型需要的设备、工具与依赖，以原作者说明为准。

授权范围

是否允许商用、修改和分发，请查看模型许可证。

闲社提供资料与镜像下载方法，不代表已运行模型或完成安全审计。下载的代码应先检查，再执行。

Problem with the information or download?

Pinned version · Multiple download options

Dayhoff-170M-GRS-SS-122000 model download

Download the full repository or choose a version or file.

Download tools · More than GGUF

Use the Hugging Face download tool to obtain microsoft/Dayhoff-170M-GRS-SS-122000. Download Safetensors, GGUF, ONNX, configuration and other repository files. Downloading does not mean the tool can run this model.

Pinned-version file information is being completed. Existing downloads remain available, or load the file manifest below.

Choose a mirror or official source, your operating system and download scope below, then copy the command. Install the download tool first if needed.

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Quantization / file

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Choose your operating system

For command-line downloads, follow these steps to get the full repository or selected files. Direct file links do not need these steps.

Open a terminal window

Create and open a folder for the model. In the folder's address bar, enter powershell and press Enter.

If Python is not installed, get it from the official Python website. In the terminal, enter python --version; continue when a version number appears.

First time? Install the download tool

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python -m pip install --upgrade huggingface_hub

Already installed? Continue to step 3.

Run the download command

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Copying does not start a download. Paste and run the command on your device.

Where are the downloaded files?

Open the folder you chose. Look for the models folder. When progress stops and the terminal returns to its prompt without errors, check that all files are present. 'Copied' does not mean 'downloaded'.

No response or an error?

python not found: Install Python and reopen the terminal. On Windows, follow the installer's instructions to enable command-line access.

hf not found: Confirm that step 2 succeeded, then reopen the terminal. If needed, add Python's Scripts folder to your user PATH. Do not repeatedly download scripts.

Network interrupted: Stay in the same folder and run the same command again. The tool checks existing files.

Permission or disk error: Choose a writable folder with enough free space. Do not disable system security protection.

View the full download command

$env:HF_ENDPOINT = "https://huggingface.co"
$env:HF_HUB_DISABLE_IMPLICIT_TOKEN = "1"
$env:HF_HUB_DISABLE_XET = "1"
hf download "microsoft/Dayhoff-170M-GRS-SS-122000" --revision "6a8f5f8ec91c03896fe51c383c0d3746e92dd875" --local-dir "./models/microsoft--Dayhoff-170M-GRS-SS-122000"

Files transfer directly from the source you choose, not through Xianshe servers.

The version is pinned. Repeating the same command checks existing files. Change the source manually if it is slow or unavailable.

Public models do not require submitting keys to Xianshe. Restricted models still require the author's authorization. On Linux / macOS, use python3 instead of python when appropriate.