happy-llm-215M-sft

kmno4zx/happy-llm-215M-sft

Publisherkmno4zx

Download access requirementsModelScope source

Happy-LLM-215M-SFT 本模型为 Happy-LLM 215M SFT模型，基于 Pytorch 框架，模型大小为 215M，训练数据为BelleGroup，总量大概在 350万条对话数据左右。注：本模型仅用于教学目的，请勿将本模型应用于任何生产环境。

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Model details

模型介绍

中文整理

Happy-LLM-215M-SFT

Happy-LLM

本模型为 Happy-LLM 215M SFT模型，基于 Pytorch 框架，模型大小为 215M，训练数据为BelleGroup，总量大概在 350万条对话数据左右。

注：本模型仅用于教学目的，请勿将本模型应用于任何生产环境。

模型下载

可以使用如下方式下载模型

模型使用

运行以下代码即可：

🙏 致谢

核心贡献者

宋志学-项目负责人 (Datawhale成员-中国矿业大学(北京))

邹雨衡-项目负责人 (Datawhale成员-对外经济贸易大学)

朱信忠-指导专家（Datawhale首席科学家-浙江师范大学杭州人工智能研究院教授）

特别感谢

感谢 @Sm1les 对本项目的帮助与支持

感谢所有为本项目做出贡献的开发者们 ❤️

Star History

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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

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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

happy-llm-215M-sft 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 kmno4zx/happy-llm-215M-sft. 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.

Pinned version: 8f84ee9dfee9482ec130ea299e2ea71feab29450. Downloads files only; does not start the model.

Choose a download source

Pinned ModelScope version: 8f84ee9dfee9482ec130ea299e2ea71feab29450. Version identifiers can differ across sites. The alternative source was provided after checking weights and runtime configuration; this is not proof of a successful model run.

A checked, pinned ModelScope source is available for this model. Use the ModelScope download tool.

Choose what to download

Need just one GGUF precision? Load the file manifest and choose a version. Direct file links do not require Python; the command below can download multiple shards together.

Quantization / file

This reads the pinned version's file manifest; no model weights are downloaded.

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

Copy this command, paste it into the terminal, press Enter and wait for installation to finish.

python -m pip install --upgrade huggingface_hub

Already installed? Continue to step 3.

Run the download command

Select the button below, paste into the same terminal and press Enter. The tool downloads all files included in the current command.

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

modelscope download --model "kmno4zx/happy-llm-215M-sft" --revision "8f84ee9dfee9482ec130ea299e2ea71feab29450" --local_dir "./models/kmno4zx--happy-llm-215M-sft--ms-8f84ee9dfee9"

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.