code_master

Astinli/code_master

PublisherAstinli

Download access requirementsModelScope source

CodeMaster：基于 qwen-1.8b-chat 模型微调的大语言模型该模型运用深度学习的自然语言处理技术，通过训练大量的文本数据，学习语言的规律和结构，从而实现对自然语言的理解、生成和推理等功能。本模型基于 qwen-1.8b-chat 模型微调而来，不但可以进行日常对话，内容生成，机器翻译，还加强了其代码能力，能够为用户提供代码上的帮助，同时也支持更复杂的数学问题解答和数据分析任务。此外，该模型在处理特定领域的问题时表现出更高的专业性和准确性，例如在医疗、法律和

Text Chat

View all download instructions Mirror repository documentation and license ↗

Model details

模型介绍

中文整理

CodeMaster：基于 qwen-1.8b-chat 模型微调的大语言模型

简介

模型认知名称：**CodeMaster**

模型作者：人工智障组

该模型运用深度学习的自然语言处理技术，通过训练大量的文本数据，学习语言的规律和结构，从而实现对自然语言的理解、生成和推理等功能。本模型基于 qwen-1.8b-chat 模型微调而来，不但可以进行日常对话，内容生成，机器翻译，还加强了其代码能力，能够为用户提供代码上的帮助，同时也支持更复杂的数学问题解答和数据分析任务。此外，该模型在处理特定领域的问题时表现出更高的专业性和准确性，例如在医疗、法律和金融等领域的应用。通过进一步的训练和优化，模型还能够理解和执行特定的指令，进行逻辑推理，以及参与更高级别的决策过程。

特点

**大规模数据集**：本模型使用大规模数据集训练而成，包括alpaca-en，code-alpaca-en等，对测试集有良好表现
**深度神经网络**：本模型使用深度神经网络（如Transformer）作为核心模型，使得本模型能够捕捉到更复杂的语言规律，从而加强对话能力。
**多任务学习**：本模型可以同时学习多个任务，如文本分类、命名实体识别、问答等，这有助于提高模型的泛化能力。
**实时推理**：本模型可以实时地对输入的文本进行推理，为用户提供即时的回答。

实验环境

modelscope免费实例环境

训练方法

数据集

alpaca-zh

alpaca-en

code-alpaca-en

leetcode-python-en

codefuse-python-en

codefuse-evol-instruction-zh

微调的模型

基于qwen-1.8b-chat，使用LoRA的方式进行微调

Parameters

训练集采样数量：train dataset sample=2000

自我认知任务参数

数据及采样条数：self cognition sample=500

超参数

训练batch size：batch size=4

数据集迭代轮次：num train epochs=12

其余参数默认

微调过程代码

训练效果

结果

Training procedure

Framework versions

SWIFT 1.5.1

Base model information

BaseModel Class QWenLMHeadModel

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

code_master model download

Download the full repository or choose a version or file.

许可待确认

下载入口不代表获得修改、分发或商用授权。使用前请查阅原仓库说明、LICENSE 或联系作者确认；闲社未验证模型运行效果。

查看原作者说明与许可 →

Download tools · More than GGUF

Use the Hugging Face download tool to obtain Astinli/code_master. 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: ad097abf99235c89dbbc25d4698586d3597c99a7. Downloads files only; does not start the model.

Choose a download source

Pinned ModelScope version: ad097abf99235c89dbbc25d4698586d3597c99a7. 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 "Astinli/code_master" --revision "ad097abf99235c89dbbc25d4698586d3597c99a7" --local_dir "./models/Astinli--code_master--ms-ad097abf9923"

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.