# 54_llm_projs

**Repository Path**: linux2014/54_llm_projs

## Basic Information

- **Project Name**: 54_llm_projs
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-06
- **Last Updated**: 2024-03-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


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### 大模型

### Git pull(拉取)，push(上传)命令整理（详细） 
https://www.cnblogs.com/jpfss/p/12084797.html
https://zhuanlan.zhihu.com/p/613810247?utm_id=0
https://www.cnblogs.com/yinzhi/p/11599997.html
https://www.thepaper.cn/newsDetail_forward_26533310


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### 优质开源项目总结
https://github.com/OpenGithubs
https://open.itc.cn/



https://github.com/OpenEmu/OpenEmu
https://open.itc.cn/?tab=repos
https://open.itc.cn/github/collection/detail?collectionId=01HNEKV7KCN0VKQ8MX8AXSB5T3
https://open.itc.cn/github/collection/detail?collectionId=01HEA0CBBHSW7FGS2G3WCHA4NN


### 实时向量搜索引擎:
https://github.com/vearch/gamma

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gpt4free:
https://blog.csdn.net/qq_35811830/article/details/130517022
https://blog.csdn.net/AI_SHELL/article/details/136370557
GPT-4的白嫖方法及其典型应用
https://zhuanlan.zhihu.com/p/653184649

GPT推出高级数据分析功能
https://mp.weixin.qq.com/s/iVEAGjpWZDwyARjgLLPy_g

用ChatGPT神器Code Interpreter做市场研究和数据分析 - 到底行不行？
https://mp.weixin.qq.com/s/jZCOxlut2HPQf-EyKJFEXA

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大语言模型案例:
https://openai.com/
https://openai.com/blog/chatgpt
https://zhuanlan.zhihu.com/p/667077454?utm_id=0
ChatGPT保姆级教程，一分钟学会使用ChatGPT！
https://juejin.cn/post/7198097078005841980
chatgpt 
文心一言
通意千问
github copilot 
Code GeeX 

学堂在线
比心云
翻墙:自行购买服务或在外网服务器搭建;

大模型:
https://zhuanlan.zhihu.com/p/647220145
https://zhuanlan.zhihu.com/p/676432440
https://zhuanlan.zhihu.com/p/676201455
https://zhuanlan.zhihu.com/p/684544418
https://juejin.cn/post/7314135212354404363
https://www.zhihu.com/question/608820310
https://zhuanlan.zhihu.com/p/630577059
https://www.zhihu.com/question/623672939/answers/updated
https://www.zhihu.com/question/635910343
https://zhuanlan.zhihu.com/p/674925860
https://mp.weixin.qq.com/s?__biz=MzUyOTA5OTcwMg==&mid=2247484879&idx=1&sn=9babf498e543f41ff16b37a3baf63b65&scene=21#wechat_redirect

### 深度学习
https://open.itc.cn/github/collection/detail?collectionId=01HNEM074CN281BV551K33X5C1

### redis技术栈
https://open.itc.cn/github/collection/detail?collectionId=01H162C1X10VD5MEFHR8SB3TA8

---

https://github.com/mlabonne/llm-course
https://github.com/ninehills/blog
https://gitee.com/oschina/awesome-llm


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https://github.com/microsoft/playwright-python
https://github.com/xszyou/Fay
https://github.com/google/gemma.cpp
https://github.com/google/gemma_pytorch
https://github.com/Z4nzu/hackingtool
https://github.com/ByteByteGoHq/system-design-101
https://github.com/WisdomShell/codeshell
https://github.com/thewhiteh4t/seeker
https://github.com/xiangsx/gpt4free-ts
https://github.com/doocs/leetcode
https://github.com/morph-labs/rift
https://github.com/modelscope/facechain
https://github.com/LlamaFamily/Llama-Chinese
https://github.com/ollama/ollama
https://github.com/lllyasviel/Fooocus

https://github.com/karpathy/llama2.c
https://github.com/mlc-ai/mlc-llm
https://github.com/sunner/ChatALL
https://github.com/xtekky/gpt4free


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https://github.com/NaiboWang/EasySpider
https://github.com/AI4Finance-Foundation/FinGPT
https://github.com/krahets/hello-algo

https://github.com/netdata/netdata
https://github.com/isocpp/CppCoreGuidelines

https://github.com/hehonghui/awesome-english-ebooks
https://github.com/geekan/MetaGPT
https://github.com/bradtraversy/50projects50days
https://github.com/vim/vim

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https://github.com/chuyangliu/snake

---
### python

docs/python.md

https://docs.python.org/3.7/tutorial/venv.html

To create a virtual environment, decide upon a directory where you want to place it, and run the venv module as a script with the directory path:

python3 -m venv tutorial-env
This will create the tutorial-env directory if it doesn’t exist, and also create directories inside it containing a copy of the Python interpreter, the standard library, and various supporting files.

Once you’ve created a virtual environment, you may activate it.

On Windows, run:

tutorial-env\Scripts\activate.bat
On Unix or MacOS, run:

source tutorial-env/bin/activate

Activating the virtual environment will change your shell’s prompt to show what virtual environment you’re using, and modify the environment so that running python will get you that particular version and installation of Python. For example:
```
$ source ~/envs/tutorial-env/bin/activate
(tutorial-env) $ python
Python 3.5.1 (default, May  6 2016, 10:59:36)
  ...
>>> import sys
>>> sys.path
['', '/usr/local/lib/python35.zip', ...,
'~/envs/tutorial-env/lib/python3.5/site-packages']
>>>
```

ou can install, upgrade, and remove packages using a program called pip. By default pip will install packages from the Python Package Index, <https://pypi.org>. You can browse the Python Package Index by going to it in your web browser, or you can use pip’s limited search feature:

(tutorial-env) $ pip search astronomy
skyfield               - Elegant astronomy for Python
gary                   - Galactic astronomy and gravitational dynamics.
novas                  - The United States Naval Observatory NOVAS astronomy library
astroobs               - Provides astronomy ephemeris to plan telescope observations
PyAstronomy            - A collection of astronomy related tools for Python.
...
pip has a number of subcommands: “search”, “install”, “uninstall”, “freeze”, etc. (Consult the Installing Python Modules guide for complete documentation for pip.)

You can install the latest version of a package by specifying a package’s name:

(tutorial-env) $ pip install novas
Collecting novas
  Downloading novas-3.1.1.3.tar.gz (136kB)
Installing collected packages: novas
  Running setup.py install for novas
Successfully installed novas-3.1.1.3
You can also install a specific version of a package by giving the package name followed by == and the version number:

(tutorial-env) $ pip install requests==2.6.0
Collecting requests==2.6.0
  Using cached requests-2.6.0-py2.py3-none-any.whl
Installing collected packages: requests
Successfully installed requests-2.6.0
pip show will display information about a particular package:
pip list will display all of the packages installed in the virtual environment:

pip freeze will produce a similar list of the installed packages, but the output uses the format that pip install expects. A common convention is to put this list in a requirements.txt file:

(tutorial-env) $ pip freeze > requirements.txt
(tutorial-env) $ cat requirements.txt
novas==3.1.1.3
numpy==1.9.2
requests==2.7.0
The requirements.txt can then be committed to version control and shipped as part of an application. Users can then install all the necessary packages with install -r:

(tutorial-env) $ pip install -r requirements.txt
Collecting novas==3.1.1.3 (from -r requirements.txt (line 1))
  ...
Collecting numpy==1.9.2 (from -r requirements.txt (line 2))
  ...
Collecting requests==2.7.0 (from -r requirements.txt (line 3))
  ...
Installing collected packages: novas, numpy, requests
  Running setup.py install for novas
Successfully installed novas-3.1.1.3 numpy-1.9.2 requests-2.7.0


删除虚拟环境实例:
python -m venv --clear my_venv_gpt4

清理pip缓存:
pip cache purge


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pip安装的可执行文件都在python/Scripts目录下,也就是python安装目录下的Scripts文件夹内;


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### pip
https://pip.pypa.io/en/stable/getting-started/

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