zinbo

KO | EN

gitlite — search

#python #ai #typescript #llm #go #rust #linux #macos #nodejs #security #ios #openai #mcp #open-source

(★ 22)

Use LLMs to clean your gmail inbox

zinbo 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Zinbo

Welcome to the Zinbo project.

## What is Zinbo?

Zinbo is a powerful tool designed to declutter your Gmail inbox by identifying and filtering out unwanted promotional emails. It leverages advanced language models such as GPT-3, GPT-4, [Llama 2 7B](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF), and [OpenHermes 2.5 Mistral 7B](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF) to intelligently sort through your messages.

https://github.com/Tylerbryy/zinbo/assets/104282235/271c985e-4f4d-47f0-9812-91c03bbf435a

## Prerequisites

- Python 3.7 or higher
- Gmail account
- Google Cloud account with Gmail API enabled
- OpenAI API key
- Llama model (if using Llama)
- OpenHermes model (if using OpenHermes)

## Setup

1. Clone this repository:

```
   git clone https://github.com/Tylerbryy/zinbo.git
   cd zinbo
   ```

2. Set up your Google API credentials:

- Follow the instructions [here](https://developers.google.com/workspace/guides/create-credentials) to create a new OAuth 2.0 Client ID.
   - Download the JSON file and rename it to `credentials.json`.
   - Put `credentials.json` in the `zinbo` directory.

3. Set up your environment variables by copying the `.env.example` file to `.env` and  just set your `OPENAI` key. The `setup.py` will fill out the rest. I left them as a option in case you want to modify the locations:

For macOS/Linux:
   ```
   cp .env.example .env
   ```

For Windows:
   ```
   copy .env.example .env
   ```
   - Edit the `.env` file with your actual values.
   - Follow the instructions [here](https://platform.openai.com/api-keys) to get your OpenAI API key.

4. Run

```bash
         python setup.py
      ```
5. You're done! Run the program!

```bash
         python run.py
      ```

## GPU Acceleration

`llama-cpp-python` supports various hardware acceleration backends. To enable GPU acceleration, set the `CMAKE_ARGS` environment variable before installing with `pip`. Below are instructions for different acceleration options:

#### OpenBLAS
For OpenBLAS support:
```bash
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" 
```
```
pip install llama-cpp-python  --upgrade --force-reinstall --no-cache-dir
```
#### cuBLAS (NVIDIA GPUs)
For cuBLAS support, ensure CUDA is installed and configured:
```bash
$env:CMAKE_ARGS="-DLLAMA_CUBLAS=on"
```
```
pip install llama-cpp-python  --upgrade --force-reinstall --no-cache-dir
```
#### Metal (MPS) for macOS
For Metal support on macOS:
```bash
CMAKE_ARGS="-DLLAMA_METAL=on"
```
```
pip install llama-cpp-python  --upgrade --force-reinstall --no-cache-dir
```
#### CLBlast
For CLBlast support:
```
CMAKE_ARGS="-DLLAMA_CLBLAST=on" 
```
```
pip install llama-cpp-python  --upgrade --force-reinstall --no-cache-dir
```

#### hipBLAS (AMD GPUs)
For hipBLAS / ROCm support on AMD GPUs:
```
CMAKE_ARGS="-DLLAMA_HIPBLAS=on"
```
```
pip install llama-cpp-python  --upgrade --force-reinstall --no-cache-dir
```
## Usage

Run the script:

```
python run.py
```

When prompted, choose the language model client you want to use. The script will then start processing your unread emails.

zinbo

zinbo 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

zinbo

zinbo 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README