semvec/pypi-packages huggingface dataset:
qoder-mcp-server 0.1.3
A custom MCP server for Qoder with file operations and DashScope integration
License: MIT
Requires Python: >=3.13

# Qoder MCP Server

A custom MCP (Model Context Protocol) server for Qoder, providing file operations and DashScope integration.

## Features

- **DashScope Integration**: Tools to check API key and call DashScope generation.
- **File System**: Basic file system operations (e.g., `pwd`).

## Installation

You can install this package directly or run it using `uvx`.

### Using uvx

```bash
uvx qoder-mcp-server
```

### Manual Installation

```bash
pip install qoder-mcp-server
```

## Configuration

Ensure you have the following environment variables set:

- `DASHSCOPE_API_KEY`: Your DashScope API key.

## Usage

This server is intended to be used with an MCP client (like Qoder or Claude Desktop).

To run it locally:

```bash
qoder-mcp-server
```
b62 1.1.5
A lightning-fast, zero-dependency Base62 encoder/decoder for Python
License: MIT
Requires Python: >=3.12
Keywords: base62, encoding, decoding, url-shortening, performance

# b62

![PyPI version](https://img.shields.io/pypi/v/b62.svg)
![Python Version](https://img.shields.io/pypi/pyversions/b62.svg)
![Total downloads](https://static.pepy.tech/badge/b62)
![License](https://img.shields.io/pypi/l/b62.svg)
![Build Status](https://github.com/smirnoffmg/b62/actions/workflows/ci.yml/badge.svg)

*Because the world desperately needed another Base62 library.*

## What 🤔

Converts integers to strings and back. Revolutionary stuff.

- Uses Rust 🦀 (because Python is apparently too slow for basic arithmetic)
- Zero dependencies (we're very proud of this achievement)
- Type hints (for people who forgot what integers look like)

## Install

```bash
pip install b62
```

*Shocking, we know.*

## Usage

```python
import b62

# Turn number into string
encoded = b62.encode(123456789)  # "8M0kX"

# Turn string back into number  
decoded = b62.decode("8M0kX")    # 123456789

# It throws errors when you give it garbage
try:
    b62.decode("not_base62!")
except ValueError:
    print("Surprise! Invalid input breaks things.")
```

## Batch Operations

For when you have *many* numbers to convert:

```python
# Encode multiple integers (in parallel, because waiting is hard)
encoded = b62.encode_batch([1, 62, 123456789])  # ['1', '10', '8M0kX']

# Decode multiple strings (also in parallel)
decoded = b62.decode_batch(['1', '10', '8M0kX'])  # [1, 62, 123456789]
```

## Performance ⚡

It's fast. Here are some numbers to make you feel better about your life choices:

- **Decode**: ~52ns per operation (congratulations, you saved nanoseconds)
- **Encode**: ~90ns per operation (your URL shortener will thank you)
- **Batch operations**: Uses all your CPU cores (because why not)

*Benchmarks run on a machine that probably costs more than your car.* 💸

## API

### `encode(num: int) -> str`
Converts integer to Base62 string. Rocket science.

### `decode(string: str) -> int` 
Converts Base62 string back to integer. PhD not required.

### `encode_batch(nums: list[int]) -> list[str]`
Like `encode()` but for people with lists.

### `decode_batch(strings: list[str]) -> list[int]`
Like `decode()` but for people with more lists.

## Character Set

```
0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
```

*Yes, we counted. There are 62 of them.*

## Use Cases

- URL shortening (because long URLs hurt feelings)
- Database ID obfuscation (security through obscurity, naturally)
- Compact serialization (for when JSON is "too verbose")
- Impressing coworkers with Rust integration

## Development

```bash
make test    # Run tests (they probably pass)
make ci      # Pretend you care about CI
make build   # Build the thing
```

## License

MIT - Because we're not monsters. 😇

---

*Now go forth and encode responsibly.*
pycpv 0.1.3
Checkpoint Versioning - Model and Data versioning for Automated Machine Learning, Training and Finetuning using Git, DVC, and AWS S3
License: MIT
Requires Python: >=3.8
Keywords: ai, aws, checkpoints, dvc, git, ml, s3, versioning

# Model Training Guide

## Setup
1. Install dependencies: `pip install -r requirements.txt`
2. Configure AWS and Bitbucket: `cpv aws-config && cpv bitbucket-config`

## Training
1. Prepare training data in `data/` directory
2. Update metrics logging in `train.py`
3. Run: `python train.py`

## Versioning
1. Upload checkpoint: `cpv model upload --message "Training v1"`
2. List checkpoints: `cpv model list-tags`
3. Revert to version: `cpv model revert --tag v1.0`

## Metrics
- Training metrics logged in `metrics.log`
- Model weights stored in `model.bin`
daget 0.7
Download dataset via DOI or landing page url
License: MIT
Requires Python: >=3.6

# 🗃️ DaGet

Simple utility to download datasets from data respositories.

The goal of this project is to explore machine readable metadata and learn more about writing python packages.

⚠️ __script is in early development and needs testing__ ⚠️ 

## Installation

![PyPI](https://img.shields.io/pypi/v/daget)

to install daget using pip: 

```
pip install daget
```

## Usage

Download dataset via DOI or landing page url:

`daget https://doi.org/10.5878/331q-3p13 ./destination`

or short form doi:

`daget 10.5878/331q-3p13 ./destination`

### Example
```text
$ daget 10.7910/DVN/LFH4H8 ./test
destination:   /home/user/test
landing page:  https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LFH4H8
 355.4KiB  Annex.pdf
 115.6KiB  List of items for measuring conceptions of democracy.csv
 81.1KiB  List of items for measuring conceptions of democracy.xlsx
 11.5KiB  Script - Part 1 Conceptions of democracy.R
 3.0KiB  Script - Part 2 Analyze items used to measure conceptions of democracy.R
 73.3KiB  Spreadsheet - Conceptions of democracy 10-2021.csv
 64.9KiB  Spreadsheet - Conceptions of democracy 10-2021.xlsx
 704.8KiB  downloaded 
```

## Supported data respositories with file metadata
* schema.org/Dataset
  * https://dataverse.harvard.edu
  * https://dataverse.no
  * https://researchdata.se/catalogue
* figshare
  * https://su.figshare.com
  * https://figshare.scilifelab.se
* zenodo 
  * https://zenodo.org

## Alternatives

* [datahugger](https://github.com/J535D165/datahugger/) - wider repository suport

## Improve the script

Adding suport for additional repositories requires test cases and investigation arround how to get file metadata from the landing page.

Please help by testing and reporting [issues](https://github.com/borsna/daget/issues)

## Development

* Check out this repository and open a terminal in this directory
* Install dependecies: `pip install pyproject.toml`
* Run the module without installing it: `python3 -m daget`

## TODO

- [ ] Add error handling
- [x] Check empty destination directory
- [ ] Improve documentation
- [x] Package script for pip