semvec/pypi-packages huggingface dataset:
Topsis-Pallika-102313055 0.1.1
A Python package for TOPSIS multi-criteria decision making
Requires Python: >=3.7

# Topsis-Pallika-102313055

For: Project-1 (UCS633)  
Submitted by: Pallika  
Roll No: 102313055  

---

## About the Project

Topsis-Pallika-102313055 is a Python library for solving Multiple Criteria Decision Making (MCDM) problems using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

---

## Installation

Install the package using pip:

pip install Topsis-Pallika-102313055

---

## Usage

Run the package from the command line:

topsis sample.csv "1,1,1,1" "+,+,-,+"

---

## Input Format

- CSV file required
- First column: Alternatives
- Remaining columns: Numeric criteria only

---

## Output

The output file includes:
- Topsis Score
- Rank

---

## Example

topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" output.csv

---

## Author

Pallika  
Roll Number: 102313055
wesamoyo 1.0.7
Official SDK for Wesamoyo-293M-MoE: 293M parameter Mixture-of-Experts transformer
Requires Python: >=3.8

```markdown
# Wesamoyo SDK

**Official SDK for Wesamoyo-293M-MoE Transformer Model**  
Professional SDK for loading and training the 293M parameter Mixture-of-Experts transformer.

## Overview
SDK for seamless integration with **Wesamoyo-293M-MoE** transformer model. Developed and maintained by **Houndtid Labs**.

## Features
- **Model Loading**: Load the complete 293M parameter MoE transformer
- **Weight Compatibility**: Works with the 591MB model.safetensors file
- **Hugging Face Native**: Integrated with transformers ecosystem
- **Training Ready**: Ready for training from scratch or fine-tuning
- **African Innovation**: Developed in Uganda by Houndtid Labs

## Installation
```bash
pip install wesamoyo
```

## Quick Start
```python
import wesamoyo

# Load the Wesamoyo-293M-MoE model
model = wesamoyo.load_wesamoyo_293m()

# Or use Hugging Face AutoModel
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
    "HoundtidLabs/wesamoyo-293M-MoE",
    trust_remote_code=True
)
```

## Model Specifications
- **Parameters**: 293 Million total
- **Architecture**: Mixture-of-Experts (64 experts, 6 activated)
- **Context Length**: 16,384 tokens
- **File Size**: 591MB (complete weights)
- **Precision**: BF16 optimized
```
spyops 0.0.1
GIS Analysis, Operations, and Spatial Data Management.
Requires Python: >=3.11
Keywords: geopackage, geometry, gis, spatial, analysis, management

# SPyOPS -- Spatial Operations in Python

GIS Analysis, Operations, and Spatial Data Management.

## Installation

`spyops` is available from the [Python Package Index](https://pypi.org/project/spyops/).


## Python Compatibility

The `spyops` library is compatible with Python 3.11 to 3.14.  Developed and 
tested on **macOS** and **Windows**, should be fine on **Linux** too.


## License

MIT

## Release History

### v0.0.1
* land grab