semvec/pypi-packages huggingface dataset:
hud-vf-gym 0.1.1
HUD environments for Reinforcement Learning through Verifiers
Requires Python: >=3.12

# hud-vf-gym

Verifiers Adapter for HUD environments - bridges [Verifiers](https://github.com/willccbb/verifiers) RL framework with [HUD's MCP infrastructure](https://github.com/hud-evals/hud-pytho) for training and evaluating agents.

### Training Support
Verifiers' GRPOTrainer does not support multimodal training as of now, you can use an [experimental trainer](https://github.com/jdchawla29/verifiers) for single-turn environments (with single prompt image due to [transformer's limitations](https://github.com/huggingface/transformers/pull/36682)). Multi-turn multimodal support is WIP.

## Prerequisites

- Python >=3.12
- HUD API key from [https://app.hud.so](https://app.hud.so)
- Environment variables:
  ```bash
  export HUD_API_KEY="your-api-key"
  export OPENAI_API_KEY="your-key"  # or ANTHROPIC_API_KEY
  ```

## Quick Start

```python
import verifiers as vf

# Load environment with HUD taskset and config
env = vf.load_environment(
    env_id="hud-vf-gym",
    taskset="hud-evals/2048-taskset",  # HuggingFace dataset
    config_path="./configs/2048.yaml",  # Environment config
    num_tasks=10
)
```

## Documentation

For comprehensive usage, examples, and configuration details, see:
**[hud-python/rl README](https://github.com/hud-evals/hud-python/tree/main/rl)**

The main documentation covers:
- Running evaluations with various models
- Training agents with GRPO
- Creating custom environments and configs
- Dataset format and creation
- Action mappings and tool configuration
- Troubleshooting guide

## License

MIT
catmemes 0.2.1
Retrieve famous cat memes (ascii and image)
Requires Python: >=3.10

# catmeme retriever

In the long history of cat memes, people who have too much spare time (like me) do need a curated collection of good ones.

### Installation
```bash
pip install catmemes
```
Make sure to set terminal font as [cica](https://github.com/miiton/Cica) to better support CJK alignment in ascii art.


## CLI ascii cats
Adopt four ascii cats:
```
　　　　 ∧___∧　　　   ∧___∧　　　　　　　　　　            ∧  ∧___
　　　　(　´∀` )　　　( ・∀・)　　　　　　　　　　　　　　  /(*ﾟーﾟ)./\
　　　　(　　  )　　  ( 　　 )　　　　　　　　　∧ ∧　 　  /|￣U U￣|\/
　　　  |  |  | 　 　|  |  | 　　　 ～′￣￣(,,ﾟДﾟ)　 　  |　 　  |/
　　　　(___)__）　　 (___)__)　　　　  UU￣U U           ￣￣￣￣

        Mona        Moralar          Giko             Shii
```
The full collection of derivatives is available [here](https://nonexistentfandomsfandom.neocities.org/AAcats/cast).


### Usage
Adopt a cat by name
```bash
ascii-cat list         # list all cat names
ascii-cat [cat_name]   # you guys have too much spare time ~
```

## Iconic cat memes

Search for the most relevant meme from a self-collected dataset

<p align="left">
  <img src="demo_1.gif" alt="bike cat" width="350"/>
  <img src="demo_0.gif" alt="Nyan Cat" width="350"/>
</p>

### Usage
Create conda environment:
```bash
conda create -n catmemes python=3.13
conda activate catmemes
pip install -r requirements.txt
```

Start backend and frontend by running the following two commands in `backend` and `frontend` folder:
```bash
uvicorn app:app --reload --port 8000
npm run dev
```

Find the history of each meme [here](https://knowyourmeme.com/).


## Acknowlegement

Partially inspired by this [Youtube video](https://www.youtube.com/watch?v=PKdUvW8fMj0). In tribute to the iconic feline celebrities who have brought joy to millions.

And I miss my cat *kupo*.

<!-- **TODOs**
- [ ] add google trend index when I have the API access -->
handy-nn 0.0.6
Delightful and useful neural networks models, including OrdinalRegressionLoss, etc.
Requires Python: >=3.7
Keywords: neural-networks, pytorch, machine-learning, ordinal-regression, models

[![](https://github.com/kaelzhang/handy-nn/actions/workflows/python.yml/badge.svg)](https://github.com/kaelzhang/handy-nn/actions/workflows/python.yml)
[![](https://codecov.io/gh/kaelzhang/handy-nn/branch/main/graph/badge.svg)](https://codecov.io/gh/kaelzhang/handy-nn)
[![](https://img.shields.io/pypi/v/handy-nn.svg)](https://pypi.org/project/handy-nn/)
[![](https://img.shields.io/pypi/l/handy-nn.svg)](https://github.com/kaelzhang/handy-nn)

# handy-nn

Delightful and useful neural networks models, including OrdinalRegressionLoss, etc.

## Install

```sh
$ pip install handy-nn
```

## Usage

```py
from handy_nn import OrdinalRegressionLoss

# Initialize the loss function
num_classes = 5
criterion = OrdinalRegressionLoss(num_classes)

# For training
logits = model(inputs)  # Shape: (batch_size, 1)
loss = criterion(logits, targets)
loss.backward()  # shape: torch.Size([])

# To get class probabilities
probas = criterion.predict_probas(logits)  # Shape: (batch_size, num_classes)
```

### Shapes

Variable | Shape
-------- | ----
`logits` | `(batch_size, 1)`
`targets` | `(batch_size,)` or one-hot `(batch_size, num_classes)`
`loss` | `torch.Size([])`
`probas` | `(batch_size, num_classes)`

# APIs

## OrdinalRegressionLoss(num_classes, learn_thresholds=True, init_scale=2.0)

- **num_classes** `int`: Number of ordinal classes (ranks)
- **learn_thresholds** `bool=True`: Whether to learn threshold parameters or use fixed ones, defaults to `True`.
- **init_scale** `float=2.0`: Scale for initializing thresholds, defaults to `2.0`

Creates the loss function for ordinal regression.

The goal of [ordinal regression](https://en.wikipedia.org/wiki/Ordinal_regression) is to model the relationship between one or more independent variables and an ordinal dependent variable. It predicts the probability that an observation falls into a specific ordinal category or a category higher than a certain threshold. This is particularly useful in fields like social sciences, medicine, and customer surveys where outcomes are often ordinal.

## TrendAwareLoss()

```py
criterion = TrendAwareLoss()
loss = criterion(logits, targets)
loss.backward()
```

`TrendAwareLoss` penalizes "too-early / too-late" misclassification inside a label segment more heavily by multiplying per-sample cross-entropy with the segment remaining-length weight.

This loss function is useful for those situations where misclassification leads to an indirect loss, such as financial trading, etc.

## License

[MIT](LICENSE)
vc-utils 0.0.9
Vulcan Coalition utililities
Requires Python: >=3.9

# vulcan-utils

Vulcan python utility

## Features

### Linkage APIs

-   Linkage API for various services

### Collab APIs

-   Collab API for various services

## Installation

```bash
pip install vulcan-utils
```

## Usage

```python
from vulcan_utils import example_function
```
chordsbert 25.8.27
BERT for working with MIDI chords
Requires Python: >=3.6
Keywords: MIDI, music, music ai, MIDI chords, Chords BERT, BERT, music chords, chords progressions, artificial intelligence, AI

# Chords BERT
## BERT for working with MIDI chords

<img width="1024" height="1024" alt="Chords-BERT-Artwork-1" src="https://github.com/user-attachments/assets/6f8dac84-87f8-4aaf-bfab-06c04bff204f" />

***

### Project Los Angeles
### Tegridy Code 2025