semvec/pypi-packages huggingface dataset:
gpt-oss-redteam 0.1.3
Simple, reproducible red teaming pipeline for GPT-OSS via Ollama with DeepSeek-based prompt generation
License: MIT
Requires Python: >=3.9

# gpt-oss-redteam

Simple, reproducible red teaming pipeline for GPT‑OSS models via Ollama, with DeepSeek-based adversarial prompt generation.

- DeepSeek API to generate adversarial prompts from high-level HITL prompts (preserves `[insert ...]`).
- Ollama for local GPT‑OSS inference with a fake tools manifest (one string arg: `input`, description: "put all information here").
- Logs every run to JSONL for later analysis (full model JSON when available).
- Minimal analyzer: refusal rate and 95% CI.
- Minimal CLI for end-to-end runs.

## Quickstart

Prereqs:
- Python 3.9+
- Ollama running locally and the model pulled: `ollama pull gpt-oss:20b`
- DeepSeek API key in env: `DEEPSEEK_API_KEY=...`

Install (editable):

```bash
pip install -e .
```

Run the full pipeline with 20 prompts × 100 runs each (2,000 total):

```bash
# Create a starter prompts file you can edit
python -m gpt_oss_redteam.cli init --out prompts.txt

# Edit prompts.txt to include one prompt per line (each may include [insert ...])

# Run pipeline
gpt-oss-redteam all \
  --prompts-file prompts.txt \
  --runs-per-prompt 100 \
  --generation-batch-size 10 \
  --ollama-model gpt-oss:20b \
  --out-dir runs
```

Outputs:
- `runs/<timestamp>/generated_prompts.jsonl` – all generated prompts with their source high-level prompt.
- `runs/<timestamp>/inference.jsonl` – every model run with raw JSON response.
- `runs/<timestamp>/analysis.json` and `analysis.md` – refusal rate and 95% CI.

## Configuration via CLI flags or environment variables

- `DEEPSEEK_API_KEY` – required for prompt generation.
- `DEEPSEEK_BASE_URL` – optional, defaults to `https://api.deepseek.com`.
- `OLLAMA_BASE_URL` – optional, defaults to `http://localhost:11434/v1`.

## Fake tools manifest (simple)

We pass a static set of tool/function definitions so the model thinks it can call tools without consuming context with actual implementations. Every tool takes a single string arg named `input` with the description "put all information here". No tool execution occurs.

## Safety note

This project logs raw model outputs for offline analysis. The provided analyzer only checks for a very simple refusal phrase; deeper evaluation is out of scope for this minimal baseline.

## Paper outline (in `docs/`)
- Brief literature review
- Package pipeline and creation
- Quantified results
- Qualitative analysis of results
- Example problem prompts
- Next steps
crypto-yfinance 0.1.3
Unified cryptocurrency data API like yfinance
License: MIT
Requires Python: >=3.8

# cryptofinance

Created by [Aakash Chavan Ravindranath](https://www.linkedin.com/in/aakashcr/)  
More content: [Medium](https://medium.com/@craakash)

A Python library for unified access to cryptocurrency data, similar to `yfinance`.  
Designed for retail investors and data scientists.

## Features

- `get_price()` – real-time spot price
- `get_history()` – OHLCV historical data
- `search_symbol()` – resolve ticker names
- `get_market_cap()` – historical market cap
- `get_volume()` – historical trading volume
- `plot_price()` – quick chart of price history

## Install

Coming soon via PyPI.

## Usage

```python
from cryptofinance import get_price, get_history, plot_price

print(get_price('btc_usd'))
df = get_history('btc_usd')
plot_price('btc_usd')
```
v2raypy 1.1.2
A Python library to handle and parse v2ray links
License: MIT
Requires Python: >=3.10

# v2raypy

**v2raypy** is a Python library for working with V2Ray configurations and links. It allows you to parse, generate, and manipulate V2Ray nodes (VLESS, VMess, Trojan, Shadowsocks) in both dictionary and link format.

---

## 📦 Installation

```bash
pip install v2raypy
```

---

## 🚀 Getting Started

### Importing

```python
from v2ray import V2ray
```

---

## 📚 Features

- Parse links into config objects: `from_link()`
- Parse dictionaries into config objects: `from_dict()`
- Generate configuration dictionaries: `to_dict()`
- Generate shareable V2Ray links: `gen_link()`

---

## 🧪 Usage Examples

### 1. Load from a V2Ray link

```python
link = "vless://<uuid>@example.com:443?security=tls&type=ws#MyServer"
v = V2ray.from_link(link)
```

### 2. Load from a Python dictionary

```python
config = {
    "tag": "my-vless-node",
    "protocol": "vless",
    "settings": {
        "address": "example.com",
        "port": 443,
        "uuid": "<uuid>"
    },
    "streamSettings": {
        "network": "ws",
        "security": "tls"
    }
}

v = V2ray.from_dict(config)
```

### 3. Export to dictionary

```python
conf_dict = v.to_dict()
```

### 4. Generate a new V2Ray share link

```python
link = v.gen_link()
print(link)
```

---

## 🌐 Supported Protocols

- **VLESS**
- **VMess**
- **Trojan**
- **Shadowsocks**

Each with full support for stream types like TCP, KCP, WebSocket, gRPC, HTTP Upgrade, XHTTP, and TLS/REALITY security layers.

---

## 🛠️ Advanced Configuration

You can manipulate advanced options such as:

- `Mux`
- `SendThrough`
- `StreamSettings` (including complex nested settings like TLS, WS headers, GRPC options)

---

## 📜 Acknowledgements

- [3X-UI](https://github.com/MHSanaei/3x-ui/) (License: **GPL-3.0**): _3x-ui is a user interface for managing and configuring V2Ray, XRay, and related protocols, with features such as a web-based dashboard and advanced configuration management. It simplifies the setup process and provides a graphical interface for managing server configurations, routing, and security settings._