semvec/pypi-packages huggingface dataset:
llm-models 0.1.0
A CLI tool to list available LLM models from various providers
License: MIT
Requires Python: >=3.7

# llm_models

![Python](https://img.shields.io/badge/python-3.7+-blue.svg)
![License](https://img.shields.io/badge/License-MIT-yellow.svg)

A simple command-line tool to list available LLM models from various providers (OpenAI, Google, Anthropic, xAI).

## Installation

1. Clone the repository:
```bash
$ git clone git@github.com:ljbuturovic/llm_models.git
$ cd llm_models
```

2. Create a virtual environment:
```bash
$ python3 -m venv venv
$ source venv/bin/activate  # On Windows: venv\Scripts\activate
```

3. Install dependencies:
```bash
$ pip install -r requirements.txt
```

## Usage

The tool requires API keys set as environment variables:
- `OPENAI_API_KEY` for OpenAI
- `GOOGLE_API_KEY` for Google Gemini API, or `GOOGLE_CLOUD_PROJECT` for Vertex AI API
- `ANTHROPIC_API_KEY` for Anthropic
- `XAI_API_KEY` for xAI

### Examples

List OpenAI models:
```bash
$ ./llm_models.py --provider OpenAI
Listing available OpenAI models...
================================================================================
Model: babbage-002
Model: chatgpt-4o-latest
Model: codex-mini-latest
Model: dall-e-2
Model: dall-e-3
Model: davinci-002
Model: gpt-3.5-turbo
...
```

List Google models using Gemini API:
```bash
$ ./llm_models.py --provider GoogleAI
Listing available Google AI Studio models (auto-routed region)...
================================================================================
Model: models/embedding-gecko-001
Model: models/gemini-2.5-pro-preview-03-25
Model: models/gemini-2.5-flash
Model: models/gemini-2.5-pro-preview-05-06
Model: models/gemini-2.5-pro-preview-06-05
```

List Google models using Vertex AI API (with regional endpoint):
```bash
$ ./llm_models.py --provider VertexAI --region us-central1
```

List Anthropic models:
```bash
$ ./llm_models.py --provider Anthropic
Listing available Anthropic models...
================================================================================
Model: claude-haiku-4-5-20251001 (Claude Haiku 4.5)
Model: claude-sonnet-4-5-20250929 (Claude Sonnet 4.5)
Model: claude-opus-4-1-20250805 (Claude Opus 4.1)
Model: claude-opus-4-20250514 (Claude Opus 4)
Model: claude-sonnet-4-20250514 (Claude Sonnet 4)
Model: claude-3-7-sonnet-20250219 (Claude Sonnet 3.7)
Model: claude-3-5-haiku-20241022 (Claude Haiku 3.5)
Model: claude-3-haiku-20240307 (Claude Haiku 3)
Model: claude-3-opus-20240229 (Claude Opus 3)
```

List xAI models:
```bash
$ ./llm_models.py --provider xAI
Listing available xAI models (NOTE: xAI uses aliases, so grok-4 is an acceptable API name, resolving to grok-4-0709 as of Nov. 2025)...
================================================================================
Model: grok-2-1212
Model: grok-2-vision-1212
Model: grok-3
Model: grok-3-mini
Model: grok-4-0709
Model: grok-4-1-fast-non-reasoning
Model: grok-4-1-fast-reasoning
Model: grok-4-fast-non-reasoning
Model: grok-4-fast-reasoning
Model: grok-code-fast-1
Model: grok-2-image-1212

```

## Requirements

- Python 3.7+
- See `requirements.txt` for package dependencies
- tested on Ubuntu 24.04
alpinekit 0.1.0
An ultralight optimisation tracking package
License: MIT
Keywords: benchmarking, metrics, optimization, performance, tracking

# AlpineKit

An ultralight optimisation tracking package for Python. Track metrics, timing and memory usage with minimal overhead.

## Quick Start

```python
import time
from alpinekit import Metric, Track, history

# Define a metric to track
x_val = Metric("x")

# Track iterations with automatic timing and progress bars
x = 1
for i in Track(range(5)):
    time.sleep(0.1)
    x += x + i
    x_val.add(x)  # Add metric value

# View results as DataFrame
history.df

# View summary statistics
history.summary
```

## Installation

**From PyPI:**

```bash
# Core package
pip install alpinekit

# With examples (optional)
pip install alpinekit[examples]
```

**With uv:**

```bash
# Core package
uv pip install alpinekit

# With examples (optional)
uv pip install alpinekit[examples]
```

**From GitHub source:**

```bash
git clone https://github.com/hconnorh/alpinekit.git
cd alpinekit

# With pip
pip install -e ".[examples]"

# With uv
uv sync --extra examples
source .venv/bin/activate
```

**Requirements:** Python >= 3.13

## Key Features

### **Metric** - Track custom values
- `Metric(name)` - Create a metric tracker
- `.add(value)` - Record a value
- `.add_norm(array)` - Track array norm
- `.add_mem(*arrays)` - Track memory usage

### **Track** - Enhanced iteration tracking
- Automatic progress bars (tqdm)
- CPU time per iteration
- Wall-clock time per iteration
- Memory (RSS) tracking per iteration
- Thread-safe operation

### **History** - Access and analyze results
- `history.df` - Get all metrics as a Polars DataFrame
- `history.summary` - Get statistics (mean, std, min, max) for all metrics
- `history.merge_runs()` - Merge results from parallel runs with `run_id` column

Check out the `examples/` directory for basic usage guides and parallel batch processing examples.

## License

This project is licensed under the MIT License. See [LICENSE.md](LICENSE.md) for details.