semvec/pypi-packages huggingface dataset:
vision-mcp 0.1.0
MCP server for image analysis using Vision Language Models
Requires Python: >=3.10
Keywords: mcp, vision, image-analysis

# Vision MCP

MCP server for image analysis using Vision Language Models.

## Quickstart

1. Install `uv` (Python package manager):
   ```sh
   curl -LsSf https://astral.sh/uv/install.sh | sh
   ```

2. Configure your MCP client (e.g., Claude Desktop):

Go to `Claude > Settings > Developer > Edit Config > claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "Vision": {
      "command": "uvx",
      "args": ["vision-mcp"],
      "env": {
        "OPENAI_API_KEY": "your-api-key",
        "OPENAI_API_BASE": "https://api.openai.com",
        "OPENAI_MODEL": "gpt-4o"
      }
    }
  }
}
```

## Environment Variables

| Variable | Required | Description |
|----------|----------|-------------|
| `OPENAI_API_KEY` | Yes | API key for authentication |
| `OPENAI_API_BASE` | Yes | API base URL |
| `OPENAI_MODEL` | Yes | Model name for vision tasks |

## Available Tools

| Tool | Description |
|------|-------------|
| `analyze_image` | Analyze images using Vision Language Model |

### analyze_image

Analyze and understand image content from files or URLs.

**Parameters:**
- `prompt` (str): The text prompt describing what to analyze
- `image_source` (str): Image URL or local file path

**Supported formats:** JPEG, PNG, WebP

## License

MIT

## Acknowledgments

This project is inspired by [MiniMax-Coding-Plan-MCP](https://github.com/MiniMax-AI/MiniMax-Coding-Plan-MCP) by MiniMax AI.
zai-framework 0.1.6
Advanced Hybrid Transformer Framework with MoE, SSM, and Adaptive Routing
Requires Python: >=3.10
Keywords: deep-learning, transformer, moe, ssm, state-space-model, mixture-of-experts, ai, machine-learning, pytorch, nlp

# ZAI Framework

**Advanced Hybrid Transformer Framework with MoE, SSM, and Adaptive Routing**

[![PyPI version](https://badge.fury.io/py/zai-framework.svg)](https://badge.fury.io/py/zai-framework)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 🔥 Features

- **Hybrid Architecture**: Combines Local/Global Attention + State Space Models (SSM)
- **Mixture of Experts (MoE)**: 4-expert system with intelligent Top-2 routing
- **Adaptive Routing**: Dynamic 4D path selection (depth/width/path/expert)
- **Chain of Thought (CoT)**: Built-in reasoning module
- **Efficient**: 9.4% sparse activation, memory-optimized
- **Easy to Use**: Simple API, pretrained tokenizers included

## 📦 Installation

```bash
pip install zai-framework
```

## 🚀 Quick Start

```python
import zai
from zai.models import IGRIS_1M
from zai.tokenizer import load_pretrained

# Load tokenizer
tokenizer = load_pretrained('zai_bpe_10k')

# Create model
model = IGRIS_1M(vocab_size=tokenizer.get_vocab_size())

# Your code here...
```

## 📊 Models Available

- **IGRIS_1M**: 5.66M parameters, efficient for CPU training

## 📄 License

MIT License

## 👨‍💻 Author

Created by Akik Faraji

**Built with ❤️ and AI assistance**