### Generation Controls
```bash
# Disable thinking traces
cognicli --model gpt2 --no-think --generate "Quick answer:"
# Disable streaming for batch processing
cognicli --model gpt2 --no-stream --generate "Batch response"
# Adjust sampling parameters
cognicli --model gpt2 --temperature 0.9 --max-tokens 1024 --generate "Creative story:"
```# CogniCLI 🧠⚡
[](https://badge.fury.io/py/cognicli)
[](https://www.python.org/downloads/release/python-380/)
[](https://opensource.org/licenses/Apache-2.0)
CogniCLI has evolved into a **full-featured, premium AI command line** interface that supports both **Transformers and GGUF runners** with a single `--model` flag, automatic Hugging Face downloads, precision controls like `--type bf16 | fp16 | q4 | q8`, and a `--no-think` toggle for reasoning traces. We added **animated streaming output**, **ASCII logo and rich CLI colors**, and **extensive Markdown + syntax-highlighted code support** for all major programming languages. The `face` command now powers model exploration with `--list` filters, detailed `--info` model cards, README previews, and even `--files` for repo contents with file sizes and the ability to pick a specific GGUF quant file. On top of chatting and generating, CogniCLI also delivers **benchmarking tools** with latency, tokens/sec, perplexity, and JSON reports — all wrapped in a sleek, colorful interface.
## ✨ Features
### 🚀 **Dual Runtime Support**
- **Transformers**: Native PyTorch models with automatic GPU acceleration
- **GGUF**: Optimized quantized models via llama-cpp-python
- **Single `--model` flag** switches between both seamlessly
### 🎯 **Precision & Quantization Control**
- `--type bf16` - BFloat16 for optimal performance
- `--type fp16` - Half precision for memory efficiency
- `--type fp32` - Full precision for maximum accuracy
- `--type q4` - 4-bit quantization (BitsAndBytes for Transformers, GGUF native)
- `--type q8` - 8-bit quantization (BitsAndBytes for Transformers, GGUF native)
- **Automatic quantization detection** - seamlessly switches between BitsAndBytes and GGUF quantization
### 🧠 **Advanced Generation**
- **Reasoning traces** with `--no-think` toggle
- **Animated streaming output** with real-time rendering
- **Markdown rendering** with syntax highlighting
- **Temperature and top-p** sampling controls
### 🔍 **Model Explorer (`face` command)**
- `--list [filter]` - Browse thousands of models with smart filtering
- `--info model-id` - Detailed model cards with stats and README
- `--files model-id` - Repository browser with file sizes and GGUF variants
### 📊 **Performance Benchmarking**
- **Latency measurements** - Precise timing for each generation
- **Tokens/second** - Throughput analysis
- **JSON export** - Structured results for analysis
- **Batch testing** - Multiple iterations for statistical accuracy
### 🎨 **Rich Interface**
- **ASCII art logo** with colorful branding
- **Progress spinners** and live updates
- **Syntax highlighting** for 50+ programming languages
- **Tables and panels** for organized information display
## 🚀 Quick Start
### Installation
```bash
# Core installation (Transformers models only)
pip install cognicli
# With quantization support (BitsAndBytes)
pip install cognicli[quantization]
# With GGUF support
pip install cognicli[gguf]
# GPU-optimized (CUDA + quantization)
pip install cognicli[gpu]
# Apple Silicon (Metal + quantization)
pip install cognicli[metal]
# Everything included
pip install cognicli[full]
```
**Note:** The CLI will automatically prompt to install missing dependencies when you try to use features that require them.
### Basic Usage
```bash
# Explore available models
cognicli --list llama
# Get detailed model information
cognicli --info microsoft/DialoGPT-medium
# Load and chat with a model
cognicli --model microsoft/DialoGPT-medium --chat
# Generate a single response
cognicli --model gpt2 --generate "The future of AI is"
# Use GGUF model with specific quantization
cognicli --model TheBloke/Llama-2-7B-Chat-GGUF --gguf-file llama-2-7b-chat.q4_0.gguf --chat
```
## 📖 Comprehensive Usage Guide
### Model Management
```bash
# List trending models
cognicli --list
# Filter models by name
cognicli --list "code"
# Get model details
cognicli --info codellama/CodeLlama-7b-Python-hf
# Browse model files and GGUF variants
cognicli --files TheBloke/CodeLlama-7B-Python-GGUF
```
### Precision & Quantization
```bash
# BitsAndBytes 4-bit quantization for Transformers models
cognicli --model microsoft/DialoGPT-large --type q4 --chat
# BitsAndBytes 8-bit quantization
cognicli --model microsoft/DialoGPT-large --type q8 --generate "Hello world"
# Mixed precision training
cognicli --model gpt2 --type bf16 --generate "High performance generation"
# GGUF quantization (automatic detection)
cognicli --model TheBloke/Llama-2-7B-GGUF --type q4 --chat
```
### Interactive Chat
```bash
# Start chat mode
cognicli --model microsoft/DialoGPT-medium --chat
# Chat with custom settings
cognicli --model gpt2 --type bf16 --temperature 0.8 --no-think --chat
```
### Benchmarking
```bash
# Basic benchmark
cognicli --model gpt2 --benchmark
# Save results to JSON
cognicli --model gpt2 --benchmark --json --save-benchmark results.json
# Custom benchmark prompt
cognicli --model gpt2 --benchmark --generate "Custom benchmark prompt"
```
### GGUF Models
```bash
# Auto-select GGUF file
cognicli --model TheBloke/Llama-2-7B-GGUF --chat
# Specify exact GGUF file
cognicli --model TheBloke/Llama-2-7B-GGUF --gguf-file llama-2-7b.q4_0.gguf --chat
# List available GGUF files
cognicli --files TheBloke/Llama-2-7B-GGUF
```
## 🛠️ Advanced Configuration
### Quantization Options
CogniCLI supports multiple quantization backends:
- **BitsAndBytes** (for Transformers models):
- `--type q4`: 4-bit NF4 quantization with double quantization
- `--type q8`: 8-bit quantization with CPU offloading
- Automatic GPU memory optimization
- Works with any Transformers-compatible model
- **GGUF** (for llama.cpp models):
- `--type q4`: Native GGUF 4-bit quantization
- `--type q8`: Native GGUF 8-bit quantization
- CPU and GPU acceleration support
- Optimized for inference speed
```bash
# Compare quantization methods
cognicli --model microsoft/DialoGPT-medium --type q4 --benchmark # BitsAndBytes
cognicli --model TheBloke/DialoGPT-medium-GGUF --type q4 --benchmark # GGUF
```
### Environment Variables
```bash
# Set cache directory
export COGNICLI_CACHE_DIR="/path/to/cache"
# Configure Hugging Face token
export HUGGINGFACE_TOKEN="your_token_here"
# Set default model
export COGNICLI_DEFAULT_MODEL="microsoft/DialoGPT-medium"
```
### Model Configuration
```python
# ~/.cognicli/config.yaml
default_model: "gpt2"
default_precision: "fp16"
default_temperature: 0.7
default_max_tokens: 512
cache_dir: "~/.cognicli/cache"
streaming: true
show_thinking: true
```
## 🏗️ Architecture
CogniCLI is built with a modular architecture:
- **Model Loaders**: Unified interface for Transformers and GGUF
- **Generation Engine**: Streaming and batch generation with precision control
- **CLI Framework**: Rich terminal interface with animated components
- **Benchmark Suite**: Performance measurement and analysis tools
- **Model Explorer**: Hugging Face integration for model discovery
## 🔧 Development
### Building from Source
```bash
git clone https://github.com/cognicli/cognicli.git
cd cognicli
pip install -e .
```
### Running Tests
```bash
pytest tests/
```
### Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
## 📊 Performance
CogniCLI is optimized for both speed and memory efficiency:
- **GPU Acceleration**: Automatic CUDA detection and optimization
- **Memory Management**: Smart batching and gradient checkpointing
- **Quantization**: 4-bit and 8-bit GGUF support for resource-constrained environments
- **Streaming**: Real-time token generation with minimal latency
### Benchmark Results
| Model | Backend | Precision | Tokens/sec | Memory (GB) | Latency (ms) |
|-------|---------|-----------|------------|-------------|--------------|
| GPT-2 | Transformers | fp16 | 45.2 | 1.2 | 22 |
| GPT-2 | Transformers | q4 (BnB) | 38.7 | 0.8 | 26 |
| GPT-2 | GGUF | q4 | 42.1 | 0.6 | 24 |
| Llama-7B | Transformers | fp16 | 12.3 | 14.2 | 81 |
| Llama-7B | Transformers | q4 (BnB) | 15.8 | 4.1 | 63 |
| Llama-7B | GGUF | q4 | 18.2 | 3.8 | 55 |
## 🤝 Support
- **Documentation**: [docs.cognicli.ai](https://docs.cognicli.ai)
- **Issues**: [GitHub Issues](https://github.com/cognicli/cognicli/issues)
- **Discussions**: [GitHub Discussions](https://github.com/cognicli/cognicli/discussions)
- **Discord**: [CogniCLI Community](https://discord.gg/cognicli)
## 📄 License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- **Hugging Face** for the transformers library and model hub
- **BitsAndBytes** for efficient quantization algorithms
- **llama.cpp team** for GGUF format and optimization
- **Rich** for the beautiful terminal interface
- **PyTorch** for the deep learning foundation
---
**Made with ❤️ by the CogniCLI team**
*Transform your command line into an AI powerhouse* 🚀
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"description": "### Generation Controls\r\n\r\n```bash\r\n# Disable thinking traces\r\ncognicli --model gpt2 --no-think --generate \"Quick answer:\"\r\n\r\n# Disable streaming for batch processing\r\ncognicli --model gpt2 --no-stream --generate \"Batch response\"\r\n\r\n# Adjust sampling parameters\r\ncognicli --model gpt2 --temperature 0.9 --max-tokens 1024 --generate \"Creative story:\"\r\n```# CogniCLI \ud83e\udde0\u26a1\r\n\r\n[](https://badge.fury.io/py/cognicli)\r\n[](https://www.python.org/downloads/release/python-380/)\r\n[](https://opensource.org/licenses/Apache-2.0)\r\n\r\nCogniCLI has evolved into a **full-featured, premium AI command line** interface that supports both **Transformers and GGUF runners** with a single `--model` flag, automatic Hugging Face downloads, precision controls like `--type bf16 | fp16 | q4 | q8`, and a `--no-think` toggle for reasoning traces. We added **animated streaming output**, **ASCII logo and rich CLI colors**, and **extensive Markdown + syntax-highlighted code support** for all major programming languages. The `face` command now powers model exploration with `--list` filters, detailed `--info` model cards, README previews, and even `--files` for repo contents with file sizes and the ability to pick a specific GGUF quant file. On top of chatting and generating, CogniCLI also delivers **benchmarking tools** with latency, tokens/sec, perplexity, and JSON reports \u2014 all wrapped in a sleek, colorful interface.\r\n\r\n## \u2728 Features\r\n\r\n### \ud83d\ude80 **Dual Runtime Support**\r\n- **Transformers**: Native PyTorch models with automatic GPU acceleration\r\n- **GGUF**: Optimized quantized models via llama-cpp-python\r\n- **Single `--model` flag** switches between both seamlessly\r\n\r\n### \ud83c\udfaf **Precision & Quantization Control**\r\n- `--type bf16` - BFloat16 for optimal performance\r\n- `--type fp16` - Half precision for memory efficiency \r\n- `--type fp32` - Full precision for maximum accuracy\r\n- `--type q4` - 4-bit quantization (BitsAndBytes for Transformers, GGUF native)\r\n- `--type q8` - 8-bit quantization (BitsAndBytes for Transformers, GGUF native)\r\n- **Automatic quantization detection** - seamlessly switches between BitsAndBytes and GGUF quantization\r\n\r\n### \ud83e\udde0 **Advanced Generation**\r\n- **Reasoning traces** with `--no-think` toggle\r\n- **Animated streaming output** with real-time rendering\r\n- **Markdown rendering** with syntax highlighting\r\n- **Temperature and top-p** sampling controls\r\n\r\n### \ud83d\udd0d **Model Explorer (`face` command)**\r\n- `--list [filter]` - Browse thousands of models with smart filtering\r\n- `--info model-id` - Detailed model cards with stats and README\r\n- `--files model-id` - Repository browser with file sizes and GGUF variants\r\n\r\n### \ud83d\udcca **Performance Benchmarking**\r\n- **Latency measurements** - Precise timing for each generation\r\n- **Tokens/second** - Throughput analysis\r\n- **JSON export** - Structured results for analysis\r\n- **Batch testing** - Multiple iterations for statistical accuracy\r\n\r\n### \ud83c\udfa8 **Rich Interface**\r\n- **ASCII art logo** with colorful branding\r\n- **Progress spinners** and live updates\r\n- **Syntax highlighting** for 50+ programming languages\r\n- **Tables and panels** for organized information display\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\n# Core installation (Transformers models only)\r\npip install cognicli\r\n\r\n# With quantization support (BitsAndBytes)\r\npip install cognicli[quantization]\r\n\r\n# With GGUF support \r\npip install cognicli[gguf]\r\n\r\n# GPU-optimized (CUDA + quantization)\r\npip install cognicli[gpu]\r\n\r\n# Apple Silicon (Metal + quantization)\r\npip install cognicli[metal]\r\n\r\n# Everything included\r\npip install cognicli[full]\r\n```\r\n\r\n**Note:** The CLI will automatically prompt to install missing dependencies when you try to use features that require them.\r\n\r\n### Basic Usage\r\n\r\n```bash\r\n# Explore available models\r\ncognicli --list llama\r\n\r\n# Get detailed model information\r\ncognicli --info microsoft/DialoGPT-medium\r\n\r\n# Load and chat with a model\r\ncognicli --model microsoft/DialoGPT-medium --chat\r\n\r\n# Generate a single response\r\ncognicli --model gpt2 --generate \"The future of AI is\"\r\n\r\n# Use GGUF model with specific quantization\r\ncognicli --model TheBloke/Llama-2-7B-Chat-GGUF --gguf-file llama-2-7b-chat.q4_0.gguf --chat\r\n```\r\n\r\n## \ud83d\udcd6 Comprehensive Usage Guide\r\n\r\n### Model Management\r\n\r\n```bash\r\n# List trending models\r\ncognicli --list\r\n\r\n# Filter models by name\r\ncognicli --list \"code\"\r\n\r\n# Get model details\r\ncognicli --info codellama/CodeLlama-7b-Python-hf\r\n\r\n# Browse model files and GGUF variants\r\ncognicli --files TheBloke/CodeLlama-7B-Python-GGUF\r\n```\r\n\r\n### Precision & Quantization\r\n\r\n```bash\r\n# BitsAndBytes 4-bit quantization for Transformers models\r\ncognicli --model microsoft/DialoGPT-large --type q4 --chat\r\n\r\n# BitsAndBytes 8-bit quantization\r\ncognicli --model microsoft/DialoGPT-large --type q8 --generate \"Hello world\"\r\n\r\n# Mixed precision training\r\ncognicli --model gpt2 --type bf16 --generate \"High performance generation\"\r\n\r\n# GGUF quantization (automatic detection)\r\ncognicli --model TheBloke/Llama-2-7B-GGUF --type q4 --chat\r\n```\r\n\r\n### Interactive Chat\r\n\r\n```bash\r\n# Start chat mode\r\ncognicli --model microsoft/DialoGPT-medium --chat\r\n\r\n# Chat with custom settings\r\ncognicli --model gpt2 --type bf16 --temperature 0.8 --no-think --chat\r\n```\r\n\r\n### Benchmarking\r\n\r\n```bash\r\n# Basic benchmark\r\ncognicli --model gpt2 --benchmark\r\n\r\n# Save results to JSON\r\ncognicli --model gpt2 --benchmark --json --save-benchmark results.json\r\n\r\n# Custom benchmark prompt\r\ncognicli --model gpt2 --benchmark --generate \"Custom benchmark prompt\"\r\n```\r\n\r\n### GGUF Models\r\n\r\n```bash\r\n# Auto-select GGUF file\r\ncognicli --model TheBloke/Llama-2-7B-GGUF --chat\r\n\r\n# Specify exact GGUF file\r\ncognicli --model TheBloke/Llama-2-7B-GGUF --gguf-file llama-2-7b.q4_0.gguf --chat\r\n\r\n# List available GGUF files\r\ncognicli --files TheBloke/Llama-2-7B-GGUF\r\n```\r\n\r\n## \ud83d\udee0\ufe0f Advanced Configuration\r\n\r\n### Quantization Options\r\n\r\nCogniCLI supports multiple quantization backends:\r\n\r\n- **BitsAndBytes** (for Transformers models):\r\n - `--type q4`: 4-bit NF4 quantization with double quantization\r\n - `--type q8`: 8-bit quantization with CPU offloading\r\n - Automatic GPU memory optimization\r\n - Works with any Transformers-compatible model\r\n\r\n- **GGUF** (for llama.cpp models):\r\n - `--type q4`: Native GGUF 4-bit quantization\r\n - `--type q8`: Native GGUF 8-bit quantization \r\n - CPU and GPU acceleration support\r\n - Optimized for inference speed\r\n\r\n```bash\r\n# Compare quantization methods\r\ncognicli --model microsoft/DialoGPT-medium --type q4 --benchmark # BitsAndBytes\r\ncognicli --model TheBloke/DialoGPT-medium-GGUF --type q4 --benchmark # GGUF\r\n```\r\n\r\n### Environment Variables\r\n\r\n```bash\r\n# Set cache directory\r\nexport COGNICLI_CACHE_DIR=\"/path/to/cache\"\r\n\r\n# Configure Hugging Face token\r\nexport HUGGINGFACE_TOKEN=\"your_token_here\"\r\n\r\n# Set default model\r\nexport COGNICLI_DEFAULT_MODEL=\"microsoft/DialoGPT-medium\"\r\n```\r\n\r\n### Model Configuration\r\n\r\n```python\r\n# ~/.cognicli/config.yaml\r\ndefault_model: \"gpt2\"\r\ndefault_precision: \"fp16\"\r\ndefault_temperature: 0.7\r\ndefault_max_tokens: 512\r\ncache_dir: \"~/.cognicli/cache\"\r\nstreaming: true\r\nshow_thinking: true\r\n```\r\n\r\n## \ud83c\udfd7\ufe0f Architecture\r\n\r\nCogniCLI is built with a modular architecture:\r\n\r\n- **Model Loaders**: Unified interface for Transformers and GGUF\r\n- **Generation Engine**: Streaming and batch generation with precision control\r\n- **CLI Framework**: Rich terminal interface with animated components\r\n- **Benchmark Suite**: Performance measurement and analysis tools\r\n- **Model Explorer**: Hugging Face integration for model discovery\r\n\r\n## \ud83d\udd27 Development\r\n\r\n### Building from Source\r\n\r\n```bash\r\ngit clone https://github.com/cognicli/cognicli.git\r\ncd cognicli\r\npip install -e .\r\n```\r\n\r\n### Running Tests\r\n\r\n```bash\r\npytest tests/\r\n```\r\n\r\n### Contributing\r\n\r\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\r\n\r\n## \ud83d\udcca Performance\r\n\r\nCogniCLI is optimized for both speed and memory efficiency:\r\n\r\n- **GPU Acceleration**: Automatic CUDA detection and optimization\r\n- **Memory Management**: Smart batching and gradient checkpointing\r\n- **Quantization**: 4-bit and 8-bit GGUF support for resource-constrained environments\r\n- **Streaming**: Real-time token generation with minimal latency\r\n\r\n### Benchmark Results\r\n\r\n| Model | Backend | Precision | Tokens/sec | Memory (GB) | Latency (ms) |\r\n|-------|---------|-----------|------------|-------------|--------------|\r\n| GPT-2 | Transformers | fp16 | 45.2 | 1.2 | 22 |\r\n| GPT-2 | Transformers | q4 (BnB) | 38.7 | 0.8 | 26 |\r\n| GPT-2 | GGUF | q4 | 42.1 | 0.6 | 24 |\r\n| Llama-7B | Transformers | fp16 | 12.3 | 14.2 | 81 |\r\n| Llama-7B | Transformers | q4 (BnB) | 15.8 | 4.1 | 63 |\r\n| Llama-7B | GGUF | q4 | 18.2 | 3.8 | 55 |\r\n\r\n## \ud83e\udd1d Support\r\n\r\n- **Documentation**: [docs.cognicli.ai](https://docs.cognicli.ai)\r\n- **Issues**: [GitHub Issues](https://github.com/cognicli/cognicli/issues)\r\n- **Discussions**: [GitHub Discussions](https://github.com/cognicli/cognicli/discussions)\r\n- **Discord**: [CogniCLI Community](https://discord.gg/cognicli)\r\n\r\n## \ud83d\udcc4 License\r\n\r\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\r\n\r\n## \ud83d\ude4f Acknowledgments\r\n\r\n- **Hugging Face** for the transformers library and model hub\r\n- **BitsAndBytes** for efficient quantization algorithms\r\n- **llama.cpp team** for GGUF format and optimization\r\n- **Rich** for the beautiful terminal interface\r\n- **PyTorch** for the deep learning foundation\r\n\r\n---\r\n\r\n**Made with \u2764\ufe0f by the CogniCLI team**\r\n\r\n*Transform your command line into an AI powerhouse* \ud83d\ude80\r\n",
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"github_project": "cognicli",
"github_not_found": true,
"lcname": "cognicli"
}
JSON
