2-bit-LLMs
| Property | Value |
|---|---|
| Model Size | 103B parameters |
| License | Other |
| Primary Use | Text Generation |
| Format | GGUF |
What is 2-bit-LLMs?
2-bit-LLMs is an innovative collection of large language models that have been quantized to 2-bit precision using a novel quip#-inspired approach in llama.cpp. This collection includes some of the most powerful models available, ranging from 70B to 120B parameters, including notable names like Senku-70b, Qwen1.5-72b-Chat, and DiscoLM-120b.
Implementation Details
The models utilize advanced quantization techniques to dramatically reduce their memory footprint while maintaining performance. They support various prompt formats including ChatML, Mistral, Vicuna, and Alpaca, making them versatile for different applications.
- Efficient 2-bit quantization for reduced memory usage
- Support for extended context lengths (requires GPU memory consideration)
- Multiple prompt format compatibility
- RoPE scaling support for scaled models
Core Capabilities
- High-performance text generation
- Support for multiple languages and specialized domains
- Extended context handling capabilities
- Optimized for both performance and storage efficiency
Frequently Asked Questions
Q: What makes this model unique?
The unique aspect of 2-bit-LLMs lies in its efficient quantization approach, allowing users to run large language models with significantly reduced memory requirements while maintaining model quality. The collection includes both base and chat-tuned variants of popular models.
Q: What are the recommended use cases?
These models are ideal for users who need to run large language models with limited computational resources. They're particularly suitable for text generation tasks, coding assistance, and general-purpose AI interactions where memory efficiency is crucial.
