Ling-lite
| Property | Value |
|---|---|
| Total Parameters | 16.8B |
| Activated Parameters | 2.75B |
| Context Length | 64K |
| License | MIT |
| Author | inclusionAI |
What is Ling-lite?
Ling-lite is an efficient Mixture of Experts (MoE) language model developed by inclusionAI. It represents a sophisticated approach to language modeling, utilizing 16.8B total parameters while only activating 2.75B parameters during operation, making it both powerful and computationally efficient. The model features an impressive 64K context length, allowing it to process and understand lengthy text sequences.
Implementation Details
The model is implemented using the Hugging Face Transformers library and can be easily integrated into existing ML pipelines. It employs a MoE architecture that enables efficient scaling and adaptation to various tasks while maintaining strong performance. The model can be loaded and used with standard PyTorch operations, supporting automatic device mapping and dtype selection for optimal performance.
- Efficient parameter activation system (2.75B of 16.8B total)
- Extended context length of 64K tokens
- Built on Hugging Face Transformers framework
- Supports automatic device mapping and dtype optimization
Core Capabilities
- Natural language processing and generation
- Long-context understanding and processing
- Adaptive task handling through MoE architecture
- Efficient scaling for various applications
- Chat-based interactions with customizable system prompts
Frequently Asked Questions
Q: What makes this model unique?
Ling-lite's distinctive feature is its efficient MoE architecture that activates only 2.75B parameters while maintaining access to 16.8B parameters total, offering an optimal balance between computational efficiency and model performance. The 64K context length also sets it apart from many other models in its class.
Q: What are the recommended use cases?
The model is well-suited for a wide range of NLP tasks, including text generation, natural language understanding, and complex problem-solving. Its architecture makes it particularly effective for applications requiring long-context understanding and efficient resource utilization.
