OneLLM-Doey-ChatQA-V1-Llama-3.2-1B
| Property | Value |
|---|---|
| Parameter Count | 1.24B |
| Base Model | LLaMA 3.2-1B |
| Training Method | LoRA (Low-Rank Adaptation) |
| License | Apache 2.0 |
| Maximum Context | 1024 tokens |
| Tensor Type | BF16 |
What is OneLLM-Doey-ChatQA-V1-Llama-3.2-1B?
OneLLM-Doey-ChatQA-V1-Llama-3.2-1B is a specialized language model that builds upon the LLaMA 3.2-1B architecture, fine-tuned specifically for conversational AI and question-answering tasks. The model leverages the NVIDIA ChatQA-Training-Data and implements LoRA fine-tuning to optimize performance while maintaining efficiency.
Implementation Details
The model employs BF16 tensor types and supports sequences up to 1024 tokens, making it suitable for both short interactions and moderately long conversations. It's designed to run efficiently on both mobile devices through the OneLLM app and traditional PC platforms via the Transformers library.
- Fine-tuned using LoRA methodology on NVIDIA ChatQA-Training-Data
- Optimized for both mobile and PC deployment
- Supports offline processing for enhanced privacy
- Compatible with transformers >= 4.43.0
Core Capabilities
- Conversational AI and chatbot applications
- Question answering with context understanding
- Instruction-following tasks
- Long-context processing up to 1024 tokens
- Cross-platform compatibility (iOS, Android, PC)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization for mobile devices while maintaining robust performance. It combines the efficiency of LoRA fine-tuning with the proven LLaMA architecture, making it particularly suitable for deployment in resource-constrained environments.
Q: What are the recommended use cases?
The model excels in conversational AI applications, question-answering systems, and instruction-following tasks. It's particularly well-suited for applications requiring offline processing or privacy-conscious implementations, such as mobile apps and edge devices.
