OneLLM-Doey-ChatQA-V1-Llama-3.2-1B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 1.24B |
| Base Model | LLaMA 3.2-1B |
| License | Apache 2.0 |
| Context Length | 1024 tokens |
| Training Dataset | NVIDIA ChatQA-Training-Data |
What is OneLLM-Doey-ChatQA-V1-Llama-3.2-1B-GGUF?
This is a quantized version of the OneLLM-Doey-ChatQA model, specifically optimized using llama.cpp for efficient deployment. The model is based on LLaMA 3.2-1B and has been fine-tuned using LoRA (Low-Rank Adaptation) on NVIDIA's ChatQA dataset, making it particularly effective for conversational AI and question-answering tasks.
Implementation Details
The model leverages GGUF format optimization and implements the following technical features:
- Fine-tuned using LoRA for efficient adaptation of the base model
- Supports sequences up to 1024 tokens for comprehensive context understanding
- Optimized for both mobile (through OneLLM app) and PC platforms
- Implements local inference capabilities for enhanced privacy
Core Capabilities
- Conversational AI and chatbot functionality
- Question answering with context awareness
- Instruction-following tasks
- Cross-platform compatibility (iOS, Android, PC)
- Offline processing capability
Frequently Asked Questions
Q: What makes this model unique?
The model combines the efficiency of GGUF quantization with the proven architecture of LLaMA, optimized specifically for conversational AI and QA tasks. Its relatively small size (1.24B parameters) makes it suitable for deployment on various devices while maintaining good performance.
Q: What are the recommended use cases?
The model excels in chatbot applications, question-answering systems, and instruction-following tasks. It's particularly well-suited for applications requiring local processing and privacy-conscious deployments, such as educational tools, customer service automation, and personal AI assistants.
