OneLLM-Doey-V1-Llama-3.2-3B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 3.61B |
| License | Apache 2.0 |
| Base Model | LLaMA 3.2-3B |
| Training Data | NVIDIA ChatQA-Training-Data |
| Max Sequence Length | 1024 tokens |
What is OneLLM-Doey-V1-Llama-3.2-3B-GGUF?
This is a GGUF-quantized version of the OneLLM-Doey-V1-Llama-3.2-3B model, specifically optimized for efficient deployment and inference. The model is based on LLaMA 3.2-3B and has been fine-tuned using LoRA (Low-Rank Adaptation) on the NVIDIA ChatQA-Training-Data dataset, making it particularly effective for conversational AI and instruction-following tasks.
Implementation Details
The model leverages the following technical specifications:
- GGUF quantization for optimal performance and reduced memory footprint
- LoRA fine-tuning methodology for efficient adaptation
- 1024 token context window for handling longer conversations
- Compatible with both mobile (iOS via OneLLM app) and desktop platforms
Core Capabilities
- Conversational AI and chatbot functionality
- Question answering with contextual understanding
- Instruction-following tasks
- Long-form text processing and reasoning
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of LLaMA 3.2-3B with specialized fine-tuning for conversational tasks, while offering the efficiency of GGUF quantization. It's particularly notable for its dual-platform support, working both on mobile devices through the OneLLM app and on desktop systems via the Transformers library.
Q: What are the recommended use cases?
The model is ideal for building chatbots, creating question-answering systems, developing conversational agents, and handling instruction-based tasks. It's particularly well-suited for applications requiring offline processing and privacy-conscious deployments.
