OneLLM-Doey-V1-Llama-3.2-1B-it-GGUF
| Property | Value |
|---|---|
| Parameter Count | 1.24B |
| License | Apache 2.0 |
| Base Model | Meta Llama 3.2 1B Instruct |
| Maximum Sequence Length | 1024 tokens |
| Framework | LoRA fine-tuning |
What is OneLLM-Doey-V1-Llama-3.2-1B-it-GGUF?
OneLLM-Doey is a quantized version of the Llama 3.2 1B model, specifically optimized for mobile applications and offline AI processing. This GGUF variant is designed to provide efficient performance while maintaining the core capabilities of the original model. It has been fine-tuned using a proprietary dataset focused on enhancing app functionality and user experience.
Implementation Details
The model utilizes the LoRA fine-tuning framework to achieve optimal performance while maintaining a relatively small parameter count of 1.24B. It supports a maximum sequence length of 1024 tokens, making it suitable for both short conversations and longer text processing tasks.
- Quantized architecture for efficient mobile deployment
- Built on Meta's Llama 3.2 1B Instruct foundation
- Optimized for offline processing and privacy-focused applications
- Implements GGUF format for improved compatibility
Core Capabilities
- Conversational AI and chatbot functionality
- Question answering with contextual understanding
- Instruction-following tasks
- Long-context processing up to 1024 tokens
- Private, offline inference capability
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its optimization for mobile devices and offline processing, making it ideal for privacy-conscious applications. The GGUF format and careful quantization ensure efficient performance while maintaining model quality.
Q: What are the recommended use cases?
The model is particularly well-suited for mobile applications requiring conversational AI, personal assistants, and question-answering systems. It's designed to work effectively within the OneLLM app ecosystem for both iOS and Android platforms.
