TensorVizion/Loi-LLM
Loi-LLM by TensorVizion is a 3.2 billion parameter conversational language model, fine-tuned from Meta's Llama 3.2 3B Instruct using QLoRA. It is optimized for general chat and assistant tasks, focusing on improving conversational coherence, instruction following, and response quality. This model excels in general-purpose dialogue and assistant functions at its scale.
Loading preview...
Model Overview
TensorVizion's Loi-LLM is a 3.2 billion parameter conversational language model, fine-tuned from Meta's Llama 3.2 3B Instruct. It leverages QLoRA (4-bit quantized low-rank adaptation) to enhance its capabilities for general chat and assistant tasks. The fine-tuning process specifically targeted improvements in conversational coherence, instruction following, and overall response quality within the 3 billion parameter scale.
Key Capabilities
- General Chat and Assistant: Designed for broad conversational applications and acting as a helpful assistant.
- Instruction Following: Improved ability to understand and execute user instructions.
- Conversational Coherence: Generates more natural and contextually relevant dialogue.
- Quantized for Efficiency: Available in Q6_K and Q4_K_M GGUF quantizations for efficient local inference on devices with limited VRAM (e.g., ~3.5 GB RAM for Q6, ~2.5 GB RAM for Q4).
- PEFT Adapter: Provides a PEFT LoRA adapter for developers to apply on top of the base Llama 3.2 3B Instruct model.
Limitations
- English Only: Performance in other languages is not tested.
- Scale-Dependent: As a 3B parameter model, it may be outperformed by larger models on complex reasoning tasks.
- Specialized Tasks: Not specifically trained for code generation, mathematics, or domain-specific professional tasks.
- Potential for Hallucinations: Like all language models, it can produce inaccurate or fabricated responses.
- Safety Alignment: Not aligned for safety-critical or high-stakes applications.