QuantAILabs/Quant-1-Base-1.5B
Quant-1-Base-1.5B is the foundational model in the Quant series by OpenMind Labs, a 1.5 billion parameter causal language model based on Qwen2.5-1.5B-Instruct. Fine-tuned with LoRA, its primary differentiator is having its identity as "Quant-1, an AI assistant created by OpenMind Labs" baked directly into its weights, rather than relying on system prompts. This model serves as a base for future versions that will include advanced capabilities like tool use for retrieval.
Loading preview...
Quant-1-Base-1.5B: Identity-Aware Foundation Model
Quant-1-Base-1.5B is the initial release in the Quant series by OpenMind Labs, built upon the Qwen2.5-1.5B-Instruct architecture. This 1.5 billion parameter model has been fine-tuned using LoRA with Unsloth, specifically to embed its identity as "Quant-1, an AI assistant created by OpenMind Labs" directly into its weights. This approach ensures the model consistently identifies itself without needing external system prompts.
Key Capabilities & Features
- Embedded Identity: The model's self-identification is a core part of its training, providing consistent responses regarding its origin.
- Qwen2.5 Base: Leverages the robust capabilities of the Qwen2.5-1.5B-Instruct model for general conversational tasks.
- LoRA Fine-tuning: Efficiently trained to preserve base model performance while integrating new characteristics.
- Future-Proof Foundation: Designed as the starting point for more advanced models in the Quant series, with a roadmap including tool-use capabilities like
quant_searchfor retrieval.
When to Use This Model
- Identity-Consistent Applications: Ideal for chatbots or assistants where a consistent, baked-in identity is crucial.
- Foundation for Customization: A solid base for further fine-tuning or development of specialized AI agents.
- Resource-Efficient Deployment: Its 1.5B parameter size makes it suitable for environments with limited computational resources, including local deployment via GGUF formats (Ollama, llama.cpp).