Goppa-LogiLlama
| Property | Value |
|---|---|
| Parameter Count | 1B |
| Model Type | Small Language Model (SLM) |
| Architecture | Hidden Size: 2048, Hidden Layers: 16, Attention Heads: 32 |
| License | MIT License |
| Model URL | https://huggingface.co/goppa-ai/Goppa-LogiLlama |
What is Goppa-LogiLlama?
Goppa-LogiLlama represents a significant advancement in efficient language models, challenging the "bigger is better" paradigm. Built on a 1B-parameter LLaMA base, this model has been specifically enhanced to deliver superior logical reasoning capabilities while maintaining a modest computational footprint. It demonstrates that through careful optimization and specialized training, smaller models can achieve impressive reasoning abilities without the resource demands of larger alternatives.
Implementation Details
The model features a sophisticated architecture with 2048 hidden dimensions, 16 hidden layers, and 32 attention heads. It incorporates a custom ROPE scaling mechanism and uses a specialized tokenizer with extensive special tokens. The implementation is optimized for float32 tensor operations and includes comprehensive configuration files for seamless integration with the Hugging Face ecosystem.
- Custom tokenizer with extensive special tokens
- Optimized for on-device inference
- Incorporates advanced ROPE scaling (llama3 type)
- Comprehensive configuration files for easy deployment
Core Capabilities
- Enhanced logical reasoning and problem-solving
- Efficient on-device processing
- Low memory and energy footprint
- Context-aware response generation
- Transparent and reproducible training process
Frequently Asked Questions
Q: What makes this model unique?
LogiLlama's uniqueness lies in its ability to deliver enhanced logical reasoning capabilities within a compact 1B parameter framework, challenging the notion that effective language models must be large. Its optimization for on-device deployment makes it particularly valuable for resource-constrained environments.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring logical reasoning and problem-solving capabilities while operating under resource constraints. This includes on-device applications, educational tools, and automated reasoning systems where efficiency is crucial.
