SuperCOT-LoRA
| Property | Value |
|---|---|
| License | MIT |
| Author | kaiokendev |
| Compatible Models | 7B, 13B, 30B LLaMA |
| Training Datasets | 4 specialized datasets |
What is SuperCOT-LoRA?
SuperCOT-LoRA is a specialized LoRA (Low-Rank Adaptation) model designed to enhance LLaMA's ability to follow prompts, particularly in Langchain applications. It's trained on a carefully curated mixture of chain-of-thought datasets, code explanations, and logical deduction tasks.
Implementation Details
The model leverages multiple high-quality datasets including Alpaca-CoT, ConaLA, and cleaned Alpaca GPT-4 prompts. It's available in various quantization formats for both 13B and 30B parameter versions, offering flexibility in deployment based on computational resources.
- Multiple quantization options (4-bit, 3-bit, GGML)
- Compatible with any 7B, 13B, or 30B 4-bit quantized LLaMA model
- Optimized for both CUDA and TRITON implementations
Core Capabilities
- Enhanced chain-of-thought reasoning
- Improved code explanation and generation
- Better logical deduction capabilities
- Optimized prompt following for Langchain integration
Frequently Asked Questions
Q: What makes this model unique?
SuperCOT-LoRA stands out for its specialized training on chain-of-thought datasets and its optimization for Langchain applications. It combines multiple high-quality datasets to enhance logical reasoning and code understanding capabilities.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring step-by-step reasoning, code explanation, and logical deduction tasks. It works best when prompted using the Alpaca format and can be enhanced by using suggestion suffixes like "Think through this step by step" or "Let's think about this logically".
