Hugging Face Overview
shitshow123/tinylamma-20000 Overview
The shitshow123/tinylamma-20000 is a compact yet capable language model, built upon the TinyLlama 1B architecture. Developed by shitshow123, this model has undergone extensive instruction tuning using the Direct Preference Optimization (DPO) method for 20,000 steps.
Key Characteristics
- Base Model: TinyLlama 1B
- Parameter Count: 1.1 billion parameters
- Training Method: Direct Preference Optimization (DPO) for 20,000 steps
- Context Length: 2048 tokens
Intended Use Cases
This model is primarily designed for applications where a smaller footprint and efficient instruction following are critical. Its DPO training suggests an optimization for generating responses aligned with human preferences, making it potentially suitable for:
- Instruction-based text generation
- Lightweight conversational agents
- Tasks requiring adherence to specific prompts within its context window.