Model Overview

The vihangd/dopeyplats-1.1b-2T-v1 is an experimental 1.1 billion parameter language model, built upon the TinyLLaMA 1.1b 2T architecture. This model has undergone fine-tuning using a combination of Alpaca-QLoRA and DPO (Direct Preference Optimization) techniques.

Key Characteristics

• Base Model: TinyLLaMA 1.1b 2T
• Parameter Count: 1.1 billion parameters
• Fine-tuning Method: Alpaca-QLoRA with DPO for improved instruction following.
• Training Data: Primarily trained on Alpaca-style datasets.
• Prompt Format: Employs an Alpaca-style prompt template, making it compatible with existing Alpaca-tuned workflows.

Use Cases

This model is particularly well-suited for:

• Instruction Following: Designed to respond effectively to instructions given its Alpaca-style fine-tuning.
• Resource-Constrained Environments: Its 1.1 billion parameter size makes it a viable option for deployment where computational resources are limited.
• Experimental Applications: Ideal for researchers and developers looking to experiment with compact, instruction-tuned models.