Overview
Fine-tuned OpenLLaMA-7B with an uncensored/unfiltered Wizard-Vicuna conversation dataset (originally from ehartford/wizard_vicuna_70k_unfiltered). Used QLoRA for fine-tuning. Trained for one epoch on a 24GB GPU (NVIDIA A10G) instance, took ~18 hours to train.
Prompt style
The model was trained with the following prompt style:
### HUMAN:
Hello
### RESPONSE:
Hi, how are you?
### HUMAN:
I'm fine.
### RESPONSE:
How can I help you?
...
Training code
Code used to train the model is available here.
Demo
For a Gradio chat application using this model, clone this HuggingFace Space and run it on top of a GPU instance. The basic T4 GPU instance will work.
Blog post
Since this was my first time fine-tuning an LLM, I also wrote an accompanying blog post about how I performed the training :)
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