---
license: apache-2.0
datasets:
- georgesung/wizard_vicuna_70k_unfiltered
base_model: OpenLLaMA-7B
---

# Overview
Fine-tuned [OpenLLaMA-7B](https://huggingface.co/openlm-research/open_llama_7b) with an uncensored/unfiltered Wizard-Vicuna conversation dataset (originally from [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/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](https://github.com/georgesung/llm_qlora).

# Demo
For a Gradio chat application using this model, clone [this HuggingFace Space](https://huggingface.co/spaces/georgesung/open_llama_7b_qlora_uncensored_chat/tree/main) 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 :)

https://georgesung.github.io/ai/qlora-ift/