| license: apache-2.0 | |
| datasets: | |
| - ehartford/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 [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/ |