license: other
datasets:
- georgesung/wizard_vicuna_70k_unfiltered
Overview
Fine-tuned Llama-3 8B with an uncensored/unfiltered Wizard-Vicuna conversation dataset.
Used QLoRA for fine-tuning.
The model here includes the fp32 HuggingFace version, plus a quantized 4-bit q4_0 gguf version.
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.
To reproduce the results:
git clone https://github.com/georgesung/llm_qlora
cd llm_qlora
pip install -r requirements.txt
python train.py configs/llama3_8b_chat_uncensored.yaml
Fine-tuning guide
https://www.georgesung.com/ai/qlora-ift
Ollama inference
First, install Ollama. Based on instructions here, run the following:
cd $MODEL_DIR_OF_CHOICE
wget https://huggingface.co/georgesung/llama3_8b_chat_uncensored/resolve/main/llama3_8b_chat_uncensored_q4_0.gguf
Create a file called llama3-uncensored.modelfile with the following:
FROM ./llama3_8b_chat_uncensored_q4_0.gguf
TEMPLATE """{{ .System }}
### HUMAN:
{{ .Prompt }}
### RESPONSE:
"""
PARAMETER stop "### HUMAN:"
PARAMETER stop "### RESPONSE:"
Then run:
ollama create llama3-uncensored -f llama3-uncensored.modelfile
ollama run llama3-uncensored