In this blog, we will explore the process of fine-tuning the Llama-3 8B model using the uncensored Wizard-Vicuna conversation dataset. With a focus on user-friendliness, we will walk you through the steps for training and running the model while offering troubleshooting tips along the way.

Overview of the Llama-3 8B Model

The Llama-3 8B model has been fine-tuned using the Wizard-Vicuna conversation dataset. This training enhances the model’s ability to generate human-like responses for better interaction. The training process utilizes QLoRA, a method that optimizes the model to achieve robust performance. The available model includes both the fp32 HuggingFace version and a quantized 4-bit q4_0 gguf version.

Prompt Style

The model has been trained to respond to prompts in a conversational format:

Example:

HUMAN: Hello
RESPONSE: Hi, how are you?
HUMAN: I'm fine.
RESPONSE: How can I help you?

Training Code

If you wish to reproduce the results obtained with this model, follow these simple steps:

• First, clone the training code repository from GitHub:

git clone https://github.com/georgesung/llm_qlora

• Navigate to the directory:

cd llm_qlora

• Install the required dependencies:

pip install -r requirements.txt

• Finally, run the training script:

python train.py configs/llama3_8b_chat_uncensored.yaml

Fine-Tuning Guide

For in-depth guidance, refer to the comprehensive fine-tuning guide available online.

Running Inference with Ollama

To interact with your newly trained model, you’ll first need to install Ollama. Follow these steps for inference:

• After installing Ollama, ensure you have the latest instructions from the GitHub README.
• Navigate to your model directory:

cd $MODEL_DIR_OF_CHOICE

• Download the gguf model file:

wget https://huggingface.co/georgesung/llama3_8b_chat_uncensored/resolvemain/llama3_8b_chat_uncensored_q4_0.gguf

• Create a model file named llama3-uncensored.modelfile with the following content:

FROM .llama3_8b_chat_uncensored_q4_0.gguf
TEMPLATE .System 
HUMAN: .Prompt 
RESPONSE: PARAMETER stop 
HUMAN: PARAMETER stop 
RESPONSE:

• Now, run the commands to create and execute the model:

ollama create llama3-uncensored -f llama3-uncensored.modelfile
ollama run llama3-uncensored

Troubleshooting

In case you encounter issues during the fine-tuning or inference phases, consider the following troubleshooting tips:

• Ensure that all necessary dependencies are accurately installed.
• Revisit the paths you are using to ensure they point to the correct directories.
• If you experience errors related to the model file, double-check the contents of llama3-uncensored.modelfile.
• Refer to the official documentation for Ollama and GitHub for further guidance.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following these steps, you can efficiently fine-tune and utilize the Llama-3 8B model to create intelligent conversational agents. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.