fine tune a model with instructlab (https://github.com/instructlab/instructlab)

gistfile1.txt

> mkdir instructlab
> cd instructlab

# install instructlab
> python3 -m venv --upgrade-deps venv
> source venv/bin/activate

> pip cache remove llama_cpp_python
> pip install git+https://github.com/instructlab/instructlab.git@stable -C cmake.args="-DLLAMA_CUBLAS=on"

# if you dont have GPU you can go here https://github.com/instructlab/instructlab?tab=readme-ov-file#-installing-ilab to use your measly CPU

# initialise ilab
> ilab init

# download base model
> ilab download 

# add to the existing taxonomy 
> mkdir taxonomy/compositional_skills/linguistics/classification/good_pizza

# create taxonomy/compositional_skills/linguistics/classification/good_pizza/qna.yaml
qna.yaml
-----
created_by: Jim Fuller
seed_examples:
- answer: 'bad pizza'
  question: 'Hawiaan pizza'
- answer: 'good pizza'
  question: 'Anchovy pizza'
- answer: 'good pizza'
  question: 'Pepperoni pizza'
-----

# dont overthink adding skills ... its as easy as that, generate step will create a lot more synthetic data based on your few examples

# make sure the yaml is ok
> ilab diff

# generate synthetic training data
> ilab generate

# fine tune the base model
> ilab train

> ilab serve --model-path <resultant_model_from_train_step>

# provides REST API