= getpass("gradient.ai workspace id:")Optional: Validate your Enviroment variables GRADIENT_ACCESS_TOKEN and GRADIENT_WORKSPACE_ID to get currently deployed models. Using the gradientai Python package.pip install gradientai    Requirement already satisfied: gradientai in /home/michi/.venv/lib/python3.10/site-packages (1.0.0)    Requirement already satisfied: aenum>=3.1.11 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (3.1.15)    Requirement already satisfied: pydantic<2.0.0,>=1.10.5 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (1.10.12)    Requirement already satisfied: python-dateutil>=2.8.2 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (2.8.2)    Requirement already satisfied: urllib3>=1.25.3 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (1.26.16)    Requirement already satisfied: typing-extensions>=4.2.0 in /home/michi/.venv/lib/python3.10/site-packages (from pydantic<2.0.0,>=1.10.5->gradientai) (4.5.0)    Requirement already satisfied: six>=1.5 in /home/michi/.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->gradientai) (1.16.0)import gradientaiclient = gradientai.Gradient()models = client.list_models(only_base=True)for model in models:    print(model.id)    99148c6d-c2a0-4fbe-a4a7-e7c05bdb8a09_base_ml_model    f0b97d96-51a8-4040-8b22-7940ee1fa24e_base_ml_model    cc2dafce-9e6e-4a23-a918-cad6ba89e42e_base_ml_modelnew_model = models[-1].create_model_adapter(name="my_model_adapter")new_model.id, new_model.name    ('674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter', 'my_model_adapter')Create the Gradient instance‚ÄãYou can specify different parameters such as the model, max_tokens generated, temperature, etc.As we later want to fine-tune out model, we select the model_adapter with the id 674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter, but you can use any base or fine-tunable model.llm = GradientLLM(    # `ID` listed in `$ gradient model list`
Gradient allows to fine tune and get completions on LLMs with a simple web API.
Gradient allows to fine tune and get completions on LLMs with a simple web API. ->: = getpass("gradient.ai workspace id:")Optional: Validate your Enviroment variables GRADIENT_ACCESS_TOKEN and GRADIENT_WORKSPACE_ID to get currently deployed models. Using the gradientai Python package.pip install gradientai    Requirement already satisfied: gradientai in /home/michi/.venv/lib/python3.10/site-packages (1.0.0)    Requirement already satisfied: aenum>=3.1.11 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (3.1.15)    Requirement already satisfied: pydantic<2.0.0,>=1.10.5 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (1.10.12)    Requirement already satisfied: python-dateutil>=2.8.2 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (2.8.2)    Requirement already satisfied: urllib3>=1.25.3 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (1.26.16)    Requirement already satisfied: typing-extensions>=4.2.0 in /home/michi/.venv/lib/python3.10/site-packages (from pydantic<2.0.0,>=1.10.5->gradientai) (4.5.0)    Requirement already satisfied: six>=1.5 in /home/michi/.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->gradientai) (1.16.0)import gradientaiclient = gradientai.Gradient()models = client.list_models(only_base=True)for model in models:    print(model.id)    99148c6d-c2a0-4fbe-a4a7-e7c05bdb8a09_base_ml_model    f0b97d96-51a8-4040-8b22-7940ee1fa24e_base_ml_model    cc2dafce-9e6e-4a23-a918-cad6ba89e42e_base_ml_modelnew_model = models[-1].create_model_adapter(name="my_model_adapter")new_model.id, new_model.name    ('674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter', 'my_model_adapter')Create the Gradient instance‚ÄãYou can specify different parameters such as the model, max_tokens generated, temperature, etc.As we later want to fine-tune out model, we select the model_adapter with the id 674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter, but you can use any base or fine-tunable model.llm = GradientLLM(    # `ID` listed in `$ gradient model list`