# `ID` listed in `$ gradient model list`    model="674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter",    # # optional: set new credentials, they default to environment variables    # gradient_workspace_id=os.environ["GRADIENT_WORKSPACE_ID"],    # gradient_access_token=os.environ["GRADIENT_ACCESS_TOKEN"],    model_kwargs=dict(max_generated_token_count=128))Create a Prompt Template‚ÄãWe will create a prompt template for Question and Answer.template = """Question: {question}Answer: """prompt = PromptTemplate(template=template, input_variables=["question"])Initiate the LLMChain‚Äãllm_chain = LLMChain(prompt=prompt, llm=llm)Run the LLMChain‚ÄãProvide a question and run the LLMChain.question = "What NFL team won the Super Bowl in 1994?"llm_chain.run(    question=question)    '\nThe San Francisco 49ers won the Super Bowl in 1994.'Improve the results by fine-tuning (optional)Well - that is wrong - the San Francisco 49ers did not win.
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. ->: # `ID` listed in `$ gradient model list`    model="674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter",    # # optional: set new credentials, they default to environment variables    # gradient_workspace_id=os.environ["GRADIENT_WORKSPACE_ID"],    # gradient_access_token=os.environ["GRADIENT_ACCESS_TOKEN"],    model_kwargs=dict(max_generated_token_count=128))Create a Prompt Template‚ÄãWe will create a prompt template for Question and Answer.template = """Question: {question}Answer: """prompt = PromptTemplate(template=template, input_variables=["question"])Initiate the LLMChain‚Äãllm_chain = LLMChain(prompt=prompt, llm=llm)Run the LLMChain‚ÄãProvide a question and run the LLMChain.question = "What NFL team won the Super Bowl in 1994?"llm_chain.run(    question=question)    '\nThe San Francisco 49ers won the Super Bowl in 1994.'Improve the results by fine-tuning (optional)Well - that is wrong - the San Francisco 49ers did not win.