docs/README_CLI.md

CLI chat

The CLI can be used instead of gradio by running for some base model, e.g.:

python generate.py --base_model=gptj --cli=True --answer_with_sources=False

and for LangChain run:

python src/make_db.py --user_path=user_path --collection_name=UserData
python generate.py --base_model=gptj --cli=True --langchain_mode=UserData --answer_with_sources=False

with documents in user_path folder, or directly run:

python generate.py --base_model=gptj --cli=True --langchain_mode=UserData --user_path=user_path --answer_with_sources=False

which will build the database first time. One can also use any other models, like:

python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b --cli=True --langchain_mode=UserData --user_path=user_path --answer_with_sources=False

or for LLaMa2:

python generate.py --base_model='llama' --prompt_type=llama2 --cli=True --langchain_mode=UserData --user_path=user_path --answer_with_sources=False

Evaluation

To evaluate some custom json data by making the LLM generate responses and/or give reward scores, with parquet output, run:

python generate.py --base_model=MYMODEL --eval_filename=MYFILE.json --eval_prompts_only_num=NPROMPTS

where NPROMPTS is the number of prompts in the json file to evaluate (can be less than total). See tests/test_eval.py::test_eval_json for a test code example.