A barebones set of utilities that help run few-shot prompting using hf

Currently, supports few-shot classification (through generation) along with generating logprobs for each class.

This is meant to be as lightweight as possible, and is not meant to be a full-fledged library.

For running a task, 4 additional files are required. example_task contains the setup for running a simple question answering task. A brief description of the 4 files is as follows :

• A prompt.json should contain prompts for generation. Each prompt should be in the following format

  {
      "prompt_name": {
          #zero_shot: The initial prompt along with the input variables. In case of zero shot classification, this is the only prompt that will be needed. Example :
          "zero_shot": "You are an expert in US history. Given a question about a US political figurem, answer it with a short paragraph.\n\nQUESTION: {question}\nANSWER: ",
  
          # followup: A followup prompt that fixes format for exemplars. Example: 
          "followup": "QUESTION: {question}\nANSWER: ",
      }
  }

  For a more involved example of a prompt, see examples/sample_prompt.json.

• An exemplars.jsonl should contain exemplars in jsonlines format with the input and output variables.

  {"input": "Who was the first president of the United States?","output": "George Washington"}
  ...

  See examples/sample_exemplars.jsonl for a sample file.

• A dataset.jsonl should contain the dataset in jsonlines format with the input (and optionally output variables). NOTE: Each dataset item must have a key, to be specified under dataset -> id_key in config.json

  {"q_id": "1001", "input": "Who was the second president of the United States?", "output": "John Adams"}
  ...

  See examples/sample_dataset.jsonl for a sample file.

• A config.yml file should contain various hyperparameters and data paths in a yaml parsable format. See configs/example_config.yml for a sample file.

See the create_task subdirectory for convenient code that will create these files automatically for you from CSV and text files, which you may find more convenient.

Create an empty conda enironment:

conda create -n hf-fewshot python=3.11
conda activate hf-fewshot

After cloning the repository, run the following commands:

# first create an empty conda environment 
conda create -n hf_fewshot python=3.11

cd hf-fewshot
pip install -e .

• If using OpenAI, add the OpenAI API key to your environment variables as OPENAI_API_KEY. If using conda, you can do this by running conda env config vars set OPENAI_API_KEY=<your key>.

• If using a huggingface "Gated" model (such as Llama 3), add the Huggingface access token to an environment variable called HF_TOKEN by running conda env config vars set HF_TOKEN=<your token>.

To run fewshot prompting, run the following command :

hf_fewshot --config configs/example_config.yml

If output_file is specified in the config, it will be saved to that location. Otherwise, a file will be automatically generated in that directory prior to saving.