{"id":15906717,"url":"https://github.com/csinva/tree-prompt-experiments","last_synced_at":"2025-03-21T15:32:40.398Z","repository":{"id":198725344,"uuid":"598821642","full_name":"csinva/tree-prompt-experiments","owner":"csinva","description":"Create a tree of prompts during training that improves efficiency and accuracy.","archived":false,"fork":false,"pushed_at":"2023-12-13T22:40:46.000Z","size":20757,"stargazers_count":5,"open_issues_count":0,"forks_count":1,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-03-17T19:11:38.908Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/csinva.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-02-07T21:48:45.000Z","updated_at":"2024-12-04T08:10:59.000Z","dependencies_parsed_at":"2023-10-12T07:11:14.393Z","dependency_job_id":"6090eb37-fcfe-4f1d-bb72-a550b931ea11","html_url":"https://github.com/csinva/tree-prompt-experiments","commit_stats":null,"previous_names":["csinva/tree-prompt"],"tags_count":0,"template":false,"template_full_name":"csinva/cookiecutter-ml-research","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csinva%2Ftree-prompt-experiments","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csinva%2Ftree-prompt-experiments/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csinva%2Ftree-prompt-experiments/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csinva%2Ftree-prompt-experiments/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/csinva","download_url":"https://codeload.github.com/csinva/tree-prompt-experiments/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244147344,"owners_count":20405940,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-06T13:41:29.505Z","updated_at":"2025-03-21T15:32:38.528Z","avatar_url":"https://github.com/csinva.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003e Tree Prompting Experiments \u003c/h1\u003e\n\u003cp align=\"center\"\u003e Tree Prompting: Efficient Task Adaptation without Fine-Tuning, code for the \u003ca href=\"https://arxiv.org/abs/2310.14034\"\u003eTree-prompt paper\u003c/a\u003e. \n\u003c/p\u003e\n\nThis repo contains code for reproducing experiments in the \u003ca href=\"https://arxiv.org/abs/2310.14034\"\u003eTree-prompt paper\u003c/a\u003e. For a simple, easy-to-use interface, see https://github.com/csinva/tree-prompt.\n\n# Reproducing experiments\n\n### Organization\n- `tprompt`: contains main code for modeling (e.g. model architecture)\n- `experiments`: code for runnning experiments (e.g. loading data, training models, evaluating models)\n- `scripts`: scripts for running experiments (e.g. python scripts that launch jobs in `experiments` folder with different hyperparams)\n- `notebooks`: jupyter notebooks for analyzing results and making figures\n- `tests`: unit tests\n\n### Setup\n- clone and run `pip install -e .`, resulting in a package named `tprompt` that can be imported\n    - see `setup.py` for dependencies, not all are required\n- example run: run `python scripts/01_train_basic_models.py` (which calls `experiments/01_train_model.py` then view the results in `notebooks/01_model_results.ipynb`\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsinva%2Ftree-prompt-experiments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcsinva%2Ftree-prompt-experiments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsinva%2Ftree-prompt-experiments/lists"}