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Project-URL: issues, https://github.com/Future-House/ether0/issues Project-URL: repository, https://github.com/Future-House/ether0 Classifier: Intended Audience :: Developers Classifier: License :: OSI Approved :: Apache Software License Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python :: 3 :: Only Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Programming Language :: Python :: 3.13 Classifier: Programming Language :: Python Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Classifier: Topic :: Scientific/Engineering :: Chemistry Requires-Python: >=3.11 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: accelerate>=1.10.1 Requires-Dist: datasets Requires-Dist: exmol>=3.3.0 Requires-Dist: gradio>=5.44.0 Requires-Dist: httpx Requires-Dist: huggingface-hub Requires-Dist: molbloom==2.3.4 Requires-Dist: pydantic>=2 Requires-Dist: rdkit Requires-Dist: regex Requires-Dist: spaces>=0.40.1 Requires-Dist: tenacity Provides-Extra: add-tokens Requires-Dist: ipykernel; extra == "add-tokens" Requires-Dist: ipywidgets>=8; extra == "add-tokens" Requires-Dist: transformers>=4.49; extra == "add-tokens" Provides-Extra: baselines Requires-Dist: fhaviary>=0.19; extra == "baselines" Requires-Dist: fhlmi>=0.26; extra == "baselines" Requires-Dist: ipython; extra == "baselines" Provides-Extra: dev Requires-Dist: ether0[add-tokens,typing]; extra == "dev" Requires-Dist: huggingface-hub[cli]; extra == "dev" Requires-Dist: ipython>=8; extra == "dev" Requires-Dist: mypy>=1.8; extra == "dev" Requires-Dist: pre-commit>=3.4; extra == "dev" Requires-Dist: pylint>=3; extra == "dev" Requires-Dist: pytest; extra == "dev" Requires-Dist: pytest-subtests; extra == "dev" Requires-Dist: pytest-sugar; extra == "dev" Requires-Dist: pytest-timer[colorama]; extra == "dev" Requires-Dist: pytest-xdist; extra == "dev" Requires-Dist: refurb>=2; extra == "dev" Requires-Dist: typeguard; extra == "dev" Provides-Extra: typing Requires-Dist: types-regex; extra == "typing" Dynamic: license-file # ether0 Reward Model [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Future-House/ether0) [![arXiv](https://img.shields.io/badge/arXiv-2506.17238-b31b1b.svg)](https://arxiv.org/abs/2506.17238) [![Project Status: Active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) ![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg) [![Tests](https://github.com/Future-House/ether0/actions/workflows/lint-test.yaml/badge.svg)](https://github.com/Future-House/ether0/actions) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![python](https://img.shields.io/badge/python-3.11+-blue?style=flat&logo=python&logoColor=white)](https://www.python.org) [![Model on HF](https://huggingface.co/datasets/huggingface/badges/resolve/main/model-on-hf-md-dark.svg)](https://huggingface.co/futurehouse/ether0) [![Dataset on HF](https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md-dark.svg)](https://huggingface.co/datasets/futurehouse/ether0-benchmark) ![ether0 logo](docs/assets/ether0_logo.svg) _ether0: a scientific reasoning model, dataset, and reward functions for chemistry._ This repo contains the reward model for evaluating ether0 and similar models, along with utilities for working with the verifiable rewards in [our benchmark](https://huggingface.co/datasets/futurehouse/ether0-benchmark). ## Overview ether0 is a reasoning language model post-trained through a loop of: 1. Supervised fine-tuning (SFT) on long chain-of-thought reasoning traces, to elicit reasoning from a base model. 2. Reinforcement learning with verifiable rewards (RLVR) to improve reasoning on focused task groups, at their own pace. These multitask learned models are referred to as 'specialists'. 3. Rejection sampling to filter specialists' reasoning for correctness and quality. 4. SFT on the base model again to make a 'generalist' reasoning model. 5. RLVR to recover any lost performance and push further in an all-task setting. ![ether0 training info](docs/assets/training_info.png) ### Repo Structure This repo contains several packages: - `ether0`: reward functions, `rdkit` data utilities, dataset generation prompts, dataset data models, language model training prompts, and data models. - `ether0.remotes`: server code for ether0 reward functions involving exotic packages and/or third party models. > [!NOTE] > This repo does not contain training code, > although you can find open source repositories like [NeMo-RL](https://github.com/NVIDIA/NeMo-RL) > or [Hugging Face TRL](https://github.com/huggingface/trl) > that can do the SFT and RL phases of training. ### Open Weights Please see our open-source weights on Hugging Face: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("futurehouse/ether0") tokenizer = AutoTokenizer.from_pretrained("futurehouse/ether0") ``` ### Open Test Set Please see our open-source benchmark (test set) on Hugging Face: ```python from datasets import load_dataset test_ds = load_dataset("futurehouse/ether0-benchmark", split="test") ``` ## Usage ### Installation The easiest way to get started is a `pip install` from GitHub: ```bash pip install git+https://github.com/Future-House/ether0.git ``` Or if you want the full set up, clone the repo and use `uv`: ```bash git clone https://github.com/Future-House/ether0.git cd ether0 uv sync ``` ### Reward Functions Here is a basic example of how to use the reward functions: ```python from ether0.rewards import valid_mol_eval # Task: provide a valid completion of this molecule partial_smiles = "O=C(OC1C(OC(=O)C=2C=CC=CC2)C3(O)C(C)(C)CCCC3(C)C4CC=5OC=CC5C(C)C14" # Here's two model-proposed SMILES completions invalid_completion_smiles = "CCC" valid_completion_smiles = ")C=6C=CC=CC6" # Evaluate the completions assert not valid_mol_eval(invalid_completion_smiles, partial_smiles) assert valid_mol_eval(valid_completion_smiles, partial_smiles) ``` ### Visualization If it helps, you can visualize the molecules: ```python from ether0.data import draw_molecule # See above reward functions demo for where these came from partial_smiles = "O=C(OC1C(OC(=O)C=2C=CC=CC2)C3(O)C(C)(C)CCCC3(C)C4CC=5OC=CC5C(C)C14" invalid_completion_smiles = "CCC" valid_completion_smiles = ")C=6C=CC=CC6" valid_mol_text = draw_molecule(partial_smiles + valid_completion_smiles) with open("valid_molecule.svg", "w") as f: f.write(valid_mol_text) ``` The output of `draw_molecule` can also be easily visualized using `IPython.display`, or in your terminal via `chafa valid_molecule.svg` ([chafa docs](https://hpjansson.org/chafa/)). ![valid molecule](docs/assets/valid_molecule.svg) ### Benchmark Here is a sample baseline of [`ether0-benchmark`](https://huggingface.co/datasets/futurehouse/ether0-benchmark) on `gpt-4o` using [`lmi`](https://github.com/Future-House/ldp/tree/main/packages/lmi). To install `lmi`, please install `ether0` with the `baselines` extra (for example `uv sync --extra baselines`). We also need to run our remote rewards server via `ether0-serve` (for more information, see [`ether0.remotes` docs](packages/remotes/README.md)): ```bash ETHER0_REMOTES_API_TOKEN=abc123 ether0-serve ``` Next, start `ipython` with the relevant environment variables set: ```bash ETHER0_REMOTES_API_BASE_URL="http://127.0.0.1:8000" ETHER0_REMOTES_API_TOKEN=abc123 \ ipython ``` And run the following Python code: ```python import itertools import statistics from collections import defaultdict from aviary.core import Message from datasets import load_dataset from lmi import LiteLLMModel from tqdm.asyncio import tqdm_asyncio as asyncio from ether0.data import get_problem_category from ether0.model_prompts import LOOSE_XML_ANSWER_USER_PROMPT, extract_answer_loose from ether0.models import RewardFunctionInfo from ether0.rewards import EVAL_FUNCTIONS # Add LLM prompt of your making to the dataset test_ds = load_dataset("futurehouse/ether0-benchmark", split="test").map( lambda x: {"prompt": "\n\n".join((LOOSE_XML_ANSWER_USER_PROMPT, x["problem"]))} ) # Prompt to LLM model = LiteLLMModel(name="gpt-4o") results = await asyncio.gather( *(model.acompletion([Message(content=row["prompt"])]) for row in test_ds), desc="Running evaluation", ) # Compute rewards per_category_rewards = defaultdict(list) for row, result in zip(test_ds, results, strict=True): # NOTE: you can also use `ether0.rewards.accuracy_reward`, # but we decided to go a bit "lower level" for this demo reward_info = RewardFunctionInfo.model_validate(row["solution"]) yhat = extract_answer_loose(result[0].text) reward = EVAL_FUNCTIONS[reward_info.fxn_name]( yhat=yhat, y=reward_info.answer_info, test=True ) per_category_rewards[get_problem_category(reward_info.problem_type)].append(reward) for category, rewards in sorted(per_category_rewards.items()): print( f"In category {category!r} of {len(rewards)} questions," f" average reward was {statistics.mean(rewards):.3f}." ) accuracy = statistics.mean(itertools.chain.from_iterable(per_category_rewards.values())) print(f"Cumulative average reward across {len(test_ds)} questions was {accuracy:.3f}.") ```