ai2-olmo-eval
The olmo_eval framework is a way to run evaluation pipelines for language models on NLP tasks.
The codebase is extensible and contains task_sets and example configurations, which run a series
of tango steps for computating the model outputs and metrics.
Using this pipeline, you can evaluate m models on t task_sets, where each task_set consists of one or more individual tasks.
Using task_sets allows you to compute aggregate metrics for multiple tasks. The optional google-sheet integration can be used
for reporting.
The pipeline is built using ai2-tango and ai2-catwalk.
Installation
conda create -n eval-pipeline python=3.10
conda activate eval-pipeline
pip install -e .
PALOMA
Details on running the evaluation on PALOMA can be found here.
Quickstart
The current task_sets can be found at configs/task_sets. In this example, we run gen_tasks on EleutherAI/pythia-1b. The example config is here.
The configuration can be run as follows:
tango --settings tango.yml run configs/example_config.jsonnet --workspace my-eval-workspace
This executes all the steps defined in the config, and saves them in a local tango workspace called my-eval-workspace. If you add a new task_set or model to your config and run the same command again, it will reuse the previous outputs, and only compute the new outputs.
The output should look like this:
Load pipeline output
from tango import Workspace
workspace = Workspace.from_url("local://my-eval-workspace")
result = workspace.step_result("combine-all-outputs")Load individual task results with per instance outputs
result = workspace.step_result("outputs_pythia-1bstep140000_gen_tasks_drop")Evaluating common models on standard benchmarks
The eval_table config evaluates falcon-7b, mpt-7b, llama2-7b, and llama2-13b, on standard_benchmarks and MMLU. Run as follows:
tango --settings tango.yml run configs/eval_table.jsonnet --workspace my-eval-workspace