Awesome
OLMo-Eval
OLMo-Eval is a repository for evaluating open language models.
Overview
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 computing 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
After cloning the repository, please run
conda create -n eval-pipeline python=3.10
conda activate eval-pipeline
cd OLMo-Eval
pip install -e .
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:
<img width="1886" alt="Screen Shot 2023-12-04 at 9 22 35 PM" src="https://github.com/allenai/ai2-llm-eval/assets/6500683/14a74e61-75d8-470c-8bde-12e35c38c44a">New models and datasets can be added by modifying the example configuration.
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
PALOMA
This repository was also used to run evaluations for the PALOMA paper
Details on running the evaluation on PALOMA can be found here.