base on Evaluation suite for LLMs # OLMo-Eval OLMo-Eval is a repository for evaluating open language models. ## Note of Deprecation **NOTE:** This repository has been superceded by the OLMES repository, available at https://github.com/allenai/olmes (Open Language Model Evaluation System). ## 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`](https://github.com/allenai/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](https://github.com/allenai/tango) and [ai2-catwalk](https://github.com/allenai/catwalk). ## Installation After cloning the repository, please run ```commandline 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](configs/task_sets). In this example, we run `gen_tasks` on `EleutherAI/pythia-1b`. The example config is [here](configs/example_config.jsonnet). The configuration can be run as follows: ```commandline 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](configs/example_config.jsonnet). ### Load pipeline output ```python 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 ```python result = workspace.step_result("outputs_pythia-1bstep140000_gen_tasks_drop") ``` ## Evaluating common models on standard benchmarks The [eval_table](configs/eval_table.jsonnet) config evaluates `falcon-7b`, `mpt-7b`, `llama2-7b`, and `llama2-13b`, on [`standard_benchmarks`](configs/task_sets/standard_benchmarks.libsonnet) and [`MMLU`](configs/task_sets/mmlu_tasks.libsonnet). Run as follows: ```commandline 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](https://www.semanticscholar.org/paper/Paloma%3A-A-Benchmark-for-Evaluating-Language-Model-Magnusson-Bhagia/1a3f7e23ef8f0bf06d0efa0dc174e4e361226ead?utm_source=direct_link) Details on running the evaluation on PALOMA can be found [here](paloma/README.md). ## Advanced * [Save output to google sheet](ADVANCED.md#save-output-to-google-sheet) * [Use a remote workspace](ADVANCED.md#use-a-remote-workspace) * [Run without Tango (useful for debugging)](ADVANCED.md#run-without-tango) * [Run on Beaker](BEAKER.md) ", Assign "at most 3 tags" to the expected json: {"id":"7491","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"