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 ## Note of Deprecation **NOTE:** This repository has been superceded by the OLMES repository, available at https://github.com/allenai/olmes (Open Language Model Evaluation System).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":39,"name":"3d-generation","display_name":"3D generation","slug":"3d-generation"},{"id":3,"name":"ai-agent","display_name":"AI agent","slug":"ai-agent"},{"id":8,"name":"ai-coding","display_name":"AI coding assistant","slug":"ai-coding"},{"id":5,"name":"ai-image","display_name":"AI image generation","slug":"ai-image"},{"id":9,"name":"ai-infrastructure","display_name":"AI infrastructure","slug":"ai-infrastructure"},{"id":10,"name":"ai-memory","display_name":"AI memory","slug":"ai-memory"},{"id":11,"name":"ai-skills","display_name":"AI skills","slug":"ai-skills"},{"id":12,"name":"ai-translation","display_name":"AI translation","slug":"ai-translation"},{"id":6,"name":"ai-video","display_name":"AI video generation","slug":"ai-video"},{"id":4,"name":"ai-voice","display_name":"AI voice","slug":"ai-voice"},{"id":7,"name":"ai-workflow","display_name":"AI workflow","slug":"ai-workflow"},{"id":22,"name":"audio-processing","display_name":"Audio processing","slug":"audio-processing"},{"id":29,"name":"authentication","display_name":"Authentication","slug":"authentication"},{"id":51,"name":"bundler","display_name":"Bundler","slug":"bundler"},{"id":41,"name":"chatbot","display_name":"Chatbot","slug":"chatbot"},{"id":27,"name":"cloud-native","display_name":"Cloud native","slug":"cloud-native"},{"id":1,"name":"computer-vision","display_name":"Computer vision","slug":"computer-vision"},{"id":37,"name":"crypto-trading","display_name":"Crypto trading","slug":"crypto-trading"},{"id":57,"name":"curated-list","display_name":"Curated list","slug":"curated-list"},{"id":54,"name":"data-streaming","display_name":"Data streaming","slug":"data-streaming"},{"id":35,"name":"data-visualization","display_name":"Data visualization","slug":"data-visualization"},{"id":16,"name":"database-backup","display_name":"Database backup","slug":"database-backup"},{"id":49,"name":"design-system","display_name":"Design system","slug":"design-system"},{"id":38,"name":"digital-human","display_name":"Digital human","slug":"digital-human"},{"id":34,"name":"document-processing","display_name":"Document processing","slug":"document-processing"},{"id":44,"name":"ecommerce","display_name":"E-commerce","slug":"ecommerce"},{"id":45,"name":"emulator","display_name":"Emulator","slug":"emulator"},{"id":46,"name":"file-management","display_name":"File management","slug":"file-management"},{"id":32,"name":"fintech","display_name":"Fintech","slug":"fintech"},{"id":31,"name":"game-development","display_name":"Game development","slug":"game-development"},{"id":24,"name":"headless-browser","display_name":"Headless browser","slug":"headless-browser"},{"id":52,"name":"headless-cms","display_name":"Headless CMS","slug":"headless-cms"},{"id":36,"name":"home-automation","display_name":"Home automation","slug":"home-automation"},{"id":20,"name":"image-editing","display_name":"Image editing","slug":"image-editing"},{"id":28,"name":"iot","display_name":"IoT","slug":"iot"},{"id":13,"name":"local-llm","display_name":"Local LLM","slug":"local-llm"},{"id":17,"name":"mcp","display_name":"MCP","slug":"mcp"},{"id":47,"name":"monitoring","display_name":"Monitoring","slug":"monitoring"},{"id":2,"name":"nlp","display_name":"NLP","slug":"nlp"},{"id":26,"name":"observability","display_name":"Observability","slug":"observability"},{"id":40,"name":"pentesting","display_name":"Pentesting","slug":"pentesting"},{"id":48,"name":"programming-examples","display_name":"Programming examples","slug":"programming-examples"},{"id":42,"name":"proxy","display_name":"Proxy","slug":"proxy"},{"id":14,"name":"rag","display_name":"RAG","slug":"rag"},{"id":56,"name":"resume-building","display_name":"Resume building","slug":"resume-building"},{"id":33,"name":"robotics","display_name":"Robotics","slug":"robotics"},{"id":30,"name":"search","display_name":"Search","slug":"search"},{"id":43,"name":"self-hosted","display_name":"Self-hosted","slug":"self-hosted"},{"id":50,"name":"static-analysis","display_name":"Static analysis","slug":"static-analysis"},{"id":18,"name":"synthetic-data","display_name":"Synthetic data","slug":"synthetic-data"},{"id":19,"name":"text-to-speech","display_name":"Text to speech","slug":"text-to-speech"},{"id":53,"name":"ui-components","display_name":"UI components","slug":"ui-components"},{"id":15,"name":"vector-database","display_name":"Vector database","slug":"vector-database"},{"id":21,"name":"video-editing","display_name":"Video editing","slug":"video-editing"},{"id":25,"name":"web-scraping","display_name":"Web scraping","slug":"web-scraping"},{"id":55,"name":"webassembly","display_name":"WebAssembly","slug":"webassembly"},{"id":23,"name":"workflow-automation","display_name":"Workflow automation","slug":"workflow-automation"}]" returns me the "expected json"