An open platform that shows every model call: traces, cost, quality scores and prompt versions. Use it in the cloud or self-host it.
All repositories
Evals & observabilityEleutherAI
lm-evaluation-harness
The standard toolkit for running models through hundreds of academic benchmarks. Many open model leaderboards are built on it.
Who it is for
For researchers and anyone comparing or fine-tuning open models.
How to start
- Clone and install:
git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness, thenpip install -e .. - Add a backend, for example
pip install "lm_eval[hf]". - Run:
lm_eval --model hf --model_args pretrained=EleutherAI/gpt-j-6B --tasks hellaswag --device cuda:0 --batch_size 8.
Steps are taken from the README. Check the current version in the repository before running them.
Stars over the last 30 days
+242Sep 6 — Oct 4
Author's description
A framework for few-shot evaluation of language models.
More in «Evals & observability»
A long-standing ML platform that now covers LLM apps too: tracing, evaluation, a prompt registry and a model gateway. Plugs into most frameworks.
A command-line tool for testing prompts and agents: describe cases in a config and compare models. Also does red teaming and vulnerability scanning. Plugs into CI.
A platform for debugging and evaluating LLM apps: traces, automated quality checks and dashboards. Supports RAG and agent chains, and can be self-hosted.
OpenAI's framework for evaluating models plus a registry of ready-made benchmarks. A historically important project that shows how eval templates are structured.
A pytest-style framework for testing LLM apps: ready-made metrics for RAG, agents and chatbots, plus trace-based checks. Results can be pushed to a cloud.