eval-framework

What the framework does

The Aleph Alpha eval framework is a production ready framework for evaluating large language models across more than 90 benchmarks. The README calls it detailed LLM evaluation at scale and lists scalability as a core property, with a distributed evaluation integration built on Determined AI. Extensibility is the second pillar: custom models, benchmarks, and metrics are added through object oriented base classes. The framework ships pre loaded with over 90 tasks covering a broad range, from reasoning and coding to safety and long context, and it includes a set of metrics that covers LLM as a judge evaluations. That breadth means a team can run a single command and get coverage across many abilities instead of wiring up separate scripts per benchmark. The object oriented design is meant to lower the cost of adding a new task, a new model, or a new metric, since each is a subclass of a base rather than a fork of the whole runner. The README also lists perturbation testing for robustness analysis with configurable perturbation types and probabilities, rich outputs that generate JSON results, plots, and detailed analysis reports, and statistical analysis with confidence intervals and significance testing for reliable comparisons. Docker support is included with pre configured Dockerfiles for local and distributed setups. The documentation site linked from the README holds the full reference, including installation and usage details that go beyond the quick start in the README itself.

Models, tasks, and metrics

The framework integrates models in a few ways. It supports models loaded via HuggingFace Transformers or custom implementations that subclass the BaseLLM class. Benchmarks are added with the BaseTask class using minimal code, and metrics are defined with the BaseMetric class. This base class pattern is what lets the framework stay open ended while still providing a large built in library. The README notes flexible model integration and gives the example of HuggingFace Transformers as the common path, with custom models possible through the base class. The pre loaded tasks span reasoning, coding, safety, and long context, which covers the usual evaluation surface for a modern LLM. Metrics include LLM as a judge, so subjective quality can be scored automatically rather than by hand. Perturbation testing adds a robustness angle: the framework can apply configured perturbations at set probabilities and measure how the model's behaviour changes, which is a simple form of adversarial evaluation. Output is rich, producing JSON results, plots, and analysis reports so a run leaves behind both machine readable numbers and human readable summaries. Statistical analysis with confidence intervals and significance testing is built in, which matters when comparing two models and claiming one is better. The combination of many tasks, custom base classes, and statistical reporting is the framework's main value over a pile of notebook scripts.

Installation and extras

The quick start says the codebase is tested with Python 3.12 and PyTorch 2.5, and GPU support needs the matching CUDA dependencies installed on the system. The easiest path is to install from PyPI with pip install eval_framework. Optional extras enable specific features: api adds inference using the Aleph Alpha client, determined adds jobs via Determined, openai adds inference against OpenAI compatible HTTP endpoints, and transformers adds inference using the transformers library. A shorthand, the all extra, installs every extra. The README recommends uv for resolving dependencies when pulling extras, and it shows uv sync --all-extras or uv pip install eval_framework[all]. It also shows installing uv itself with the Astral install script and ensuring Python 3.12 is present via uv python install. The project therefore supports both a plain pip workflow and a uv managed workflow, with uv preferred for the optional extras because it handles the resolver more smoothly. Dockerfiles are provided for local and distributed setups, so a team can run the framework in a container without matching the host environment by hand. The documentation page covers detailed installation, and the README badges point to a build status, a PyPI version, and the docs site. The source, issues, and releases are on the Aleph-Alpha-Research eval-framework GitHub repository, and the package name on PyPI is eval_framework.

Editorial conclusion

The project is published under the Apache-2.0 license and written in Python, with its source at the Aleph-Alpha-Research eval-framework repository.