raidex 0.1.1

Self-contained CLI to measure an LLM against the Raidex Responsible-AI index

Raidex

An open Responsible AI index for frontier models. — raidex.ai

Raidex evaluates frontier LLMs across open RAI benchmarks — safety, fairness, factuality, security, machine ethics, robustness, and privacy — and publishes a composite RAI Score on a submit-driven leaderboard. Every benchmark is open-source and runnable; the project shows the capability-vs-RAI reporting gap side by side.

• Live leaderboard: https://huggingface.co/spaces/cloudronin/raidex
• Results dataset: https://huggingface.co/datasets/cloudronin/raidex-results
• Eval queue: https://huggingface.co/datasets/cloudronin/raidex-requests

Measure your own model: the raidex CLI

pip install raidex gives you the same measurement core the leaderboard runs, as a self-contained CLI. Score your own fine-tuned or self-hosted model against the Raidex index in your own environment — no account, no queue, no upload, no dependency on Raidex servers — and get board-comparable per-dimension + composite RAI scores.

pip install raidex

raidex eval --model openai/gpt-5.2 --tier A                                      # any litellm model
raidex eval --model http://localhost:8000/v1 --served-name my-model --tier A+B  # a local OpenAI-compatible endpoint
raidex eval --model ... --benchmarks bbq,strongreject                           # a subset
raidex eval --model ... --judge anthropic/claude-opus-4-8                        # configure the LLM judge
raidex eval --model ... --dry-run                                               # cost estimate only
raidex fetch-data                                                               # pre-cache data for offline / air-gapped use
raidex eval --model ... --offline --output results.json                         # run with zero network

It prints per-dimension + composite RAI Score + coverage (N/9) and writes a self-describing JSON — model spec, pinned dataset versions, judge, sampling, timestamp — that never leaves your machine. Benchmarks with no judge configured are skipped with honestly reduced coverage, not a failure. Scores are identical in scale to the leaderboard (same core, same benchmarks, same normalization).

Repository layout

• raidex/ — the pip-installable raidex CLI (raidex/cli.py) over the pure raidex.core eval-and-score library. The core is the shared foundation; the backend service and the CLI are two thin frontends over it, which is why a local score matches the board.
• space/ — the HuggingFace Space (Gradio app): leaderboard, the capability-vs-RAI gap visual, model cards, and the submit form. Deployed to the Space above.
• backend/ — the eval runner / service: polls the request queue, calls raidex.core, and writes results to the dataset.

The two datasets (raidex-results, raidex-requests) are generated data and live on the HF Hub, not in this repo.

Benchmarks (8 constituents)

Tier	Benchmark	Dimension	Pipeline
A	BBQ	Fairness & Bias	lm-eval (generative)
A	WMDP	Security	lm-eval (generative)
A	SimpleQA	Factuality	litellm + judge
A	StrongREJECT	Security (refusal)	strong_reject rubric
A	ETHICS	Machine Ethics	lm-eval (generative)
A	XSTest	Safety (over-refusal)	litellm + judge
B	AdvGLUE	Robustness	litellm (exact-match)
B	ConfAIde	Privacy	litellm (correlation)

The RAI Score is the mean of normalized constituent scores (0–100); coverage is reported as N/8. See space/METHODOLOGY.md for the index design, generative-task creation, judging, sampling, and normalization.

Running the backend

cd backend
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
# litellm reads provider keys from env vars (OPENAI_API_KEY, ANTHROPIC_API_KEY, ...);
# an OpenAI/Anthropic key is required for the judges.
python runner.py --dry-run --model openai/gpt-5.2 --tier A+B    # cost estimate
python runner.py --model anthropic/claude-opus-4-8 --tier A+B   # full run + upload
python runner.py --poll                                         # drain the request queue

Reproducing the published board. The leaderboard was produced by running the full 17-model roster at Tier A+B. backend/rerun.py runs the rate-limited batch with per-provider throttling and resume; the five always-greedy OpenAI/Anthropic models run via runner.py. See backend/README.md for the roster and provider routing, the exact keys, the reasoning-locked / WMDP-recovery / sampling / neutral-judge settings, the 2026-06-18 capability snapshot, and the generative-vs-loglikelihood calibration. Numbers reproduce within the stated error bars (composite 95% half-width ~±2 points), not bit-for-bit.

Running the Space locally

cd space
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
RAIDEX_DATA_SOURCE=hf python app.py     # reads the live results dataset

License

MIT — see LICENSE.