Ekbasis-27B-INT4

Papers: Look When Unsure, Check When Sure · DOI 10.5281/zenodo.23146970 · When Does a Consequence Model Make AI Agents Safer? · DOI 10.5281/zenodo.23197341 · Code: github.com/OpenInterpretability/ekbasis · Site: openinterp.org/ekbasis

INT4 build of Ekbasis-27B, an open world model for agents: given the current state and an action, it predicts what the action will do — will this lose work? will it fail? what will X be afterwards? — as calibrated, typed answers, in one forward pass. Not a model that thinks, nor one that judges: a model that foresees.

Weights in 4 bits (GPTQ W4A16, groups of 128, static activation order) made with llm-compressor (compressed-tensors format), calibrated on 256 items of the training mixture (never on evaluation items). The vision tower, MTP layers, embeddings and LM head stay in bf16. vLLM runs it with Marlin kernels: it saves memory (a 27B on a 24–32 GB GPU), not time. The prompt format, the letter readout and the calibration (calib.json, T = 1) are the same as the bf16 model. See the Ekbasis-27B card for what the model is, how it was trained, the full evaluation, its limits, and how to let it look at the real state when unsure (predict, observe, correct) over long sequences.

Context window: 262,144 tokens, as the bf16 model (the architecture's); the serving commands set 16,384 (4,096 in the 24 GB command below), and the evaluation's prompts average 0.7–1.6K tokens per state.

Use

Requires vLLM ≥ 0.30.0. One GPU; the API and the client are the same as for Ekbasis-27B.

hf download caiovicentino1/Ekbasis-27B-INT4 --local-dir Ekbasis-27B-INT4
bash Ekbasis-27B-INT4/serve_vllm.sh Ekbasis-27B-INT4 8001          # vLLM on 127.0.0.1:8001
python Ekbasis-27B-INT4/serve.py --model Ekbasis-27B-INT4 --vllm-url http://127.0.0.1:8001 --port 8000   # the API
pip install "git+https://github.com/OpenInterpretability/ekbasis"                                   # the client
EKBASIS_URL=http://127.0.0.1:8000 ekbasis git-check -- "git reset --hard"

On a 24 GB GPU (RTX 3090/4090 class), text only, with a 4k context (Ekbasis' prompts take about 2k tokens). We checked this setting by limiting vLLM to 95% of 24 GB: 60 of 60 held-out git answers the same as at full memory, 0.10 s per question (with the default settings, 24 GB leaves no memory for the cache).

vllm serve Ekbasis-27B-INT4 --host 127.0.0.1 --port 8001 --served-model-name decider --dtype bfloat16 \
  --gpu-memory-utilization 0.95 --max-model-len 4096 --max-num-batched-tokens 2048 --max-num-seqs 4 --enforce-eager \
  --limit-mm-per-prompt '{"image": 0, "video": 0}' --enable-prefix-caching --mamba-cache-mode align \
  --logprobs-mode processed_logprobs --max-logprobs 600

Quality against bf16

The same sets and scripts as the release evaluation, the build served the same way (vLLM 0.30, one RTX PRO 6000).

It meets 11 of the 12 criteria of the pre-registered quality gate. Where it differs from bf16:

• On command types never seen in training it raised 2 false alarms in the 42 cases that lose nothing, against 1 for bf16 (one case is 2.4 points; the criterion allows 1).
• The git answers that crossed the 0.2 flag (work lost: bf16 7% → 27%; nothing lost: bf16 13% → 56%) are three-command sequences with command types the model never saw; the AUROC there moves by +0.002.

The gate was written before the first builds existed, for V42's (PREREG_quantized.md in the repository: accuracy within 1 point of bf16; every work-losing real-repository scenario flagged, and flag and false-alarm rates within 1 point; ECE within 0.01; at least 97% of answers unchanged, 99% where bf16 is confident), and is applied unchanged to this release's builds, against this release's bf16. It informs; every build that works is released, so each machine can run the one that fits.

Which build

GPU sizes were tested by limiting vLLM to that much memory on one RTX PRO 6000 and checking that the answers match the build at full memory; the MLX build was validated with MLX on a Linux GPU, not on a Mac.

License

Apache-2.0 (LICENSE). Built on Eikos-27B (MIT, LICENSE-Eikos) and Qwen3.8-27B (Apache-2.0, LICENSE-Qwen); attributions in NOTICE.

References

See the Ekbasis-27B card: every entry checked against its source.

Contact

Questions, collaborations and commercial use: [email protected]. Found a way to make the guard approve a destructive action? Please email it before publishing (SECURITY.md in the repository).

Acknowledgments

Ekbasis was built by Caio Vicentino (OpenInterp) with the help of Claude Opus 5.5 (Anthropic). Claude worked on the project with him:

• planning and running the experiments;
• writing and reviewing the code and the documentation;
• checking the numbers and the references in this card.