GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
caiovicentino1
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
caiovicentino1
Model Tree
caiovicentino1/Ekbasis-27B
Input Modalities
Output Modalities
Supported Functionality
Requires vLLM ≥ 0.30.0. One GPU; the API and the client are the same as for Ekbasis-27B.
bash
hf download caiovicentino1/Ekbasis-27B-FP8 --local-dir Ekbasis-27B-FP8bash Ekbasis-27B-FP8/serve_vllm.sh Ekbasis-27B-FP8 8001 # vLLM on 127.0.0.1:8001python Ekbasis-27B-FP8/serve.py --model Ekbasis-27B-FP8 --vllm-url http://127.0.0.1:8001 --port 8000 # the APIpip install ekbasis # the clientEKBASIS_URL=http://127.0.0.1:8000 ekbasis git-check -- "git reset --hard"The same sets and scripts as the release evaluation, the build served the same way (vLLM 0.30, one RTX PRO 6000).
| Ekbasis-27B (bf16) | Ekbasis-27B-FP8 | |
|---|---|---|
| Size | 55 GB | 30.4 GB |
| Git, all 3,105 questions (seen + never-seen command types), % | 96.6 | 96.3 |
| "Will work be lost?" AUROC — seen / never-seen types | 0.998 / 0.989 | 0.997 / 0.987 |
| Work-losing flagged at P ≥ 0.2 — seen / never-seen, % | 98.0 / 95.2 | 98.0 / 95.2 |
| False alarms at P ≥ 0.2 — seen / never-seen, % | 0.0 / 2.4 | 0.0 / 2.4 |
| 16 fresh real-repository scenarios: work-losing flagged / false alarms / "fails?" right | 3/3 · 1/13 · 16/17 | 3/3 · 1/13 · 16/17 |
| Never-trained worlds, answers the actions change — one prompt per question / read-once, % | 81.2 / 78.0 | 81.4 / 78.1 |
ftest_family, answers the actions change, % | 81.9 | 81.4 |
| The 240-question git comparison — seen / never-seen types, % | 95.8 / 85.8 | 95.8 / 85.8 |
| ECE on git (lower is better) | 0.024 | 0.026 |
| Same answer as bf16 — all / bf16-confident (≥ 0.9), % | — | 98.2 / 99.9 |
| One check, one GPU — latency / checks per second with 16 in parallel | 0.088 s / 23 | 0.074 s / 37 |
| Forecasts of 10–30 actions, one GPU — median seconds one at a time / per minute with 16 in parallel (one prompt per question) | 5.0 s / 14.9 | 3.3 s / 24.5 |
| Same, reading the state once per step | 4.1 s / 28.2 | 3.1 s / 41.2 |
It meets every criterion of the pre-registered quality gate.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.
| Build | Size | Runs on | Speed (one RTX PRO 6000) |
|---|---|---|---|
| Ekbasis-27B (bf16) | 55 GB | GPUs with 80 GB | the reference |
| Ekbasis-27B-FP8 | 30.4 GB | GPUs with 48 GB; fastest with FP8 kernels (Ada, Hopper, Blackwell) | 1.6× the throughput of bf16 |
| Ekbasis-27B-INT4 | 18.6 GB |
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.
Apache-2.0 (LICENSE). Built on Eikos-27B (MIT, LICENSE-Eikos) and Qwen3.8-27B (Apache-2.0, LICENSE-Qwen);
attributions in NOTICE.
See the Ekbasis-27B card: every entry checked against its source.
Questions, collaborations and commercial use: caio@openinterp.org. Found a way to make the guard approve a destructive action? Please email it before publishing (SECURITY.md in the repository).
Ekbasis was built by Caio Vicentino (OpenInterp) with the help of Claude Opus 5.5 (Anthropic). Claude worked on the project with him:
| GPUs with 32 GB; 24 GB with text only and a 4k context (command in its card) |
| about bf16's |
| Ekbasis-27B-MLX-4bit | 15 GB | Macs with Apple Silicon (32 GB or more recommended) | — |