Eikos-27B-INT4

Eikos overview: JevBench hard accuracy, error when at least 90% confident and long context, against Jev and Laya

INT4 (GPTQ) build of Eikos-27B: 4-bit weights (GPTQ W4A16, groups of 128) with bf16 activations, made with llm-compressor (compressed-tensors format), calibrated on 256 training items (never on evaluation data). The vision tower, MTP weights, embeddings and LM head are kept in higher precision. The prompt format, the letter readout and the calibration (calib.json, T = 1) are the same as the bf16 model.

Eikos answers typed decisions (yes/no, one of N options, ordinal scores) about a given state in one forward pass, with a calibrated probability for every option. See the Eikos-27B card for what the model does, how it was trained, the full evaluation and its limitations.

Use

Requires vLLM ≥ 0.30.0. Older builds return wrong answers when several long requests are batched together on this hybrid (Gated DeltaNet) architecture.

hf download caiovicentino1/Eikos-27B-INT4 --local-dir Eikos-27B-INT4
bash Eikos-27B-INT4/serve_vllm.sh $PWD/Eikos-27B-INT4 8001      # vLLM engine: letter readout + hybrid prefix cache
python Eikos-27B-INT4/serve.py --model $PWD/Eikos-27B-INT4 --vllm-url http://127.0.0.1:8001 --port 8000   # HTTP API on :8000

The HTTP API, agent sessions and the question types are the same as for Eikos-27B.

Validation against bf16

Same 7,371 items for both builds (7 suites, never used in training), vLLM 0.30 with batching and prefix cache on. The release gate was fixed before looking at results: accuracy within 1 point of bf16, ECE within 0.01, and at least 97% of answers unchanged.

Eikos-27B (bf16) Eikos-27B-INT4
Size 55.6 GB 19.4 GB
JevBench public — original / hard 100.0 / 82.0 100.0 / 82.9
DecisionBench — medium / hard 89.1 / 78.2 88.4 / 78.5
General battery (9 tasks) 82.6 82.5
Finance (CUAD, sentiment, FinQA-judge) 85.4 84.9
Trade rules — seen / unseen 85.4 / 87.4 85.4 / 88.6
Compositional rules — same type / new domain / rulebooks 95.6 / 94.3 / 95.3 95.6 / 94.5 / 95.3
ECE (lower is better) 0.043 0.041
≥0.90 confidence: decides / error 44.1% / 2.5% 43.6% / 2.4%
Same answer as bf16 (all / confident ≥0.9) — 97.8% / 100.0%

It passes our release gate.

License

MIT for our contributions (LICENSE). The base model, Qwen3.8-27B, is Apache-2.0 (LICENSE-Qwen); attributions are in NOTICE. Not legal, tax or investment advice.

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