Eikos
Collection
Open, calibrated, single-pass typed-decision models (4B & 27B) for finance and trading • 10 items • Updated • 9
Eikos-27B-FP8
FP8 build of Eikos-27B: weights in FP8 (per channel) with dynamic FP8 activations (per token), made with llm-compressor (FP8_DYNAMIC, compressed-tensors format). No calibration 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-FP8 --local-dir Eikos-27B-FP8
bash Eikos-27B-FP8/serve_vllm.sh $PWD/Eikos-27B-FP8 8001 # vLLM engine: letter readout + hybrid prefix cache
python Eikos-27B-FP8/serve.py --model $PWD/Eikos-27B-FP8 --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.
Images (serve v1.3): this build reads images too; its vision tower is kept in bf16. Send them in
"images", as multipart files or inside the state: see Images for the
format, the limits and the full results. Zero-shot, on the same public items for both builds:
| 300 items per set | Eikos-27B (bf16) | Eikos-27B-FP8 |
|---|---|---|
| MME (yes/no) | 89.0% | 90.0% |
| MMStar | 70.7% | 70.7% |
| SEED-Bench-2-Plus | 74.0% | 74.3% |
| ScreenSpot-v2, no marks | 70.7% | 71.3% |
| ScreenSpot-v2, 4×4 grid drawn | 61.0% | 60.0% |
| ScreenSpot-Pro, no marks | 66.0% | 65.7% |
| Same answer as bf16: all / confident (≥0.9) | — | 97.6% / 100.0% |
Images are harder than our text suites: the bf16 model is at least 90% confident on only 25% of these items. Of the 44 answers (out of 1,800) that differ from bf16, all are on items where bf16 itself was below 0.7 confidence.
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-FP8 | |
|---|---|---|
| Size | 55.6 GB | 31.2 GB |
| JevBench public — original / hard | 100.0 / 82.0 | 100.0 / 83.8 |
| DecisionBench — medium / hard | 89.1 / 78.2 | 88.7 / 78.8 |
| General battery (9 tasks) | 82.6 | 82.8 |
| Finance (CUAD, sentiment, FinQA-judge) | 85.4 | 85.3 |
| Trade rules — seen / unseen | 85.4 / 87.4 | 85.6 / 86.9 |
| Compositional rules — same type / new domain / rulebooks | 95.6 / 94.3 / 95.3 | 95.6 / 94.7 / 95.0 |
| ECE (lower is better) | 0.043 | 0.042 |
| ≥0.90 confidence: decides / error | 44.1% / 2.5% | 44.2% / 2.6% |
| Same answer as bf16 (all / confident ≥0.9) | — | 98.8% / 100.0% |
It passes our release gate.
Third-party benchmarks
We ran other groups' public benchmarks on this build, with their own harnesses and scorers, in September 2026. Official placement depends on each maintainer.
Decision Index 0.2.1: 38 benchmarks in five areas, each chance-corrected (0 = random guessing, 100 = perfect). We ran the full suite with the kit's own runner and scorer.
| Index | Knowledge & Reasoning | Language | Retrieval & Classification | Tools & Automation | Arts & Human Taste | |
|---|---|---|---|---|---|---|
| Jev | 57.89 | 51.3 | 62.0 | 55.4 | 75.1 | 37.7 |
| Eikos-27B-FP8 | 55.46 | 39.9 | 63.3 | 55.9 | 74.4 | 39.8 |
- All 151,476 requests were answered and none was refused. Requests went one at a time on one RTX PRO 6000, with a median of 124 ms per request.
- On the 27 September board this would be 5th among open models; the best open model there is Surogate Rune 26B-A4B v3 at 57.44.
- The gap to Jev is in knowledge and reasoning (GPQA Diamond, MMLU-Pro, BBH). Eikos is ahead of Jev in Language, Retrieval & Classification and Arts & Human Taste, and 0.7 behind in Tools & Automation.
- Results and scripts: dataset. Submission: #19.
AgentRewardBench: judging whether a web agent completed its task. Test split, 1,106 trajectories, official scorer.
| Judge | Precision | Recall | F1 |
|---|---|---|---|
| Eikos-27B-FP8 | 78.9 | 73.6 | 76.1 |
| Jev (same inputs, through its API) | 75.7 | 65.4 | 70.2 |
| GPT-4o, Axtree (best published LLM judge) | 69.8 | 83.1 | 75.9 |
- Eikos read the same text as the published Axtree judges, and the four official questions went out as typed questions in one request.
- Judgments and method: dataset. Leaderboard submission: #12.
Jevals suite 0.1.0: Decision Score (100 = perfect, 0 = guessing the base rates). 300 items × 5 repeats per task, using the published states byte for byte.
| PubMedQA (yes/no) | Banking77 (choice, 77 options) | HelpSteer2 (score) | |
|---|---|---|---|
| Gemini 3.8 Flash | 73.0 | 74.1 | 4.6 |
| Eikos-27B-FP8 | 71.4 | 66.8 | 8.3 |
| Jev | 69.0 | 67.8 | 9.2 |
- Eikos is within Jev's confidence intervals on all three tasks.
- On these tasks Eikos is underconfident: its mean confidence is below its accuracy, and the Brier-based score penalizes that.
- Run records: gist. Listing request: #2.
JevBench v1.4.2: a setup check with the stock typesafe adapter on the
231 public items gave easy 48/48, standard 72/72 and hard 93/111. The official run, which adds held-out and sealed
items, speed and cost, is requested: #117.
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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