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title: README
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# Binomial Technologies

**Open-source ML specialists for finance.**

We build small (≤500M parameter) task-specific models for finance under Apache 2.0 — engineered for sub-second CPU inference, public eval tables, and drop-in compatibility with the pipelines quant teams actually run.

## Thesis

For narrow finance tasks, small specialists beat:

- **Frontier LLMs** on cost and latency by two orders of magnitude
- **Dictionary methods** (Loughran-McDonald, FinBERT) on context-awareness and number of dimensions captured per article
- **Closed bespoke fine-tunes** on auditability — every model card here ships with eval tables, methodology, and explicit limitations

Nobody has open-sourced this stack at this fidelity. That's the gap we fill.

## The model zoo

Six task-specialists named after thinkers in quantitative finance. One per quarter through 2027.

| Model                                                                                   | Task                                                                                | Status                     |
| --------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- | -------------------------- |
| **[binomial-marks-1](https://huggingface.co/BinomialTechnologies/binomial-marks-1)** | Earnings-call NLP scoring — 23 outputs (10 topics × {mention, direction}, 3 tone) | Shipped (v1.1, April 2026) |
| binomial-shannon-1                                                                      | Financial news characterizer                                                        | In progress                |
| binomial-godel-1                                                                        | Realized volatility forecasting                                                     | In design                  |
| binomial-mandelbrot-1                                                                   | Market regime classification                                                        | In design                  |
| binomial-simons-1                                                                       | Order-flow / microstructure                                                         | In design                  |
| binomial-bachelier-1                                                                    | Vol surface dynamics                                                                | v2 cycle                   |

All models Apache 2.0. All run under 100 ms on CPU (most under 30 ms).

## What we publish

- **Weights** on this org's HF Hub
- **Runtime helpers** as PyPI packages — `pip install binomial-marks`
- **Source, training scripts, eval harnesses** — [github.com/Binomial-Capital-Management/binomial-ai-research](https://github.com/Binomial-Capital-Management/binomial-ai-research)
- **Model cards** — full eval tables, panel comparisons, tier (1 / 2 / 3) declared upfront

## Tier system

Each model card declares one of three tiers honestly:

| Tier        | Definition                                                                                         |
| ----------- | -------------------------------------------------------------------------------------------------- |
| **1** | Production-validated against measurable outcomes (returns, realized vol). Tradeable as a feature.  |
| **2** | Research preview. Eval against an LLM panel + held-out test sets. Use as input to your own models. |
| **3** | Experimental.                                                                                      |

We do not host inference. Weights are yours to deploy.

## Contact

ilay@binomialtec.com