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| title: README |
| emoji: 🔁 |
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| # Hopit |
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| **AI that gets better at its job by doing it.** |
|
|
| Hopit is a continual-learning lab. We build models and |
| harnesses that learn new tasks, keep what they already know, and improve inside |
| the enterprises that use them. → [hopit.ai](https://hopit.ai) |
|
|
| ## Decision models |
|
|
| Small models that answer a typed decision question in one forward pass, with a |
| calibrated probability for each option. |
|
|
| | Model | What it is | Where it stands | |
| |---|---|---| |
| | [hopper](https://huggingface.co/HopitAI/hopper) | Hopper 1.0 — LoRA on Qwen3.5-4B | #2 in JevBench's Jev-class capability ranking, 63.5 against the leader's 64.7¹ | |
| | [hopper-g](https://huggingface.co/HopitAI/hopper-g) | Hopper (G) — general-purpose, 4.66B served | Top five of 46 systems under 5B on the Jev Decision Index² | |
|
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| Both are released for research and demonstration only — see each card for its |
| licence and training data. Code: [hopit-ai/hopper](https://github.com/hopit-ai/hopper). |
|
|
| ## Continual learning |
|
|
| Our update method lifted an internal tool-use evaluation from 57.9% to 66.1% |
| without losing earlier abilities on the retention suites we track. That is one |
| seed and an internal measurement; we will publish the protocol and artifacts |
| before treating it as established. |
|
|
| ## Track record |
|
|
| Before continual learning, we built open models and public benchmark suites for |
| fashion, and held ourselves to them in public. They remain the standard our newer |
| work has to clear. |
|
|
| | Track | Models | Benchmark | |
| |---|---|---| |
| | Retrieval | [moda-fashion-distilled](https://huggingface.co/HopitAI/moda-fashion-distilled) · [matryoshka](https://huggingface.co/HopitAI/moda-fashion-matryoshka) · [crossdomain](https://huggingface.co/HopitAI/moda-fashion-crossdomain) · [pro-lite](https://huggingface.co/HopitAI/moda-pro-lite) | [MODA](https://hopit-ai.github.io/Moda/) | |
| | Attribute extraction | [crop](https://huggingface.co/HopitAI/moda-ner-v-crop) (MIT) · [catalog](https://huggingface.co/HopitAI/moda-ner-v-catalog) · [full-body](https://huggingface.co/HopitAI/moda-ner-v-fullbody) (CC BY-NC 4.0) | [MODA_NER](https://hopit-ai.github.io/Moda_ner/) | |
|
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| ## How we publish |
|
|
| - Every rank is quoted with its qualifier. |
| - Protocols are frozen before inference, predictions are hashed before labels |
| open, and scorers fail closed. |
| - The runs we lose are published beside the runs we win. |
|
|
| ## Work with us |
|
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| We deploy with a small forward-deployed team inside your environment. Your data |
| and your deployed models stay yours. The approach is ideal for regulated |
| enterprises. → [hopit.ai](https://hopit.ai) |
|
|
| <sub>¹ JevBench v1.4.2, 24 September 2026 snapshot, scored by an independent |
| maintainer. Second on capability; fifth on the composite score, which also |
| weighs speed and cost. ² Jev Decision Index 0.2.1, 28 September 2026: Hopper (G) |
| 1.2 is third of 46 systems under 5B on the chance-corrected headline score |
| (40.77), within 0.1 of fourth, and 18th of 70 overall. The edition is 76% scored.</sub> |
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|
