# HIT-TMG (Lychee Team): kalm-jev

> A training-free Choice, Score and Noul service from the KaLM-Embedding team at HIT Shenzhen. Each option, level or yes/no criterion is scored as a document by the frozen KaLM-Reranker-V1-R2 rerankers (Nano, Small, Large), giving a probability per option with no generated text.

- Page: https://systemonemodels.ai/hit-tmg/kalm-jev
- API: https://api.systemonemodels.ai/v1/models/hit-tmg/kalm-jev
- Download: `pip install systemonemodels && systemone pull hit-tmg/kalm-jev`

## Facts

| | |
|---|---|
| Maker | HIT-TMG (Lychee Team) (https://systemonemodels.ai/hit-tmg) |
| Decides | choice, score, noul |
| Architecture | kalm-jev |
| Base model | kalm-embedding/kalm-reranker-v1-nano-r2 |
| Licence | No licence on the KaLM-Jev code (treated as non-commercial); KaLM-Reranker-V1-R2 weights Apache-2.0 |
| Availability | Open weights |
| Released | 2026-09-21 |
| Latest version | 0.1.0 |

## Reported evaluation

Suite: JevBench public set (231 items), the maker's own regression run, KaLM-Jev Nano in BF16. Numbers are the publisher's own.

- Decision accuracy: 52.8%

## Model card

<!-- generated by scripts/seed_catalog.py; edit content/models/catalog.yaml -->

# KaLM-Jev

A training-free Choice, Score and Noul service from the KaLM-Embedding team at HIT Shenzhen. Each option, level or yes/no criterion is scored as a document by the frozen KaLM-Reranker-V1-R2 rerankers (Nano, Small, Large), giving a probability per option with no generated text.

KaLM-Jev adds no trained weights: fixed instruction adapters drive KaLM-Embedding's own KaLM-Reranker-V1-R2 checkpoints (T5Gemma-2 based, Apache-2.0), one model per local HTTP process, so there are no files to pull here. The KaLM-Jev code carries no licence. Built by Xinping Zhao, first author of KaLM-Reranker-V1. The authors say the outputs are not calibrated probabilities and that their results do not establish Jev-equivalent quality. On their own 36-case smoke set, Choice accuracy is 67%, 92% and 83% for Nano, Small and Large, but Noul without supplied criteria answered yes on every case. Validated on an H100 MIG slice only; a demo Space runs Small.

## What it decides

- **choice** — picks one option from a set
- **score** — places the input on an ordered scale
- **noul** — answers a yes/no question with one probability

## At a glance

| | |
|---|---|
| Base model | `KaLM-Embedding/KaLM-Reranker-V1-Nano-R2` |
| Maker | HIT-TMG (Lychee Team) |
| Released | 2026-09-21 |
| License | No licence on the KaLM-Jev code (treated as non-commercial); KaLM-Reranker-V1-R2 weights Apache-2.0 |
| Reported accuracy | 52.8% |
| Reported latency | about 52 ms p50 for a 3-option Choice on Nano (30 ms with a warm document cache), batch 8, H100 MIG slice |

## Read more

- [Code and results](https://github.com/KaLM-Embedding/KaLM-Jev)
- [Demo Space](https://huggingface.co/spaces/Yuki131/KaLM-Jev)
- [KaLM-Reranker-V1-R2 checkpoints](https://huggingface.co/collections/KaLM-Embedding/lychee-kalm-reranker)

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