---
license: apache-2.0
base_model: Qwen/Qwen3-4B-Base
pipeline_tag: text-classification
library_name: peft
tags:
- decision-model
- calibrated-probabilities
- lora
- qwen3
- typesafe-compatible
---

> Free trial API: https://leo.kognare.com (key: `free`). Code: https://github.com/SuparvaCode/leo. On Hugging Face the base model is not bundled; `Leo.load` downloads `Qwen/Qwen3-4B-Base` at the pinned revision.

# Leo-1 (4B)

Leo's final model from the v5 round: an open-weight decision model. Send a **state** (text or JSON) and typed questions (`choice`, `score`, `noul` yes/no); get calibrated probabilities from one forward pass. It speaks the `POST /v1/systemone` wire format, so TypeSafe/Jev clients (for example naturalcodz) work by changing the base URL.

Internally this is `leo-4b-soup3`: an exact weight merge of leo-4b-v5 (70%) and leo-4b-v5.1 (30%) on `Qwen/Qwen3-4B-Base` (revision `906bfd4b`, bundled in `base/`), with a LoRA of rank 64, re-calibrated on the dev set (dev ECE 0.007).

## What is in this folder

| path | what |
|---|---|
| `adapter/`, `leo_head.safetensors`, `leo_config.json` | Leo-1's weights and calibration |
| `base/` | the exact Qwen3-4B-Base weights it was trained on (Apache-2.0), so nothing is downloaded |
| `leo/` | inference code and the HTTP server |
| `results/` | the benchmark files behind the numbers below |
| `verify.py` | offline self-check: re-answers the blind short-input suite and compares with the recorded results |
| `SHA256SUMS.txt` | checksums of every file |

Total size is about 8 GB. Running it needs about 9 GB of GPU memory in bf16 (for example an L4, A10, RTX 3090/4090 or better), or a CPU with about 12 GB free RAM (slow: about 3–4 s per request).

## Use

```bash
pip install -r requirements.txt
python verify.py                      # prints OK when the files are intact
```

```python
import sys; sys.path.insert(0, r"D:\Leo-1")
from leo.infer import Leo
leo = Leo.load(r"D:\Leo-1", dtype="bf16")        # device="cpu" if the GPU is too small
print(leo.system_one("I was charged twice this month.",
      {"team": {"type": "choice", "instructions": "Which team?", "criteria": {"billing": None, "technical": None, "other": None}},
       "angry": {"type": "noul", "instructions": "angry"}}))
```

HTTP server (127.0.0.1 only unless a key is set):

```bash
cd D:\Leo-1
set LEO_API_KEY=change-me
python -m leo.serve --model . --port 8000 --canonicalize input
```

`--canonicalize input` rewrites bare yes/no conditions ("angry") into questions, which raised the short-input score from 0.953 to 0.972. States longer than 8,192 tokens get a 422 by default instead of being silently cut.

## Results (identical requests; Jev = live `jev-1.13.0`, September 2026)

| benchmark | Leo-1 | Jev 1.13 | released leo-1.7b-v3 |
|---|---|---|---|
| Browser tasks, jev-ultrafast (21 runs) | **18/21** | 18/21 | 18/21 |
| naturalcodz drop-in (37 scenarios) | 35/37 | 36/37 | 32/37 |
| Blind short-input suite (211 conditions) | 0.953 (0.972 with rewrite) | 0.976 | 0.872 |
| Short-input calibration error (lower is better) | **0.029** | 0.075 | 0.093 |
| JevBench all / standard / hard | 0.701 / 0.944 / 0.414 | 0.861 / 0.986 / 0.721 | 0.697 / 0.958 / 0.396 |
| Held-out classification, mean accuracy | 0.650 | 0.689 | 0.628 |
| tweet_topic | **0.840** | 0.790 | 0.845 |
| Held-out calibration error (lower is better) | **0.069** | 0.167 | 0.099 |
| Multilingual (Belebele / MMMLU / INCLUDE) | 0.681 / 0.481 / 0.576 | 0.917 / 0.861 / 0.775 | 0.692 / 0.479 / 0.491 |
| False DONE on held-out browser screens | 0.000 | not measured | 0.067 |
| Option-order flips, emotion / fin_topic | 0.074 / 0.101 | 0.018 / 0.069 | 0.100 / 0.217 |

## Where it stands

- **Level with Jev or close:** browser agents (18/21), naturalcodz-style calls, short and one-word conditions, spam / PII / toxicity checks, routing, JevBench easy and standard. Its probabilities are better calibrated than Jev's on the short-input and held-out suites.
- **Clearly behind Jev:** hard reasoning (JevBench hard 0.41 vs 0.72: multi-hop, dates and numbers, ambiguous items), world knowledge (MMMLU 0.48 vs 0.86) and low-resource languages. It is not a Jev replacement for those.
- **Known flaws:** Google Flights still fails (it no longer claims DONE, but it does not finish the search); a refund message was routed to "sales" in naturalcodz; `isSafe` misses one insult (Jev misses the same one); option order still moves answers more than Jev (use `--order-views 2`).
- Always verify agent outcomes independently; never let a DONE trigger something irreversible.

Not trained on any Jev output: Jev was called only to score it. Weights and code are Apache-2.0; training datasets keep their own licences (see the Leo repository).