---
license: apache-2.0
base_model: Qwen/Qwen3-0.6B
tags:
  - mini-jev
  - decision-model
  - tool-selection
  - tool-use
  - agents
  - action-selection
  - decision-making
  - finite-choice
  - qwen3
  - peft
---

# mini-Jev Qwen3-0.6B - step 10,626

mini-Jev is a **0.6B agentic decision model specialized for finite-choice tool and action selection**. Give it a state, a question, and candidate actions; it returns a probability for each candidate. It uses a Qwen3-0.6B backbone with an adapter and a compact decision head. Candidate scoring is permutation equivariant, and the model returns finite-choice probabilities rather than generated text or tool arguments.

This is an **early 10,626-update checkpoint**. Training is planned through 50,000 updates; more training and broader decision supervision are coming. This interim release is not the final checkpoint.

## Release files and revisions

The current [`step-010626`](https://huggingface.co/samatv256/mini-Jev/tree/step-010626) checkpoint loads its adapter from [`adapter/`](adapter/), its decision-head weights from [`decision_head.safetensors`](decision_head.safetensors), and its inference loader from [`inference.py`](inference.py). Use these with `config.json` and the separately downloaded Qwen3-0.6B base model.

The retained root `model.safetensors` and `odm_mini.py` belong to the previous v1 baseline. They are preserved only for history and backward reference; they are not the step 10,626 weights or loader. To use that baseline, pin the [previous revision](https://huggingface.co/samatv256/mini-Jev/tree/02acc03cc28ec0b99705a8459ea987274fa7987f).

## Measured results

| Evaluation | Result | Scope |
|---|---:|---|
| BFCL V1 Multiple Function - Jev function-selection adaptation | **96.5% (193/200)** | Selects the supplied function identity from 2-4 definitions. **Not an official BFCL leaderboard score**; does not generate arguments or execute calls. |
| Strict held-out Choice | **65.6% (63/96)** | A selected, bucket-balanced validation slice, not the full validation split. |
| Bespoke OOD typed decisions | **72.0% (36/50)** | Small synthetic sanity check with straightforward distractors; not a public benchmark. |
| LocalLLaMA/typed-decisions, full test set | **34.0% (680/2,000)** | Choice 27.0%, Noul 52.2%, Score 25.6%. General typed decisions remain a weakness. |

![BFCL selection accuracy at step 0 and step 10,626](assets/bfcl-step-comparison.svg)

![BFCL selection accuracy by candidate count](assets/bfcl-by-candidates.svg)

![Accuracy snapshot across separate evaluations](assets/evaluation-summary.svg)

These evaluations have different datasets and denominators; their percentages are not directly comparable. The BFCL adaptation uses the [BFCL V1 Multiple Function AST examples](https://github.com/EnlightenedAI/BFCL/blob/main/berkeley-function-call-leaderboard/bfcl_eval/data/README.md). The typed-decision result uses the [LocalLLaMA/typed-decisions test split](https://huggingface.co/datasets/LocalLLaMA/typed-decisions).

## Intended use and limits

The current strength is **finite-choice tool and action selection** from state and supplied candidates. The model is less reliable for general typed decisions and for large candidate sets. It is not a general probability or classification model, and its probabilities have not been calibrated for arbitrary domains. A selected 17+ candidate held-out slice scored 4/17, so large sets need particular care.

## Use

A CUDA GPU with BF16 support is required for this 4-bit package. Install the tested dependencies from requirements.txt. The [Qwen3-0.6B base model](https://huggingface.co/Qwen/Qwen3-0.6B) is downloaded separately on first load; this repository contains only the adapter and decision head.

    from inference import MiniJev

    model = MiniJev.load(".")
    result = model.predict(
        state={"user_goal": "Find tomorrow's weather forecast for Paris."},
        question="Given the current state and available options,\nwhich option should be selected?",
        question_type="choice",
        answer_options=[
            {"id": "weather", "type": "tool", "label": "get_weather_forecast",
             "description": "Look up the weather forecast for a place and date."},
            {"id": "invoice", "type": "tool", "label": "calculate_invoice_total",
             "description": "Add amounts on an invoice."},
            {"id": "email", "type": "tool", "label": "send_email",
             "description": "Send an email message."},
        ],
    )
    print(result["selected_id"])
    print(result["options"])

Option IDs are bookkeeping and are excluded from model text. The model scores each supplied option and normalizes probabilities over the finite set. Input branches are limited to 8,192 tokens.

## Data attribution

Decision supervision for this checkpoint draws in part on [Jev Decisions v1](https://huggingface.co/datasets/samatv256/jev-decisions-v1), a public dataset released under CC BY 4.0. Its public upstream datasets carry their own attribution and licensing terms; see the [dataset card](https://huggingface.co/datasets/samatv256/jev-decisions-v1) and [upstream license notes](https://huggingface.co/datasets/samatv256/jev-decisions-v1/blob/main/SOURCE_LICENSES.md).

## License

Apache-2.0. The Qwen3-0.6B base model is separately licensed by Qwen under Apache-2.0. See LICENSE and the [base model card](https://huggingface.co/Qwen/Qwen3-0.6B).