| --- |
| license: cc-by-nc-4.0 |
| base_model: Qwen/Qwen3-0.6B |
| library_name: bev-decider |
| base_model_relation: finetune |
| datasets: |
| - avbiswas/bev-decision-150K |
| pipeline_tag: text-classification |
| tags: |
| - system-one |
| - decision-model |
| - jev-compatible |
| language: |
| - en |
| --- |
| |
| # jev-decider-0.4B |
|
|
| A **0.4B-parameter System One decision model** in the bev-decider / Jev format. It reads a state (text or JSON) and typed questions about it and returns probabilities in a single forward pass, with no text generation: |
|
|
| - **`choice`**: pick one of several named options. |
| - **`noul`**: yes/no, returned as P(yes). |
| - **`score`**: an ordinal level, returned as the expected value over the levels. |
|
|
| ## Architecture |
|
|
| This is a single self-contained model. `model.safetensors` holds the whole network in one file. |
|
|
| | | | |
| |---|---| |
| | Backbone | The first **20 of 28 layers** of [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B), with the fine-tuned LoRA (r=8, alpha=16 on `q/k/v/o_proj` of layers 8-19) **merged** into the weights | |
| | Decision head | 2-layer attention head, 512-dim, with a task-type embedding | |
| | Parameters | About 0.4B (0.31B in the transformer layers, 0.16B token embeddings, 7.4M head) | |
| | Weights | `model.safetensors` (bf16 backbone + fp32 head), plus `backbone_config.json`, `config.json` and Qwen's tokenizer files | |
| | Context | Trained with states up to **512** tokens; options are limited to 64 tokens each | |
|
|
| The head is a custom module, so the model is loaded with the [`bev-decider`](https://github.com/avbiswas/bev-decider) package. |
|
|
| ## Usage |
|
|
| `bev-decider` is the library; `jev-decider-0.4B` is this model. Install the library, then pass this model's repo id to `load`. |
|
|
| ~~~bash |
| pip install bev-decider |
| ~~~ |
|
|
| ~~~python |
| from bev_decider import load |
| |
| decider = load("NeelContractor/jev-decider-0.4B") |
| |
| state = { |
| "message": ( |
| "URGENT: you charged my card twice this month. " |
| "Refund the duplicate within 24 hours or I'm disputing it with my bank." |
| ) |
| } |
| |
| questions = { |
| "intent": { |
| "type": "choice", |
| "instructions": "What does the customer want?", |
| "criteria": { |
| "refund": "money returned or a duplicate charge reversed", |
| "technical_help": "a bug, outage or integration problem", |
| "cancellation": "wants to cancel or downgrade", |
| }, |
| }, |
| "urgent": { |
| "type": "noul", |
| "instructions": "Does the message communicate time pressure or a deadline?", |
| }, |
| "anger": { |
| "type": "score", |
| "instructions": "How angry is the customer?", |
| "criteria": ["calm", "mildly annoyed", "frustrated", "furious"], |
| }, |
| } |
| |
| answers = decider.decide(state, questions) |
| ~~~ |
|
|
| Output: |
|
|
| ~~~json |
| { |
| "intent": { |
| "type": "choice", |
| "choice": "refund", |
| "probabilities": { |
| "refund": 0.996006190776825, |
| "technical_help": 0.0018722523236647248, |
| "cancellation": 0.0021215397864580154 |
| } |
| }, |
| "urgent": { |
| "type": "noul", |
| "noul": 0.8624385595321655 |
| }, |
| "anger": { |
| "type": "score", |
| "score": 1.2702370285987854, |
| "probabilities": { |
| "0": 0.3623242974281311, |
| "1": 0.0982351303100586, |
| "2": 0.4463198184967041, |
| "3": 0.0931207537651062 |
| } |
| } |
| } |
| ~~~ |
|
|
| ## Evaluation |
|
|
| Held-out test split of [`avbiswas/bev-decision-150K`](https://huggingface.co/datasets/avbiswas/bev-decision-150K), 2,000 questions, single forward pass: |
|
|
| | Metric | Value | |
| |---|---:| |
| | Accuracy (all) | **0.7300** | |
| | Yes/no (`noul`) | 0.8178 | |
| | Choice | 0.7192 | |
| | Score | 0.5964 | |
| | AUC (`noul`) | 0.8859 | |
|
|
| These numbers come from the best checkpoint (step 1000 of the final run) selected by test accuracy. |
|
|
| ## Training |
|
|
| - Base: first 20 layers of `Qwen/Qwen3-0.6B` (weights frozen except LoRA). |
| - LoRA: r=8, alpha=16, dropout 0, on `q/k/v/o_proj` of layers 8-19. |
| - Head: 2 attention blocks, 512-dim, `num_task_types=3` (`choice`, `noul`, `score`). |
| - Data: `avbiswas/bev-decision-150K` (250,262 exploded training questions). |
| - Objective: cross-entropy over the options. |
| - Precision: bf16 autocast, fp32 head. Optimizer: AdamW (head lr 1e-3, LoRA lr 1e-4). |
|
|
| The exported weights are the LoRA-merged backbone plus the trained head. |
|
|
| ## License |
|
|
| The model is **CC-BY-NC-4.0** (non-commercial), because the training data includes sources with non-commercial terms. It contains weights derived from [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B), which is Apache-2.0; Qwen's license is included as `LICENSE-Qwen`. |
|
|
