| license: apache-2.0 | |
| base_model: Qwen/Qwen3-4B | |
| library_name: anyjev | |
| pipeline_tag: text-generation | |
| language: [en] | |
| version: v1 | |
| tags: [anyjev, tacit, decision-model, typed-decisions, churn, customer-success, slack, self-distillation, lora, v1, qwen3-4b] | |
| datasets: [workmagicio/slack-churn] | |
| metrics: [accuracy, f1, expected_calibration_error] | |
| # workmagic-jev-decision | |
| - 版本：v1 | |
| - 基座：`Qwen/Qwen3-4B` | |
| 用 AnyJev **自蒸馏**（self-distillation）得到的一次前向决策模型：把老师 `Decider(level=L0)` 在 **Slack 渠道流失（churn）** 决策上的答案蒸馏进单次前向读出；训练**无人工标签**。 | |
| ## 用法 | |
| ```python | |
| from anyjev import Tacit | |
| tacit = Tacit.from_pretrained("workmagicio/workmagic-jev-decision", adaptive=False) # 可用 revision="v1" 固定版本 | |
| d = tacit.decide( | |
| state="...slack conversation...", | |
| question="...how likely is the customer to churn...", | |
| options=["low", "medium", "high"], | |
| ) | |
| print(d["answer"], d["probs"]) | |
| ``` | |
| ## 训练 | |
| 基座 `Qwen/Qwen3-4B`，采用 AnyJev readout 提示词格式，LoRA 自蒸馏（只在答案 token 上算 loss）。 | |