---
language: en
license: apache-2.0
library_name: unsloth
base_model: unsloth/Qwen3.5-0.8B
tags:
- tool-calling
- distillation
- calibration
- research
- devops
---

# Mini-Jev: Calibrated DevOps Tool-Calling Agent

Mini-Jev is a research project. It is a distilled version of the Beam-1.0-501B model. It is not officially affiliated with or endorsed by the creators of the original Beam model.

## The Problem: Overconfidence
Most small models are "overconfident"—they will guess a tool even when they are unsure. In a production DevOps environment, a wrong tool call (like triggering a rollback by mistake) can be catastrophic.

## The Solution: RLCD
This model was trained using **Reinforcement Learning for Calibrated Decisions (RLCD)**. Instead of just training for accuracy, we optimized for:
1. **Cross-Entropy:** Getting the right answer.
2. **Brier Score:** Ensuring the probability matches the empirical truth.
3. **KL Divergence:** Distilling the epistemic uncertainty of a 501B parameter teacher.

## Calibration
The model is calibrated using post-hoc Temperature Scaling ($T^*$). This ensures that when the model says it is 70% confident, it is actually correct approximately 70% of the time.

## Usage (System 1 / System 2 Cascade)
This model is designed to act as **System 1**. 
- If confidence $\hat{p} \ge 0.7$, execute the tool immediately.
- If confidence $\hat{p} < 0.7$, escalate to a larger expert model (System 2).

```python
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained("khide/mini-jev-rlcd")
FastLanguageModel.for_inference(model)
# Use the calibrated logits to route your requests!