# Kev-4B (GGUF)

**Credit:** This is a quantized GGUF version of the original [Kev-4B](https://huggingface.co/jaredpalmer/kev-4b) created by **Jared Palmer**. All credit for the model architecture, training, and capabilities goes to the original author. 

## Model Summary
Kev-4B is a highly efficient **decision model**. Unlike standard LLMs, it does not generate text. Instead, it reads a document (the state) and a set of typed questions, returning a calibrated probability distribution over the provided options in a single forward pass. 

This repository contains the **Q4_K_M GGUF** quantized version of the model, making it incredibly lightweight and optimized for local CPU/GPU inference via `llama.cpp` or similar local inference engines. 

## Intended Use
It is intended for developers building AI agents that need to:
* **Classify, route, triage, or check documents.**
* Obtain strict mathematical probabilities that can be thresholded (e.g., to send uncertain cases to human review).
* Serve the TypeSafe public System One API (`POST /v1/systemone`).

## Model Details
* **Original Developer:** Jared Palmer ([jaredpalmer/kev](https://github.com/jaredpalmer/kev))
* **Model Type:** Decision model (causal language-model backbone run prefill-only, with a pointer head over the options)
* **Format:** GGUF (`Q4_K_M` quantization)
* **Backbone:** Qwen3.5-4B-Base (32 layers, frozen)
* **Context Length:** States of up to 65,536 tokens, plus at least 8,192 tokens per question. 
* **Languages:** English
* **License:** Apache-2.0