---
license: apache-2.0
base_model: Kwaipilot/KAT-Coder-V2.5-Dev
language:
  - en
  - zh
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - qwen3
  - code
  - coder
  - agentic-coding
  - agent
---

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# KAT-Coder-V2.5-Dev — APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [Kwaipilot/KAT-Coder-V2.5-Dev](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev) — Kwaipilot's Mixture-of-Experts model for agentic coding.

**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant) | [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)

## Available Files

| File | Profile | Best For |
|------|---------|----------|
| KAT-Coder-V2.5-Dev-APEX-I-Balanced.gguf | I-Balanced | Best overall — imatrix-enhanced |
| KAT-Coder-V2.5-Dev-APEX-I-Quality.gguf | I-Quality | Highest quality with imatrix |
| KAT-Coder-V2.5-Dev-APEX-Quality.gguf | Quality | Highest quality (no imatrix) |
| KAT-Coder-V2.5-Dev-APEX-Balanced.gguf | Balanced | General purpose |
| KAT-Coder-V2.5-Dev-APEX-I-Compact.gguf | I-Compact | Consumer GPUs, imatrix-enhanced |
| KAT-Coder-V2.5-Dev-APEX-Compact.gguf | Compact | Consumer GPUs |
| KAT-Coder-V2.5-Dev-APEX-I-Mini.gguf | I-Mini | Smallest viable, fastest inference |

## What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers (first/last 5) get higher precision, middle layers compress more aggressively. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

In MoE models the routed-expert FFN tensors dominate the weight budget but only ~8/256 experts activate per token, so APEX compresses middle-layer experts hardest while preserving edge layers, attention, and the always-active shared expert.

See the [APEX project](https://github.com/mudler/apex-quant) for full details.

## Architecture

- **Model**: KAT-Coder-V2.5-Dev (Qwen3_5MoeForConditionalGeneration)
- **Layers**: 40 · **Experts**: 256 routed + 1 shared (8 active per token)
- **Attention**: 16 heads / 2 KV, hybrid (full attention every 4th layer)
- **Calibration**: v1.3 diverse dataset

> Note: the config advertises an image token, but the released checkpoint ships no vision encoder weights, so these are text-only GGUFs (no mmproj).

## Run with LocalAI

```bash
local-ai run mudler/KAT-Coder-V2.5-Dev-APEX-GGUF@KAT-Coder-V2.5-Dev-APEX-I-Balanced.gguf
```

## Credits

APEX is brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp). Base model by [Kwaipilot](https://huggingface.co/Kwaipilot).