mudler/Mistral-Small-4-119B-2603-APEX-GGUF

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Mistral-Small-4-119B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Mistral-Small-4-119B-2603.

Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team | APEX Project | Technical Report

Benchmark Results

Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.

Available Files

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 get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

See the APEX project for full details, technical report, and scripts.

Architecture

• —Model: Mistral-Small-4-119B-2603 (Mistral4/DeepSeek-V2 style)
• —Layers: 36
• —Experts: 128 routed + 1 shared (4 active per token)
• —Total Parameters: ~119B
• —Active Parameters: ~11-12B per token
• —Attention: Multi-head Latent Attention (MLA, kvlorarank=256, qlorarank=1024)
• —Vision: Pixtral encoder (mmproj included)
• —Context: 1M tokens (YaRN RoPE)
• —APEX Config: 5+5 symmetric edge gradient across 36 layers, MLA-aware tensor mapping

Run with LocalAI

local-ai run mudler/Mistral-Small-4-119B-2603-APEX-GGUF@Mistral-Small-4-119B-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.