mudler/Agents-A1-APEX-GGUF

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Agents-A1 — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of InternScience/Agents-A1 — a 35B Mixture-of-Experts agentic model built to scale heterogeneous agentic abilities across long-horizon search, engineering, scientific research, instruction following, and tool calling.

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

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 (first/last 5) get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only ~8/256 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers and keeping attention, SSM/Mamba, and shared-expert tensors at higher precision.

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

Architecture

• —Model: Agents-A1 (Qwen3_5MoeForConditionalGeneration, Qwen3.5 35B-A3B MoE base)
• —Layers: 40
• —Experts: 256 routed + 1 shared (8 active per token)
• —Total Parameters: ~35B
• —Active Parameters: ~3B per token
• —Attention: Hybrid (full attention every 4th layer, linear otherwise)
• —Vision: Built-in vision encoder (mmproj included)
• —APEX Config: 5+5 symmetric edge gradient across 40 layers
• —Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, agentic traces, Wikipedia)

Run with LocalAI

local-ai run mudler/Agents-A1-APEX-GGUF@Agents-A1-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. Base model by InternScience.