---

license: apache-2.0

library_name: gguf

pipeline_tag: image-text-to-text

language:

• en
• zh

tags:

• zen5
• zenlm
• hanzo
• gguf
• moe
• 3b-active
• long-context
• multimodal
• vision-language
• zen-5

---

Zen5

Canonical default of the Zen5 family. Multimodal sparse MoE (image + text in → text out) with 35B total / 3B active parameters per token, 256K context. The everyday Zen5 model — agentic-trained, fast at scale, frontier-quality vision-language reasoning at a 3B-active compute budget.

Part of the canonical Zen5 ladder:

| SKU | Hardware fit | This repo |

|---|---|---|

| zen5-flash | anything (4 GB VRAM) | zen-5-flash-gguf |

| zen5-mini | 32 GB | zen-5-mini-gguf |

| zen5 (default) | 24 GB+ VRAM (Q4_K) | ← you are here |

| zen5-pro | Mac M4 Max / DGX Spark / H100 80GB | zen-5-pro-gguf |

| zen5-max | Mac Studio M3 Ultra 512GB / 8x H100 | zen-5-max-gguf |

Files

| File | Format |

|---|---|

| main GGUF (*-Q4_K.gguf) | GGUF Q4_K (text + vision), refusal-orthogonalized |

| mmproj-model-f16.gguf | multimodal vision projector — load alongside the main GGUF for image input |

Run

Hosted via the Hanzo gateway (api.hanzo.ai) as zen5.

Local with llama.cpp (CLI / server) or zen5-engine:

hf download zenlm/zen-5-gguf --local-dir gguf
MAIN=$(ls gguf/*-Q4_K.gguf | head -1)

# text-only chat
llama-cli -m "$MAIN" -p "Explain MoE inference."

# vision-language (image input)
llama-cli -m "$MAIN" \
          --mmproj gguf/mmproj-model-f16.gguf \
          --image path/to/screenshot.png \
          -p "Describe this UI and propose a fix."