---

license: apache-2.0

library_name: transformers

tags:

• qwen
• qwen3_6
• merge
• mergekit
• reasoning
• code
• vision
• conversational
• gguf

base_model: ManniX-ITA/Qwen3.6-27B-Omnimerge-v4

---

Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

This is a Q4_K_L quantized GGUF version of ManniX-ITA/Qwen3.6-27B-Omnimerge-v4.

Why this quantization exists?

Based on my tests if you are a personal AI person with 24GB Vram, best quant is not Q4_K_M versions. Q4_K_L quantization is offers better

performance-size-speed trio. I think this model is performing better among other Qwen3.6 27B versions for general purpose agentic AI use case.

Original Model

• Base: ManniX-ITA/Qwen3.6-27B-Omnimerge-v4
• Architecture: qwen3_5
• Parameters: 27B
• Task: Image-Text-to-Text

Quantization

• Format: GGUF
• Quantization: Q4_K_L
• Quantized by: kdqemre

Usage with llama.cpp (Optimized Params for 24GB VRAM- You can try longer context length)

llama-server -m Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L.gguf \
  -ngl 99 \
  -c 16384 \
  -fa 1 \
  -np 1 --cache-type-k q8_0 --cache-type-v q5_1 --reasoning-format deepseek --reasoning-budget 2048 \
  --spec-type draft-mtp --spec-draft-n-max 3 --cache-type-k-draft q5_1 --cache-type-v-draft q5_1 --kv-unified \
  --port 8088

License

Original model licensed under Apache-2.0. This quantized version preserves the original license.