---

license: apache-2.0

base_model: ProCreations/grug-v1.1-qwen-3.8-27b

tags:

• grug
• gguf
• llama.cpp
• token-efficient
• agentic

language:

• en

pipeline_tag: text-generation

---

grug-v1.1-qwen-3.8-27b — GGUF

grug in box for llama.cpp. same brain as

ProCreations/grug-v1.1-qwen-3.8-27b,

just smaller box.

| file | size | who for |

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

| grug-27b-v1.1-Q8_0.gguf | 28.6 GB | want closest to full weight |

| grug-27b-v1.1-Q6_K.gguf | 22.1 GB | very good, less space |

| grug-27b-v1.1-Q5_K_M.gguf | 19.2 GB | good middle |

| grug-27b-v1.1-Q4_K_M.gguf | 16.5 GB | grug pick this one for most cave |

| grug-27b-v1.1-Q3_K_M.gguf | 13.3 GB | small cave, tight RAM |

| mmproj-grug-27b-v1.1-f16.gguf | 0.9 GB | eyes. only need if you show picture |

every quant load-tested with llama-bench before upload. built with llama.cpp

7c35571e.

run grug

llama-cli -m grug-27b-v1.1-Q4_K_M.gguf -p "write a function that flattens a nested list"

with eyes:

llama-mtmd-cli -m grug-27b-v1.1-Q4_K_M.gguf \
  --mmproj mmproj-grug-27b-v1.1-f16.gguf --image cave-painting.png -p "what this?"

grug think small

grug reason inside <think> in caveman, then answer in normal english. on agent

step grug spend 20 think token where base model spend 108.5. on HumanEval

grug spend 79.5 where base spend 559.

use medium reasoning effort. xhigh make grug worse at picking tool (76.5 vs

97.1) — full table and chart on the

main model card.

apache-2.0.