metadata
license: apache-2.0
base_model: ProCreations/grug-3b
tags:
- grug
- gguf
- llama.cpp
- reasoning
- token-efficient
language:
- en
pipeline_tag: text-generation
grug-3b-qat-q4-gguf
q4 that survive the squeeze.
normal q4 round the weight after training and hope. this one train WITH the rounding: every linear weight fake-quantized to asymmetric int4 (group 32) on each forward, straight-through gradient update the bf16 weight underneath. model learn weight that still work after Q4_K_M round them. same recipe as grug-9b-qat and grug-27b-qat.
trained on same data as ProCreations/grug-3b, so grug dialect and adaptive think length come through intact.
| file | size | note |
|---|---|---|
| grug-3b-qat-q4-Q4_K_M.gguf | 2.57 GB | the point of this repo |
| grug-3b-qat-q4-f16.gguf | 8.34 GB | qat weights unquantized, roll your own quant |
use the Q4_K_M one. plain (non-qat) quants live here.
llama.cpp support
Nanbeige4.2 not in upstream llama.cpp yet (issue
#26086). Nanbeige team PR
#25994 add it - weight-shared
depth loop, num_loops=2. until merge, build from that branch:
git clone --depth 1 --branch nanbeige42 https://github.com/Nanbeige/llama.cpp
cd llama.cpp && cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j
./build/bin/llama-cli -m grug-3b-Q4_K_M.gguf -p "What is 12 times 12?"
these gguf converted and load-probed with that branch.