Instructions to use ProCreations/grug-3b-qat-q4-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ProCreations/grug-3b-qat-q4-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ProCreations/grug-3b-qat-q4-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ProCreations/grug-3b-qat-q4-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ProCreations/grug-3b-qat-q4-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ProCreations/grug-3b-qat-q4-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ProCreations/grug-3b-qat-q4-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ProCreations/grug-3b-qat-q4-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ProCreations/grug-3b-qat-q4-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ProCreations/grug-3b-qat-q4-gguf:Q4_K_M

Use Docker

docker model run hf.co/ProCreations/grug-3b-qat-q4-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use ProCreations/grug-3b-qat-q4-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ProCreations/grug-3b-qat-q4-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ProCreations/grug-3b-qat-q4-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ProCreations/grug-3b-qat-q4-gguf:Q4_K_M

Ollama
How to use ProCreations/grug-3b-qat-q4-gguf with Ollama:
```
ollama run hf.co/ProCreations/grug-3b-qat-q4-gguf:Q4_K_M
```

Unsloth Studio

How to use ProCreations/grug-3b-qat-q4-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ProCreations/grug-3b-qat-q4-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ProCreations/grug-3b-qat-q4-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ProCreations/grug-3b-qat-q4-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use ProCreations/grug-3b-qat-q4-gguf with Docker Model Runner:
```
docker model run hf.co/ProCreations/grug-3b-qat-q4-gguf:Q4_K_M
```

Lemonade

How to use ProCreations/grug-3b-qat-q4-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ProCreations/grug-3b-qat-q4-gguf:Q4_K_M

Run and chat with the model

lemonade run user.grug-3b-qat-q4-gguf-Q4_K_M

List all available models

lemonade list

ProCreations commited on 4 days ago

Commit

b97e983

verified ·

1 Parent(s): 7b73229

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+---
+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](https://huggingface.co/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](https://huggingface.co/ProCreations/grug-3b-gguf).
+## llama.cpp support
+Nanbeige4.2 not in upstream llama.cpp yet (issue
+[#26086](https://github.com/ggml-org/llama.cpp/issues/26086)). Nanbeige team PR
+[#25994](https://github.com/ggml-org/llama.cpp/pull/25994) add it - weight-shared
+depth loop, `num_loops=2`. until merge, build from that branch:
+```bash
+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.