Instructions to use legraphista/shieldgemma-9b-IMat-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 legraphista/shieldgemma-9b-IMat-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 legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S

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 legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S

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 legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S

Use Docker

docker model run hf.co/legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use legraphista/shieldgemma-9b-IMat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "legraphista/shieldgemma-9b-IMat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "legraphista/shieldgemma-9b-IMat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S

Ollama
How to use legraphista/shieldgemma-9b-IMat-GGUF with Ollama:
```
ollama run hf.co/legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S
```
Unsloth Desktop
Docker Model Runner
How to use legraphista/shieldgemma-9b-IMat-GGUF with Docker Model Runner:
```
docker model run hf.co/legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S
```

Lemonade

How to use legraphista/shieldgemma-9b-IMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull legraphista/shieldgemma-9b-IMat-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.shieldgemma-9b-IMat-GGUF-Q4_K_S

List all available models

lemonade list

Atomic Chat

shieldgemma-9b-IMat-GGUF / README.md

legraphista

Upload README.md with huggingface_hub

b5f68f4 verified about 2 years ago

preview code

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	---
	base_model: google/shieldgemma-9b
	extra_gated_button_content: Acknowledge license
	extra_gated_heading: Access Gemma on Hugging Face
	extra_gated_prompt: "To access Gemma on Hugging Face, you\u2019re required to review\
	\ and agree to Google\u2019s usage license. To do this, please ensure you\u2019\
	re logged in to Hugging Face and click below. Requests are processed immediately."
	inference: false
	library_name: gguf
	license: gemma
	pipeline_tag: text-generation
	quantized_by: legraphista
	tags:
	- quantized
	- GGUF
	- quantization
	- imat
	- imatrix
	- static
	- 16bit
	- 8bit
	- 6bit
	- 5bit
	- 4bit
	- 3bit
	- 2bit
	- 1bit
	---

	# shieldgemma-9b-IMat-GGUF
	_Llama.cpp imatrix quantization of google/shieldgemma-9b_

	Original Model: [google/shieldgemma-9b](https://huggingface.co/google/shieldgemma-9b)
	Original dtype: `BF16` (`bfloat16`)
	Quantized by: llama.cpp [b3525](https://github.com/ggerganov/llama.cpp/releases/tag/b3525)
	IMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt)

	- [Files](#files)
	- [IMatrix](#imatrix)
	- [Common Quants](#common-quants)
	- [All Quants](#all-quants)
	- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)
	- [Inference](#inference)
	- [Simple chat template](#simple-chat-template)
	- [Llama.cpp](#llama-cpp)
	- [FAQ](#faq)
	- [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)
	- [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)

	---

	## Files

	### IMatrix
	Status: ✅ Available
	Link: [here](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/imatrix.dat)

	### Common Quants
	\| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \|
	\| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \|
	\| [shieldgemma-9b.Q8_0.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q8_0.gguf) \| Q8_0 \| 9.83GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [shieldgemma-9b.Q6_K.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q6_K.gguf) \| Q6_K \| 7.59GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [shieldgemma-9b.Q4_K.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q4_K.gguf) \| Q4_K \| 5.76GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.Q3_K.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q3_K.gguf) \| Q3_K \| 4.76GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.Q2_K.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q2_K.gguf) \| Q2_K \| 3.81GB \| ✅ Available \| 🟢 IMatrix \| 📦 No


	### All Quants
	\| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \|
	\| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \|
	\| [shieldgemma-9b.BF16.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.BF16.gguf) \| BF16 \| 18.49GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [shieldgemma-9b.FP16.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.FP16.gguf) \| F16 \| 18.49GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [shieldgemma-9b.Q8_0.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q8_0.gguf) \| Q8_0 \| 9.83GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [shieldgemma-9b.Q6_K.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q6_K.gguf) \| Q6_K \| 7.59GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [shieldgemma-9b.Q5_K.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q5_K.gguf) \| Q5_K \| 6.65GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [shieldgemma-9b.Q5_K_S.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q5_K_S.gguf) \| Q5_K_S \| 6.48GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [shieldgemma-9b.Q4_K.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q4_K.gguf) \| Q4_K \| 5.76GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.Q4_K_S.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q4_K_S.gguf) \| Q4_K_S \| 5.48GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ4_NL.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ4_NL.gguf) \| IQ4_NL \| 5.44GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ4_XS.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ4_XS.gguf) \| IQ4_XS \| 5.18GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.Q3_K.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q3_K.gguf) \| Q3_K \| 4.76GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.Q3_K_L.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q3_K_L.gguf) \| Q3_K_L \| 5.13GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.Q3_K_S.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ3_M.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ3_M.gguf) \| IQ3_M \| 4.49GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ3_S.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ3_S.gguf) \| IQ3_S \| 4.34GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ3_XS.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ3_XS.gguf) \| IQ3_XS \| 4.14GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ3_XXS.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ3_XXS.gguf) \| IQ3_XXS \| 3.80GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.Q2_K.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q2_K.gguf) \| Q2_K \| 3.81GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.Q2_K_S.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.Q2_K_S.gguf) \| Q2_K_S \| 3.55GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ2_M.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ2_M.gguf) \| IQ2_M \| 3.43GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ2_S.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ2_S.gguf) \| IQ2_S \| 3.21GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ2_XS.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ2_XS.gguf) \| IQ2_XS \| 3.07GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ2_XXS.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ2_XXS.gguf) \| IQ2_XXS \| 2.82GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ1_M.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ1_M.gguf) \| IQ1_M \| 2.55GB \| ✅ Available \| 🟢 IMatrix \| 📦 No
	\| [shieldgemma-9b.IQ1_S.gguf](https://huggingface.co/legraphista/shieldgemma-9b-IMat-GGUF/blob/main/shieldgemma-9b.IQ1_S.gguf) \| IQ1_S \| 2.38GB \| ✅ Available \| 🟢 IMatrix \| 📦 No


	## Downloading using huggingface-cli
	If you do not have hugginface-cli installed:
	```
	pip install -U "huggingface_hub[cli]"
	```
	Download the specific file you want:
	```
	huggingface-cli download legraphista/shieldgemma-9b-IMat-GGUF --include "shieldgemma-9b.Q8_0.gguf" --local-dir ./
	```
	If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:
	```
	huggingface-cli download legraphista/shieldgemma-9b-IMat-GGUF --include "shieldgemma-9b.Q8_0/*" --local-dir ./
	# see FAQ for merging GGUF's
	```

	---

	## Inference

	### Simple chat template
	```
	<bos>You are a policy expert trying to help determine whether a user prompt is in violation of the defined safety policies.

	<start_of_turn>
	Human Question: {next_user_prompt}
	<end_of_turn>

	Our safety principle is defined in the below:

	* {guideline}

	===

	Does the human question violate the above principle? Your answer must start with 'Yes' or 'No'. And then walk through step by step to be sure we answer correctly.


	```

	### Llama.cpp
	```
	llama.cpp/main -m shieldgemma-9b.Q8_0.gguf --color -i -p "prompt here (according to the chat template)"
	```

	---

	## FAQ

	### Why is the IMatrix not applied everywhere?
	According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).

	### How do I merge a split GGUF?
	1. Make sure you have `gguf-split` available
	- To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases
	- Download the appropriate zip for your system from the latest release
	- Unzip the archive and you should be able to find `gguf-split`
	2. Locate your GGUF chunks folder (ex: `shieldgemma-9b.Q8_0`)
	3. Run `gguf-split --merge shieldgemma-9b.Q8_0/shieldgemma-9b.Q8_0-00001-of-XXXXX.gguf shieldgemma-9b.Q8_0.gguf`
	- Make sure to point `gguf-split` to the first chunk of the split.

	---

	Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!