Instructions to use ops-malware/gemma-2-2b-it-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ops-malware/gemma-2-2b-it-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ops-malware/gemma-2-2b-it-abliterated-GGUF",
	filename="gemma-2-2b-it-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ops-malware/gemma-2-2b-it-abliterated-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 ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ops-malware/gemma-2-2b-it-abliterated-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 ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ops-malware/gemma-2-2b-it-abliterated-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 ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ops-malware/gemma-2-2b-it-abliterated-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 ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ops-malware/gemma-2-2b-it-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ops-malware/gemma-2-2b-it-abliterated-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ops-malware/gemma-2-2b-it-abliterated-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M

Ollama
How to use ops-malware/gemma-2-2b-it-abliterated-GGUF with Ollama:
```
ollama run hf.co/ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use ops-malware/gemma-2-2b-it-abliterated-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 ops-malware/gemma-2-2b-it-abliterated-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 ops-malware/gemma-2-2b-it-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ops-malware/gemma-2-2b-it-abliterated-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use ops-malware/gemma-2-2b-it-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use ops-malware/gemma-2-2b-it-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-2-2b-it-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

gemma-2-2b-it-abliterated-GGUF

GGUF builds of ops-malware/gemma-2-2b-it-abliterated, for llama.cpp, Ollama, LM Studio and Jan.

The parent card carries what this model is, how it was made, what abliteration did to it, and the evaluation numbers. Read it before using these weights: this model does not refuse, which is the entire point of it and the thing to understand before downloading.

Files

File	Precision	Size	Use when
`gemma-2-2b-it-f16.gguf`	F16	larger	You want the conversion with no quantisation loss, or you are making your own quants
`gemma-2-2b-it-Q4_K_M.gguf`	Q4_K_M	~4x smaller	Almost always. The usual quality and size compromise

Both were converted from the parent's safetensors with convert_hf_to_gguf.py and quantised with llama-quantize. Each file was loaded and asked to generate before publication, because a GGUF that converts but does not run is exactly the kind of thing that ships broken.

Usage

llama.cpp

llama-server -m gemma-2-2b-it-Q4_K_M.gguf -c 4096

Ollama

ollama run hf.co/ops-malware/gemma-2-2b-it-abliterated-GGUF:Q4_K_M

Python, via huggingface_hub

from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="ops-malware/gemma-2-2b-it-abliterated-GGUF",
    filename="gemma-2-2b-it-Q4_K_M.gguf",
)

Limitations

Everything on the parent card applies here unchanged, plus the usual quantisation caveat: Q4_K_M trades some quality for size, and small models have less quality to spare than large ones. If a result matters, check it against the F16.

Downloads last month: 27

GGUF

Model size

3B params

Architecture

gemma2

Hardware compatibility

4-bit

16-bit

Model tree for ops-malware/gemma-2-2b-it-abliterated-GGUF

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Finetuned

ops-malware/gemma-2-2b-it-abliterated

Quantized

(1)

this model