---

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

base_model_relation: quantized

library_name: gguf

pipeline_tag: text-generation

language:

- en

license: gemma

tags:

- gguf

- llama.cpp

- abliterated

- uncensored

- senbonzakura

---

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.