multilingual datasets:
- mlabonne/harmless_alpaca
- mlabonne/harmful_behaviors library_name: transformers license: gemma license_link: https://ai.google.dev/gemma/terms pipeline_tag: text-generation tags:
nlp
code quantized_by: ymcki widget:
messages:
- role: user content: Can you provide ways to eat combinations of bananas and dragonfruits?

Original model: https://huggingface.co/google/gemma-2-2b-jpn-it

Prompt format

<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
<end_of_turn>
<start_of_turn>model

Note that this model does not support a System prompt.

This is abliterated model of google/gemma-2-2b-jpn-it using the method described by mlabonne.

Layer 18 of the original model was chosen for abliteration. I also created another layer 17 abliterated model for comparison. These two layers were chosen due to they both produce uncensored response after respective layer was abliterated.

It is uploaded here to be evaluated by the Open LLM Leaderboard to see how brain damaged it is compared to the original model.

ORPO fine tuning is currently underway to see if it can regain its sanity. You can play with this model first or wait until I am done with the fine tuning.

Benchmark (100.0*raw scores only)

Click on the model name go to the raw score json generated by Open LLM Leaderboard.

Model	Average	IFEval	BHH	GPQA	MUSR	MMLU-PRO
gemma-2-2b-jpn-it	30.82	54.11	41.43	27.52	37.17	24.67
gemma-2-2b-jpn-it-abliterated-17	30.29	52.65	40.46	27.18	36.90	24.55
gemma-2-2b-jpn-it-abliterated-18	30.61	53.02	40.96	27.35	37.30	25.05

It is only slightly dumber than the original.

How to run this model

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model_id = "gemma-2-2b-jpn-it-abliterated-18"
dtype = torch.bfloat16

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cuda",
    torch_dtype=dtype,)

chat = [
    { "role": "user", "content": "Write a hello world program" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download ymcki/gemma-2-2b-jpn-it-abliterated-18 --include "*" --local-dir ./

Credits

Thank you mlabonne for describing his abliteration method.

RichardErkhov/ymcki_-_gemma-2-2b-jpn-it-abliterated-18-awq

Original model description:

Prompt format

Benchmark (100.0*raw scores only)

How to run this model

Downloading using huggingface-cli

Credits

RichardErkhov/
ymcki_-_gemma-2-2b-jpn-it-abliterated-18-awq