base_model: google/gemma-2-2b-jpn-it
language:
- 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.
This time multiple Layers are abliterated to study its performance. Layer 17, 18 and 24 of the original model were chosen for abliteration.
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 | Math Lv5 | GPQA | MUSR | MMLU-PRO |
|---|---|---|---|---|---|---|---|
| gemma-2-2b-jpn-it | 30.82 | 54.11 | 41.43 | 0.0 | 27.52 | 37.17 | 24.67 |
| gemma-2-2b-jpn-it-abliterated-17 | 30.29 | 52.65 | 40.46 | 0.0 | 27.18 | 36.90 | 24.55 |
| gemma-2-2b-jpn-it-abliterated-18 | 30.61 | 53.02 | 40.96 | 0.0 | 27.35 | 37.30 | 25.05 |
| gemma-2-2b-jpn-it-abliterated-24 | 30.61 | 51.37 | 40.77 | 0.0 | 27.77 | 39.02 | 24.73 |
| gemma-2-2b-jpn-it-abliterated-17-18-24 | 29.17 | 51.33 | 37.82 | 0.0 | 28.10 | 34.92 | 22.82 |
It is only slightly dumber than the original for models that have only one layer abliterated. For the model with three layers abiliterated, the brain damage is more significant.
How to run this model
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "gemma-2-2b-jpn-it-abliterated-17-18-24"
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-17-18-24 --include "*" --local-dir ./
Credits
Thank you mlabonne for describing his abliteration method.