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---
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](https://huggingface.co./google/gemma-2-2b-jpn-it) using the
[method](https://medium.com/@mlabonne/uncensor-any-llm-with-abliteration-d30148b7d43e)
described by mlabonne.
Layer 24 of the original model was chosen for abliteration.
I also created models with layer 17 and 18 abliterated respectively for comparison.
These three layers were chosen due to they all 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 | Math Lv5 | GPQA | MUSR | MMLU-PRO |
| ----- | ------- | ------ | ----|--------- | ---- | ---- | -------- |
| [gemma-2-2b-jpn-it](https://huggingface.co./datasets/open-llm-leaderboard/results/blob/main/google/gemma-2-2b-jpn-it/results_2024-10-15T15-21-39.173019.json) | 30.82 | 54.11 | 41.43 | 0.0 | 27.52 | 37.17 | 24.67 |
| [gemma-2-2b-jpn-it-abliterated-17](https://huggingface.co./datasets/open-llm-leaderboard/results/raw/main/ymcki/gemma-2-2b-jpn-it-abliterated-17/results_2024-10-18T15-18-46.821674.json) | 30.29 | 52.65 | 40.46 | 0.0 | 27.18 | 36.90 | 24.55 |
| [gemma-2-2b-jpn-it-abliterated-18](https://huggingface.co./datasets/open-llm-leaderboard/results/raw/main/ymcki/gemma-2-2b-jpn-it-abliterated-18/results_2024-10-18T15-41-42.399571.json) | 30.61 | 53.02 | 40.96 | 0.0 | 27.35 | 37.30 | 25.05 |
| [gemma-2-2b-jpn-it-abliterated-24](https://huggingface.co./datasets/open-llm-leaderboard/results/raw/main/ymcki/gemma-2-2b-jpn-it-abliterated-24/results_2024-10-25T16-29-46.542899.json) | 30.61 | 51.37 | 40.77 | 0.0 | 27.77 | 39.02 | 24.73 |
It is only slightly dumber than the original.
## How to run this model
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "gemma-2-2b-jpn-it-abliterated-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-24 --include "*" --local-dir ./
```
## Credits
Thank you mlabonne for describing his abliteration method.
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