---
base_model: google/gemma-2-2b-jpn-it
language:
- multilingual
datasets:
  - mlabonne/orpo-dpo-mix-40k
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 17 of the original model was chosen for abliteration.
I also created another layer 18 abliterated model for comparison.

ORPO fine tuning was performed for four epoches.

| Epoch | loss | eval_loss |
| ----- | ---- | --------- |
| 1 | 10.51274610161781342 | 11.023366928100586 |
| 2 | 10.09700682163238566 | 10.434176445007324 |
| 3 | 10.35771694183349566 | 10.179500579833984 |
| 4 | 10.82988178133964582 | 10.084120750427246 |

The fine tuned model is uploaded here to be evaluated by the Open LLM Leaderboard to see if the brain damaged 
suffered by the non-ORPO model can be healed.

## 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-ORPO | TBD | TBD | TBD | TBD | TBD | TBD | TBD |
| [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_2024-10-17T11-26-10.721815.json) | 16.74 | 0.0 | 29.13 | 0.0 | 25.92 | 33.73 | 11.68 |
| [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_2024-10-16T07-58-03.781979.json) | 16.74 | 0.0 | 29.13 | 0.0 | 25.92 | 33.73 | 11.68 |

Indeed, it is quite dumbed down relative to the original. Interestingly, both abliteration models have the same Open LLM results. 

## How to run this model

```py
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model_id = "gemma-2-2b-jpn-it-abliterated-17-ORPO"
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-ORPO --include "*" --local-dir ./
```

## Credits

Thank you mlabonne for describing his abliteration method.