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 using the method 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 | 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 | 16.74 | 0.0 | 29.13 | 0.0 | 25.92 | 33.73 | 11.68 |
| gemma-2-2b-jpn-it-abliterated-18 | 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
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.