You can deploy this model and start earning money today!

RichardErkhov/
ymcki_-_gemma-2-2b-jpn-it-abliterated-24-gguf

Name: ymcki_-_gemma-2-2b-jpn-it-abliterated-24-gguf
Availability: InStock

Views

No views yet

Quantization made by Richard Erkhov.

Github

Discord

Request more models

gemma-2-2b-jpn-it-abliterated-24 - GGUF

Model creator: https://huggingface.co/ymcki/
Original model: https://huggingface.co/ymcki/gemma-2-2b-jpn-it-abliterated-24/

Name	Quant method	Size
gemma-2-2b-jpn-it-abliterated-24.Q2_K.gguf	Q2_K	1.15GB
gemma-2-2b-jpn-it-abliterated-24.IQ3_XS.gguf	IQ3_XS	1.22GB
gemma-2-2b-jpn-it-abliterated-24.IQ3_S.gguf	IQ3_S	1.27GB
gemma-2-2b-jpn-it-abliterated-24.Q3_K_S.gguf	Q3_K_S	1.27GB
gemma-2-2b-jpn-it-abliterated-24.IQ3_M.gguf	IQ3_M	1.3GB
gemma-2-2b-jpn-it-abliterated-24.Q3_K.gguf	Q3_K	1.36GB
gemma-2-2b-jpn-it-abliterated-24.Q3_K_M.gguf	Q3_K_M	1.36GB
gemma-2-2b-jpn-it-abliterated-24.Q3_K_L.gguf	Q3_K_L	1.44GB
gemma-2-2b-jpn-it-abliterated-24.IQ4_XS.gguf	IQ4_XS	1.47GB
gemma-2-2b-jpn-it-abliterated-24.Q4_0.gguf	Q4_0	1.52GB
gemma-2-2b-jpn-it-abliterated-24.IQ4_NL.gguf	IQ4_NL	1.53GB
gemma-2-2b-jpn-it-abliterated-24.Q4_K_S.gguf	Q4_K_S	1.53GB
gemma-2-2b-jpn-it-abliterated-24.Q4_K.gguf	Q4_K	1.59GB
gemma-2-2b-jpn-it-abliterated-24.Q4_K_M.gguf	Q4_K_M	1.59GB
gemma-2-2b-jpn-it-abliterated-24.Q4_1.gguf	Q4_1	1.64GB
gemma-2-2b-jpn-it-abliterated-24.Q5_0.gguf	Q5_0	1.75GB
gemma-2-2b-jpn-it-abliterated-24.Q5_K_S.gguf	Q5_K_S	1.75GB
gemma-2-2b-jpn-it-abliterated-24.Q5_K.gguf	Q5_K	1.79GB
gemma-2-2b-jpn-it-abliterated-24.Q5_K_M.gguf	Q5_K_M	1.79GB
gemma-2-2b-jpn-it-abliterated-24.Q5_1.gguf	Q5_1	1.87GB
gemma-2-2b-jpn-it-abliterated-24.Q6_K.gguf	Q6_K	2.0GB
gemma-2-2b-jpn-it-abliterated-24.Q8_0.gguf	Q8_0	2.59GB

Original model description:

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.

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	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
gemma-2-2b-jpn-it-abliterated-24	30.61	51.37	40.77	27.77	39.02	24.73

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-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.

RichardErkhov/ymcki_-_gemma-2-2b-jpn-it-abliterated-24-gguf