Instructions to use apol/gemma4-12b-it-libre-benign-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use apol/gemma4-12b-it-libre-benign-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("C:\Users\MGManchon\Documents\Codex\2026-06-02\goal-ablietra-gemma4-31b-dense-para\work\gemma4-12b-it-text-only") model = PeftModel.from_pretrained(base_model, "apol/gemma4-12b-it-libre-benign-adapter") - Notebooks
- Google Colab
- Kaggle
Gemma 4 12B IT Libre Benign Adapter
This repository contains a LoRA adapter for google/gemma-4-12B-it.
The adapter was trained as a benign false-refusal reduction / local assistant tuning pass. It is not presented as an "abliterated", uncensored, or safety-control-removal model.
Contents
adapter_model.safetensors: PEFT LoRA adapter weightsadapter_config.json: PEFT adapter configurationtokenizer.json,tokenizer_config.json,chat_template.jinja: tokenizer assets copied for convenienceEVAL_RESULTS.md: small direct evaluation report from local validationFREEDOM_CRITERIA.md: benign local-assistant criteria used during the run
Intended Use
Use this adapter with users who already have access to the base model:
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_id = "google/gemma-4-12B-it"
adapter_id = "apol/gemma4-12b-it-libre-benign-adapter"
tokenizer = AutoTokenizer.from_pretrained(base_id)
model = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)
Notes
This is a derived adapter for local experimentation. Use must comply with the base model license and terms. The adapter does not include the base model weights.
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