library_name: transformers
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: any-to-any
base_model: huihui-ai/Huihui-gemma-4-26B-A4B-it-qat-q4_0-unquantized-abliterated
tags:

• abliterated
• uncensored
• mlx
• mlx-my-repo

isetnefret/Huihui-gemma-4-26B-A4B-it-qat-q4_0-unquantized-abliterated-mlx-4Bit

The Model isetnefret/Huihui-gemma-4-26B-A4B-it-qat-q4_0-unquantized-abliterated-mlx-4Bit was converted to MLX format from huihui-ai/Huihui-gemma-4-26B-A4B-it-qat-q4_0-unquantized-abliterated using mlx-lm version 0.31.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("isetnefret/Huihui-gemma-4-26B-A4B-it-qat-q4_0-unquantized-abliterated-mlx-4Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)