Gemma 4 E4B It Heretic Mlx 4Bit

from mlx_lm import load, generate

model, tokenizer = load("McG-221/gemma-4-E4B-it-heretic-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)

--- library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any tags: - heretic - uncensored - decensored - abliterated - ara - mlx - mlx-my-repo base_model: coder3101/gemma-4-E4B-it-heretic --- # McG-221/gemma-4-E4B-it-heretic-mlx-4Bit The Model [McG-221/gemma-4-E4B-it-heretic-mlx-4Bit](https://huggingface.co/McG-221/gemma-4-E4B-it-heretic-mlx-4Bit) was converted to MLX format from [coder3101/gemma-4-E4B-it-heretic](https://huggingface.co/coder3101/gemma-4-E4B-it-heretic) using mlx-lm version **0.31.2**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("McG-221/gemma-4-E4B-it-heretic-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) ```