license: apache-2.0
datasets:

• TeichAI/gemini-3-pro-preview-high-reasoning-250x
  language:
• en
  base_model: DavidAU/Gemma-3-27b-it-Uncensored-HERETIC-Gemini-Deep-Reasoning
  pipeline_tag: image-text-to-text
  library_name: transformers
  tags:
• uncensored
• heretic
• abliterated
• unsloth
• finetune
• mlx
• mlx-my-repo

enet45/Gemma-3-27b-it-Uncensored-HERETIC-Gemini-Deep-Reasoning-mlx-2Bit

The Model enet45/Gemma-3-27b-it-Uncensored-HERETIC-Gemini-Deep-Reasoning-mlx-2Bit was converted to MLX format from DavidAU/Gemma-3-27b-it-Uncensored-HERETIC-Gemini-Deep-Reasoning using mlx-lm version 0.31.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("enet45/Gemma-3-27b-it-Uncensored-HERETIC-Gemini-Deep-Reasoning-mlx-2Bit")

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)