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-4Bit
The Model enet45/Gemma-3-27b-it-Uncensored-HERETIC-Gemini-Deep-Reasoning-mlx-4Bit 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-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)