license, datasets, base_model, language, tags, pipeline_tag, library_name

license	datasets	base_model	language	tags	pipeline_tag	library_name
apache-2.0	TeichAI/claude-4.5-opus-high-reasoning-250x	DavidAU/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning	en fr de es it pt zh ja ru ko	thinking reasoning instruct heretic uncensored abliterated Claude4.5-Opus creative creative writing fiction writing plot generation sub-plot generation story generation scene continue storytelling fiction story science fiction romance all genres story writing vivid prosing vivid writing fiction roleplaying bfloat16 role play 128k context llama3.3 llama-3 llama-3.3 unsloth finetune mlx mlx-my-repo	text-generation	transformers

alexgusevski/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning-mlx-fp16

The Model alexgusevski/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning-mlx-fp16 was converted to MLX format from DavidAU/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning using mlx-lm version 0.29.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("alexgusevski/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning-mlx-fp16")

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)