license, library_name, base_model, tags, extra_gated_prompt, pipeline_tag

license	library_name	base_model	tags	extra_gated_prompt	pipeline_tag
mit	mlx	huihui-ai/DeepSeek-R1-0528-Qwen3-8B-abliterated	chat abliterated uncensored mlx	**Usage Warnings** “**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “**Legal and Ethical Responsibilities**“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “**Research and Experimental Use**“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “**Monitoring and Review Recommendations**“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.	text-generation

SnowFlash383935/DeepSeek-R1-0528-Qwen3-8B-abliterated-mlx

This model SnowFlash383935/DeepSeek-R1-0528-Qwen3-8B-abliterated-mlx was converted to MLX format from huihui-ai/DeepSeek-R1-0528-Qwen3-8B-abliterated using mlx-lm version 0.25.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("SnowFlash383935/DeepSeek-R1-0528-Qwen3-8B-abliterated-mlx")

prompt = "hello"

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

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