metadata
license: mit
library_name: mlx
base_model: huihui-ai/DeepSeek-R1-0528-Qwen3-8B-abliterated
tags:
- chat
- abliterated
- uncensored
- mlx
extra_gated_prompt: >-
**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.
pipeline_tag: text-generation
SnowFlash383935/DeepSeek-R1-0528-Qwen3-8B-abliterated-mlx-4bit
This model SnowFlash383935/DeepSeek-R1-0528-Qwen3-8B-abliterated-mlx-4bit 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-4bit")
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)