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)