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bn license: apache-2.0 library_name: mlx inference: false base_model: huihui-ai/Devstral-Small-2505-abliterated extra_gated_description: If you want to learn more about how we process your personal data, please read our Privacy Policy. pipeline_tag: text-generation 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.'

AITRADER/Devstral-Small-2505-abliterated-MLX-8bit

This model AITRADER/Devstral-Small-2505-abliterated-MLX-8bit was converted to MLX format from huihui-ai/Devstral-Small-2505-abliterated using mlx-lm version 0.30.0.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("AITRADER/Devstral-Small-2505-abliterated-MLX-8bit")

prompt = "hello"

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

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

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate model, tokenizer = load("AITRADER/Devstral-Small-2505-abliterated-MLX-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_dict=False, ) response = generate(model, tokenizer, prompt=prompt, verbose=True)