Image-Text-to-Text
MLX
Safetensors
English
Chinese
abliterated
deepseek
deepseek-v4.1-flash
uncensored
ai-red-team
red-teaming
apple-silicon
quantized
2-bit
3-bit
4-bit precision
Mixture of Experts
engram
vision-language
multimodal
function-calling
reasoning
Instructions to use orcarouter/DeepSeek-V4.1-Flash-Uncensored-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use orcarouter/DeepSeek-V4.1-Flash-Uncensored-MLX with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("orcarouter/DeepSeek-V4.1-Flash-Uncensored-MLX") config = load_config("orcarouter/DeepSeek-V4.1-Flash-Uncensored-MLX") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Gated model You can list files but not access them
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