Instructions to use GCSA-AiLab/GLM-5.3-Flash-Uncensored-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use GCSA-AiLab/GLM-5.3-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("GCSA-AiLab/GLM-5.3-Flash-Uncensored-MLX") config = load_config("GCSA-AiLab/GLM-5.3-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
GLM-5.3-Flash-Uncensored-MLX
Unofficial community derivative of zai-org/GLM-5.3-Flash, published by GCSA-AiLab.
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Complete model with LoRA v2 already merged. Do not apply the original adapter again.
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Introduction and usage / ไป็ปไธไฝฟ็จ
English | ็ฎไฝไธญๆ | ็น้ซไธญๆ | ๆฅๆฌ่ช | ํ๊ตญ์ด | Franรงais | Portuguรชs
Downloads / ไธ่ฝฝ
Technical details ยท English | ๆๆฏ็ป่ ยท ็ฎไฝไธญๆ
Experimental changes to refusal behavior may alter safeguards. Validate outputs and safety measures before use; follow applicable laws and the MIT upstream terms.
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Quantized
Model tree for GCSA-AiLab/GLM-5.3-Flash-Uncensored-MLX
Base model
zai-org/GLM-5.3-Flash