Image-Text-to-Text
MLX
Safetensors
English
gemma3
axolotl
gemma
roleplay
mlx-my-repo
conversational
6-bit
Instructions to use dong-99/CardProjector-27B-v4-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dong-99/CardProjector-27B-v4-mlx-6Bit 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("dong-99/CardProjector-27B-v4-mlx-6Bit") config = load_config("dong-99/CardProjector-27B-v4-mlx-6Bit") # 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
Download tokenizer.json from dong-99/CardProjector-27B-v4-mlx-6Bit: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/dong-99/CardProjector-27B-v4-mlx-6Bit/resolve/main/tokenizer.json
- Command line
-
hf download hf://dong-99/CardProjector-27B-v4-mlx-6Bit/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/dong-99/CardProjector-27B-v4-mlx-6Bit/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- c4c7aa84fb94a696d78f2bde62590a25b6741b7365ed842f9310b5dcab71d95d
- Size of remote file:
- 33.4 MB
- SHA256:
- 4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
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