Image-Text-to-Text
MLX
Safetensors
qwen3_5
pdf
extraction
structured-data
json
mlx-vlm
vlm
quantized
conversational
6-bit
Instructions to use mlx-community/lift-oQ6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/lift-oQ6 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("mlx-community/lift-oQ6") config = load_config("mlx-community/lift-oQ6") # 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
Gabriel Francisco dos Santos Silva
Mirror of gabfssilva/lift-MLX-oQ6 (oMLX MLX-VLM quant of datalab-to/lift)
8fa369a verified Download tokenizer.json from mlx-community/lift-oQ6: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/mlx-community/lift-oQ6/resolve/main/tokenizer.json
- Command line
-
hf download hf://mlx-community/lift-oQ6/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mlx-community/lift-oQ6/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 458bcbf483ed805b4297af928f717e64bd00c633a07be5fae5717cacbd48e2ef
- Size of remote file:
- 20 MB
- SHA256:
- 87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.