Instructions to use mlx-community/llava-v1.6-mistral-7b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/llava-v1.6-mistral-7b-4bit 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/llava-v1.6-mistral-7b-4bit") config = load_config("mlx-community/llava-v1.6-mistral-7b-4bit") # 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
| { | |
| "aspect_ratio_setting": "anyres", | |
| "crop_size": { | |
| "height": 336, | |
| "width": 336 | |
| }, | |
| "do_center_crop": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_grid_pinpoints": [ | |
| [ | |
| 336, | |
| 672 | |
| ], | |
| [ | |
| 672, | |
| 336 | |
| ], | |
| [ | |
| 672, | |
| 672 | |
| ], | |
| [ | |
| 1008, | |
| 336 | |
| ], | |
| [ | |
| 336, | |
| 1008 | |
| ] | |
| ], | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "LlavaNextImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "processor_class": "LlavaNextProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 336 | |
| } | |
| } | |