Instructions to use mlx-community/llava-v1.6-34b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/llava-v1.6-34b-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-v1.6-34b-4bit") config = load_config("mlx-community/llava-v1.6-34b-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
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Download README.md from mlx-community/llava-v1.6-34b-4bit: direct link, hf CLI and curl.
- Browser
- Download file 524 Bytes
-
https://huggingface.co/mlx-community/llava-v1.6-34b-4bit/resolve/main/README.md
- Command line
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hf download hf://mlx-community/llava-v1.6-34b-4bit/README.md
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curl -L -o README.md https://huggingface.co/mlx-community/llava-v1.6-34b-4bit/resolve/main/README.md
524 Bytes
metadata
language:
- en
pipeline_tag: image-text-to-text
tags:
- vision
- image-text-to-text
- mlx
mlx-community/llava-v1.6-34b-4bit
This model was converted to MLX format from llava-hf/llava-v1.6-34b-hf using mlx-vlm version 0.1.0.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model mlx-community/llava-v1.6-34b-4bit --max-tokens 100 --temp 0.0