Image-Text-to-Text
MLX
Safetensors
English
idefics3
text-generation
screen-parsing
ui-understanding
object-detection
grounding
web
screentag
docling
granite
quantized
apple-silicon
conversational
4-bit precision
Instructions to use olragon/ScreenVLM-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use olragon/ScreenVLM-MLX-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("olragon/ScreenVLM-MLX-4bit") config = load_config("olragon/ScreenVLM-MLX-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
| {%- for message in messages -%} | |
| {{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' -}} | |
| {%- if message['content'] is string -%} | |
| {{- message['content'] -}} | |
| {%- else -%} | |
| {%- for part in message['content'] -%} | |
| {%- if part['type'] == 'text' -%} | |
| {{- part['text'] -}} | |
| {%- elif part['type'] == 'image' -%} | |
| {{- '<image>' -}} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- endif -%} | |
| {{- '<|end_of_text|> | |
| ' -}} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| {{- '<|start_of_role|>assistant' -}} | |
| {%- if controls -%}{{- ' ' + controls | tojson() -}}{%- endif -%} | |
| {{- '<|end_of_role|>' -}} | |
| {%- endif -%} | |