Image-to-Text
Transformers
Safetensors
MLX
qwen2_5_vl
image-text-to-text
OCR
vision-language
VLM
Reasoning
document-to-markdown
qwen2.5
markdown
extraction
RAG
text-generation-inference
4-bit precision
Instructions to use numind/NuMarkdown-8B-Thinking-mlx-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuMarkdown-8B-Thinking-mlx-4bits with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="numind/NuMarkdown-8B-Thinking-mlx-4bits")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("numind/NuMarkdown-8B-Thinking-mlx-4bits") model = AutoModelForImageTextToText.from_pretrained("numind/NuMarkdown-8B-Thinking-mlx-4bits") - MLX
How to use numind/NuMarkdown-8B-Thinking-mlx-4bits with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir NuMarkdown-8B-Thinking-mlx-4bits numind/NuMarkdown-8B-Thinking-mlx-4bits
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
metadata
license: mit
base_model: Qwen/Qwen2.5-VL-7B
new_version: numind/NuExtract3
tags:
- OCR
- vision-language
- VLM
- Reasoning
- document-to-markdown
- qwen2.5
- markdown
- extraction
- RAG
- mlx
model_name: NuMarkdown-8B-Thinking
library_name: transformers
pipeline_tag: image-to-text
numind/NuMarkdown-8B-Thinking-mlx-4bits
This model was converted to MLX format from numind/NuMarkdown-8B-Thinking using mlx-vlm version 0.3.7.
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 numind/NuMarkdown-8B-Thinking-mlx-4bits --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>