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language: en
license: cdla-permissive-2.0
pipeline_tag: image-text-to-text
library_name: mlx
tags:
- mlx
- mlx-vlm
- idefics3
base_model: docling-project/CodeFormulaV2
base_model_relation: quantized
datasets:
- ds4sd/SynthFormulaNet
- ds4sd/SynthCodeNet
---
# CodeFormulaV2-mlx-bf16
MLX bf16 conversion of [docling-project/CodeFormulaV2](https://huggingface.co/docling-project/CodeFormulaV2), produced with [`mlx_vlm.convert`](https://github.com/Blaizzy/mlx-vlm). Architecture, training data, and intended use are described on the upstream model page; this repo only changes the storage format (PyTorch safetensors → MLX safetensors, same bf16 precision).
## Usage
```bash
pip install mlx-vlm
```
```python
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
model, processor = load("mlx-community/CodeFormulaV2-mlx-bf16")
prompt = apply_chat_template(processor, model.config, "<formula>", num_images=1)
result = generate(
model, processor,
prompt=prompt,
image="path/to/image.png",
temperature=0.0,
)
print(result.text)
```
Use `"<formula>"` as the prompt for a math-expression image, `"<code>"` for a code-block image, per the upstream model card.
## License and attribution
This is a derivative of [docling-project/CodeFormulaV2](https://huggingface.co/docling-project/CodeFormulaV2), redistributed under the same [Community Data License Agreement – Permissive 2.0 (CDLA-Permissive-2.0)](https://cdla.dev/permissive-2-0/).
Please cite the upstream work:
```bibtex
@techreport{Docling,
author = {Deep Search Team},
month = {8},
title = {{Docling Technical Report}},
url = {https://arxiv.org/abs/2408.09869},
eprint = {2408.09869},
year = {2024}
}
```
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