language: en
license: cdla-permissive-2.0
pipeline_tag: image-text-to-text
library_name: mlx
tags:
- mlx
- mlx-vlm
- idefics3
- quantized
- 8-bit
base_model: docling-project/CodeFormulaV2
base_model_relation: quantized
datasets:
- ds4sd/SynthFormulaNet
- ds4sd/SynthCodeNet
CodeFormulaV2-mlx-q8
8-bit quantized MLX conversion of docling-project/CodeFormulaV2, produced with mlx_vlm.convert --quantize --q-bits 8. The text decoder linear layers are quantized to 8 bits per weight; the vision encoder stays at bf16 (the mlx-vlm convention via skip_multimodal_module).
Architecture, training data, and intended use are described on the upstream model page. This repo only re-encodes the weights at lower precision; no retraining or modification of behaviour was performed.
A bf16 variant is also available: mlx-community/CodeFormulaV2-mlx-bf16.
Usage
pip install mlx-vlm
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
model, processor = load("mlx-community/CodeFormulaV2-mlx-q8")
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, redistributed under the same Community Data License Agreement – Permissive 2.0 (CDLA-Permissive-2.0).
Please cite the upstream work:
@techreport{Docling,
author = {Deep Search Team},
month = {8},
title = {{Docling Technical Report}},
url = {https://arxiv.org/abs/2408.09869},
eprint = {2408.09869},
year = {2024}
}