cstr/uni-mumer-qwen3-vl-2b-GGUF

Uni-MuMER-Qwen3-VL-2B GGUF

GGUF conversions of phxember/Uni-MuMER-Qwen3-VL-2B for CrispEmbed inference.

Handwritten Mathematical Expression Recognition (HMER) model. Converts images of handwritten math into LaTeX. Fine-tuned from Qwen3-VL-2B-Instruct using multi-task training (recognition + symbol counting + position identification).

Based on the Uni-MuMER paper (NeurIPS 2025 Spotlight).

Model variants

File	Quant	Size	Notes
uni-mumer-qwen3-vl-2b-f16.gguf	F16	4.7 GB	Full precision
uni-mumer-qwen3-vl-2b-q8_0.gguf	Q8_0	2.2 GB	Recommended
uni-mumer-qwen3-vl-2b-q4_k.gguf	Q4_K	1.5 GB	Max compression

Architecture

• Vision: Qwen3-VL ViT (24L, 1024d, 16 heads, patch=16, learned pos embed)
• DeepStack: Multi-layer feature concat at layers [5, 11, 17]
• Merger: LayerNorm + GELU MLP (4096d intermediate)
• LLM: Qwen3 decoder (28L, 2048d, GQA 16/8, SwiGLU, interleaved mRoPE)
• Parameters: 2.1B
• mRoPE sections: [24, 20, 20]

Usage

# CLI — auto-detects math OCR prompt from model name
./crispembed -m uni-mumer-qwen3-vl-2b-q4_k.gguf --ocr equation.png

# Server
./crispembed-server --ocr uni-mumer-qwen3-vl-2b-q4_k.gguf --port 8080
curl -X POST http://localhost:8080/math/ocr -F "image=@equation.png"

from crispembed import CrispMathOcr

ocr = CrispMathOcr("uni-mumer-qwen3-vl-2b-q4_k.gguf")
latex = ocr.recognize("equation.png")
print(latex)  # x ^ { 2 } + 2 x y + y ^ { 2 } = 0

Accuracy

• ~82% ExpRate on CROHME (handwritten math benchmark)
• Replaces CC BY-NC-SA models (PosFormer/BTTR/HMER at 57%) with Apache-2.0 licensed alternative

License

Apache-2.0 — same as the base model.

Credits

Original model by BFlameSwift/Uni-MuMER (phxember on HuggingFace). GGUF conversion and inference engine by CrispEmbed.

Provenance and EU AI Act Art. 53 note

• Upstream model: phxember/Uni-MuMER-Qwen3-VL-2B — published by phxember.
• Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.