Triangle104/LatexMind-2B-Codec-Q8_0-GGUF

This model was converted to GGUF format from prithivMLmods/LatexMind-2B-Codec using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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The LatexMind-2B-Codec model is a fine-tuned version of Qwen2-VL-2B-Instruct, optimized for Optical Character Recognition (OCR), image-to-text conversion, and mathematical expression extraction with LaTeX formatting. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively.

    Key Enhancements:

SoTA understanding of images with various resolutions & aspect ratios: LatexMind-2B-Codec achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.

Advanced LaTeX extraction: The model specializes in extracting structured mathematical expressions from images and documents, converting them into LaTeX format for precise rendering and further computation.

Understanding long-duration videos (20min+): LatexMind-2B-Codec can process videos over 20 minutes long, enabling high-quality video-based question answering, mathematical solution explanation, and educational content creation.

Agent capabilities for automated operations: With complex reasoning and decision-making abilities, the model can be integrated with mobile devices, robots, and assistive technologies to automate tasks based on visual and textual inputs.

Multilingual Support: To serve global users, in addition to English and Chinese, the model supports text recognition inside images across multiple languages, including European languages, Japanese, Korean, Arabic, Vietnamese, etc.

This model is particularly effective in retrieving mathematical notations and equations from scanned documents, whiteboard images, and handwritten notes, ensuring accurate conversion to LaTeX code for further academic and computational applications.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/LatexMind-2B-Codec-Q8_0-GGUF --hf-file latexmind-2b-codec-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/LatexMind-2B-Codec-Q8_0-GGUF --hf-file latexmind-2b-codec-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/LatexMind-2B-Codec-Q8_0-GGUF --hf-file latexmind-2b-codec-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/LatexMind-2B-Codec-Q8_0-GGUF --hf-file latexmind-2b-codec-q8_0.gguf -c 2048