transformers gguf qwen latex vLM Vision Latex llama-cpp gguf-my-repo image-text-to-text en base_model:prithivMLmods/LatexMind-2B-Codec base_model:quantized:prithivMLmods/LatexMind-2B-Codec license:apache-2.0 endpoints_compatible region:us conversational
---
base_model: prithivMLmods/LatexMind-2B-Codec
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- qwen
- latex
- vLM
- Vision
- Latex
- llama-cpp
- gguf-my-repo
---

# Triangle104/LatexMind-2B-Codec-Q5_K_S-GGUF
This model was converted to GGUF format from [`prithivMLmods/LatexMind-2B-Codec`](https://huggingface.co/prithivMLmods/LatexMind-2B-Codec) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/prithivMLmods/LatexMind-2B-Codec) for more details on the model.

---
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.  

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/LatexMind-2B-Codec-Q5_K_S-GGUF --hf-file latexmind-2b-codec-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/LatexMind-2B-Codec-Q5_K_S-GGUF --hf-file latexmind-2b-codec-q5_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q5_K_S-GGUF --hf-file latexmind-2b-codec-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/LatexMind-2B-Codec-Q5_K_S-GGUF --hf-file latexmind-2b-codec-q5_k_s.gguf -c 2048
```
