Instructions to use prithivMLmods/LatexMind-2B-Codec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/LatexMind-2B-Codec with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/LatexMind-2B-Codec")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("prithivMLmods/LatexMind-2B-Codec")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/LatexMind-2B-Codec")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/LatexMind-2B-Codec with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/LatexMind-2B-Codec"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/LatexMind-2B-Codec",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/LatexMind-2B-Codec

SGLang

How to use prithivMLmods/LatexMind-2B-Codec with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/LatexMind-2B-Codec" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/LatexMind-2B-Codec",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/LatexMind-2B-Codec" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/LatexMind-2B-Codec",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/LatexMind-2B-Codec with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/LatexMind-2B-Codec
```

prithivMLmods commited on Feb 1, 2025

Commit

72b7cde

verified ·

1 Parent(s): 639f41e

Update README.md

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README.md +28 -1

README.md CHANGED Viewed

@@ -37,6 +37,8 @@ This model is particularly effective in **retrieving mathematical notations and
 ![latexqwen.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-h5z3giEudPrdM9qRMMTe.png)
 # Use it with Transformers
@@ -102,7 +104,7 @@ output_text = processor.batch_decode(
 )
 print(output_text)
 ```
-### Buf
 ```python
     buffer = ""
     for new_text in streamer:
@@ -111,5 +113,30 @@ print(output_text)
         buffer = buffer.replace("<|im_end|>", "")
         yield buffer
 ```

 ![latexqwen.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-h5z3giEudPrdM9qRMMTe.png)
+Demo: https://huggingface.co/prithivMLmods/LatexMind-2B-Codec/blob/main/latexmind/latexmind-codec.ipynb
 # Use it with Transformers
 )
 print(output_text)
 ```
+# Buf
 ```python
     buffer = ""
     for new_text in streamer:
         buffer = buffer.replace("<|im_end|>", "")
         yield buffer
 ```
+Here’s the **Intended Use & Limitations** section for **LatexMind-2B-Codec**:
+---
+# Intended Use
+**LatexMind-2B-Codec** is designed for tasks that require **image-based text recognition**, **math equation extraction**, and **multi-modal understanding**. It is particularly useful in the following scenarios:
+🔹 **Optical Character Recognition (OCR)** – Extracting printed and handwritten text from images, documents, and scanned pages.
+🔹 **Math Expression Recognition** – Converting mathematical notations into structured **LaTeX format** for further computation and documentation.
+🔹 **Image-to-Text Conversion** – Generating accurate descriptions for text-rich and math-heavy images.
+🔹 **Document and Academic Processing** – Assisting researchers, students, and professionals in digitizing handwritten notes and extracting structured content from books, PDFs, and whiteboards.
+🔹 **Automated Educational Support** – Enabling AI-powered tutors, content summarization, and interactive learning for subjects involving complex equations.
+🔹 **Multi-Language OCR** – Recognizing text inside images across multiple languages, including English, Chinese, Japanese, Korean, Arabic, and various European languages.
+🔹 **Video-Based Question Answering** – Understanding long-duration videos for content summarization, question answering, and structured data extraction.
+# Limitations
+Despite its capabilities, **LatexMind-2B-Codec** has some inherent limitations:
+⚠ **Handwritten Text Accuracy** – While it can recognize handwritten equations, performance may degrade with highly unstructured or messy handwriting.
+⚠ **Complex LaTeX Formatting** – The model may struggle with deeply nested or ambiguous LaTeX expressions, requiring manual corrections for precise formatting.
+⚠ **Low-Resolution Images** – Extracting accurate text from blurry or low-resolution images can lead to misinterpretations or OCR errors.
+⚠ **Contextual Understanding in Multi-Step Equations** – While it recognizes math expressions, solving multi-step problems autonomously may be limited.
+⚠ **Limited Support for Rare Mathematical Notations** – Some specialized or domain-specific symbols may not be recognized with high accuracy.
+⚠ **Processing Speed for Large Documents** – Performance may slow down when handling extremely large documents or dense mathematical content in real-time applications.
+⚠ **Language-Specific OCR Variability** – While it supports multiple languages, OCR accuracy may vary depending on the script complexity and font style.