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 6, 2025

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@@ -119,22 +119,22 @@ print(output_text)
 **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.

 **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.