Instructions to use thelamapi/next-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next-ocr with Transformers:

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

pipe = pipeline("image-text-to-text", model="thelamapi/next-ocr")
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("thelamapi/next-ocr")
model = AutoModelForImageTextToText.from_pretrained("thelamapi/next-ocr")
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]:]))

llama-cpp-python

How to use thelamapi/next-ocr with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thelamapi/next-ocr",
	filename="mmproj-next-ocr-F16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use thelamapi/next-ocr with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next-ocr:F16
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-ocr:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next-ocr:F16
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-ocr:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf thelamapi/next-ocr:F16
# Run inference directly in the terminal:
./llama-cli -hf thelamapi/next-ocr:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf thelamapi/next-ocr:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thelamapi/next-ocr:F16

Use Docker

docker model run hf.co/thelamapi/next-ocr:F16

LM Studio
Jan

vLLM

How to use thelamapi/next-ocr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next-ocr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thelamapi/next-ocr",
		"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/thelamapi/next-ocr:F16

SGLang

How to use thelamapi/next-ocr 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 "thelamapi/next-ocr" \
    --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": "thelamapi/next-ocr",
		"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 "thelamapi/next-ocr" \
        --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": "thelamapi/next-ocr",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use thelamapi/next-ocr with Ollama:
```
ollama run hf.co/thelamapi/next-ocr:F16
```

Unsloth Studio new

How to use thelamapi/next-ocr with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for thelamapi/next-ocr to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for thelamapi/next-ocr to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thelamapi/next-ocr to start chatting

Pi new

How to use thelamapi/next-ocr with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf thelamapi/next-ocr:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "next-ocr"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use thelamapi/next-ocr with Docker Model Runner:
```
docker model run hf.co/thelamapi/next-ocr:F16
```

Lemonade

How to use thelamapi/next-ocr with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thelamapi/next-ocr:F16

Run and chat with the model

lemonade run user.next-ocr-F16

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🖼️ Next OCR 8B

Compact OCR AI — Accurate, Fast, Multilingual, Math-Optimized

📖 Overview

Next OCR 8B is an 8-billion parameter model optimized for optical character recognition (OCR) tasks with mathematical and tabular content understanding.

Supports multilingual OCR (Turkish, English, German, Spanish, French, Chinese, Japanese, Korean, Russian...) with high accuracy, including structured documents like tables, forms, and formulas.

⚡ Highlights

🖼️ Accurate text extraction, including math and tables
🌍 Multilingual support (30+ languages)
⚡ Lightweight and efficient
💬 Instruction-tuned for document understanding and analysis

📊 Benchmark & Comparison

Model	OCR-Bench Accuracy (%)	Multilingual Accuracy (%)	Layout / Table Understanding (%)
Next OCR	99.0	96.8	95.3
PaddleOCR	95.2	93.9	95.3
Deepseek OCR	90.6	87.4	86.1
Tesseract	92.0	88.4	72.0
EasyOCR	90.4	84.7	78.9
Google Cloud Vision / DocAI	98.7	95.5	93.6
Amazon Textract	94.7	86.2	86.1
Azure Document Intelligence	95.1	93.6	91.4

Model	Handwriting (%)	Scene Text (%)	Complex Tables (%)
Next OCR	92	96	91
PaddleOCR	88	92	90
Deepseek OCR	80	85	83
Tesseract	75	88	70
EasyOCR	78	86	75
Google Cloud Vision / DocAI	90	95	92
Amazon Textract	85	90	88
Azure Document Intelligence	87	91	89

🚀 Installation & Usage

from transformers import AutoTokenizer, AutoModelForVision2Seq
import torch

model_id = "Lamapi/next-ocr"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16)

img = Image.open("image.jpg")

# ATTENTION: The content list must include both an image and text.
messages = [
    {"role": "system", "content": "You are Next-OCR, an helpful AI assistant trained by Lamapi."},
    {
        "role": "user",
        "content": [
            {"type": "image", "image": img},
            {"type": "text", "text": "Read the text in this image and summarize it."}
        ]
    }
]

# Apply the chat template correctly
prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=prompt, images=[img], return_tensors="pt").to(model.device)

with torch.no_grad():
    generated = model.generate(**inputs, max_new_tokens=256)

print(processor.decode(generated[0], skip_special_tokens=True))

🧩 Key Features

Feature	Description
🖼️ High-Accuracy OCR	Extracts text from images, documents, and screenshots reliably.
🇹🇷 Multilingual Support	Works with 30+ languages including Turkish.
⚡ Lightweight & Efficient	Optimized for resource-constrained environments.
📄 Layout & Math Awareness	Handles tables, forms, and mathematical formulas.
🏢 Reliable Outputs	Suitable for enterprise document workflows.

📐 Model Specifications

Specification	Details
Base Model	Qwen 3
Parameters	8 Billion
Architecture	Vision + Transformer (OCR LLM)
Modalities	Image-to-text
Fine-Tuning	OCR datasets with multilingual and math/tabular content
Optimizations	Quantization-ready, FP16 support
Primary Focus	Text extraction, document understanding, mathematical OCR

🎯 Ideal Use Cases

Document digitization
Invoice & receipt processing
Multilingual OCR pipelines
Tables, forms, and formulas extraction
Enterprise document management

📄 License

MIT License — free for commercial & non-commercial use.

📞 Contact & Support

📧 Email: lamapicontact@gmail.com
🤗 HuggingFace: Lamapi

Next OCR — Compact OCR + math-capable AI, blending accuracy, speed, and multilingual document intelligence.

Downloads last month: 4,247

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for thelamapi/next-ocr

Quantizations

20 models

thelamapi
/

next-ocr