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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("thelamapi/next-ocr")
model = AutoModelForMultimodalLM.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 Settings

llama.cpp

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

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

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 serve -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": "thelamapi/next-ocr:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use thelamapi/next-ocr with Hermes Agent:

Start the llama.cpp server

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

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default thelamapi/next-ocr:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use thelamapi/next-ocr with OpenClaw:

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "thelamapi/next-ocr:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

next-ocr

File size: 7,743 Bytes

---
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3_vl
- trl
- sft
- chemistry
- code
- climate
- art
- biology
- finance
- legal
- music
- medical
- agent
license: apache-2.0
language:
- en
- ab
- aa
- ae
- af
- ak
- am
- an
- ar
- as
- av
- ay
- az
- ba
- be
- bg
- bh
- bi
- bm
- bn
- bo
- br
- bs
- ca
- ce
- ch
- co
- cr
- cs
- cu
- cv
- cy
- da
- de
- dv
- dz
- ee
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- eo
- es
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- eu
- fa
- ff
- fi
- fj
- fo
- fr
- fy
- ga
- gd
- gl
- gn
- gv
- ha
- he
- hi
- ho
- gu
- hr
- ht
- hu
- hz
- hy
- id
- ia
- ig
- ie
- ik
- ii
- is
- io
- iu
- it
- jv
- ja
- kg
- ka
- kj
- ki
- kl
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- kn
- km
- kr
- ko
- ku
- ks
- kw
- kv
- la
- ky
- lg
- lb
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- li
- lt
- lo
- lv
- lu
- mg
- mi
- mh
- ml
- mk
- mr
- mn
- mt
- ms
- na
- my
- nd
- nb
- ng
- nl
- ne
- 'no'
- nn
- nv
- nr
- oc
- oj
- om
- ny
- os
- or
- pa
- pi
- pl
- ps
- pt
- rm
- rn
- qu
- ro
- ru
- sn
- rw
- so
- sa
- sc
- sd
pipeline_tag: image-text-to-text
library_name: transformers
---
<img src='bannerocr.png'>

# 🖼️ Next OCR 8B

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

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Language: Multilingual](https://img.shields.io/badge/Language-Multilingual-red.svg)]()
[![HuggingFace](https://img.shields.io/badge/🤗-Lamapi/Next--OCR--orange.svg)](https://huggingface.co/Lamapi/next-ocr)
[![Discord](https://cdn.modrinth.com/data/cached_images/e84c69448cbf878a167f996d63e1a253437fcea2.png)](https://discord.gg/XgH4EpyPD2)

---

## 📖 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

![image](https://cdn-uploads.huggingface.co/production/uploads/67d46bc5fe6ad6f6511d6f44/wLtEbJ9U3KCJe4OCxvAF7.png)

---

| 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

```python
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](mailto:lamapicontact@gmail.com)
* 🤗 HuggingFace: [Lamapi](https://huggingface.co/Lamapi)

---

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

[![Follow on HuggingFace](https://img.shields.io/badge/Follow-HuggingFace-yellow?logo=huggingface)](https://huggingface.co/Lamapi)