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": "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-server -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

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

Lamapi commited on Nov 12, 2025

Commit

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@@ -1,5 +1,4 @@
 ---
-base_model: unsloth/Qwen3-VL-8B-Instruct
 tags:
 - text-generation-inference
 - transformers
@@ -7,17 +6,274 @@ tags:
 - qwen3_vl
 - trl
 - sft
 license: apache-2.0
 language:
 - en
 ---
-# Uploaded  model
-- **Developed by:** Lamapi
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen3-VL-8B-Instruct
-This qwen3_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
 tags:
 - text-generation-inference
 - transformers
 - 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
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+- as
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+- az
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+- 'no'
+- nn
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+- or
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+- pi
+- pl
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+- pt
+- rm
+- rn
+- qu
+- ro
+- ru
+- sn
+- rw
+- so
+- sa
+- sc
+- sd
+pipeline_tag: image-to-text
+library_name: transformers
 ---
+<img src='assets/banner.png'>
+# 🖼️ Next OCR 8B
+### *Türkiye’nin 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)
+---
+## 📖 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 Accuracy (%)         | Multilingual Accuracy (%) | Layout / Table Understanding (%) | Notes                                                                                                                 |
+| ------------------- | ------------------------ | ------------------------- | -------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
+| **Next OCR 8B**     | 94.8                     | 92.5                      | 90.7                             | Compact, Türkiye ve çokdilli odaklı, matematik & tablo destekli                                                       |
+| **DeepSeek‑OCR 3B** | 97 (yüksek sıkıştırmada) | 88–90                     | 85–87                            | Matematik ve tablo odaklı, 3B parametre, “optical context compression” ile long-doc ve tablolar için güçlü alternatif |
+> ⚡ **Note:** DeepSeek‑OCR 3B özellikle **matematiksel içerikli dokümanlar, tablolar ve formüller** üzerinde güçlü. Next OCR 8B ise Türkiye ve çokdilli OCR ile genel kullanım ve matematik odaklı dokümanlar için optimize edilmiş.
+---
+## 🚀 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, device_map="auto")
+image_path = "document.png"
+images = [image_path]
+inputs = tokenizer(images, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[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)