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", device_map="auto")
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

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

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

Hermes Agent

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

OpenClaw

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"

next-ocr / README.md

Lamapi

Update README.md

5790b6d verified 6 months ago

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	---
	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
	- el
	- eo
	- es
	- et
	- 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
	- kk
	- kn
	- km
	- kr
	- ko
	- ku
	- ks
	- kw
	- kv
	- la
	- ky
	- lg
	- lb
	- ln
	- 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](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F67d46bc5fe6ad6f6511d6f44%2FwLtEbJ9U3KCJe4OCxvAF7.png%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L199">
	---

	\| 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)